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) 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role="separator" class="divider"></li> <li><a id="rmd-download-source" href="#">Download Rmd</a></li> </ul> </div> <h1 class="title toc-ignore">IMPC Mouse data - Variance in sex differences</h1> <h3 class="subtitle">Electronic Supplementary Material</h3> <h4 class="author">Susanne Zajitschek, Felix Zajitschek, Russell Bonduriansky,Robert Brooks, Will Cornwell, Daniel Falster, Malgortaza Lagisz, Jeremy Mason, Daniel Noble, Alistair Senior & Shinichi Nakagawa</h4> <h4 class="date">August 2019</h4> </div> <div id="TOC"> <ul> <li><a href="#set-up">Set-up</a><ul> <li><a href="#loading-packages-custom-functions">Loading packages & custom functions</a></li> <li><a href="#load-clean-data">Load & clean data</a></li> </ul></li> <li><a href="#table-1-strains-and-center-sample-sizes">Table 1: “Strains and Center Sample Sizes”</a></li> <li><a href="#meta-analyses">Meta-analyses</a><ul> <li><a href="#population-as-analysis-unit">1. Population as analysis unit</a><ul> <li><a href="#loop-meta-analyses-on-all-traits">Loop: Meta-analyses on all traits</a></li> <li><a href="#merging-datasets-removal-of-non-converged-traits">Merging datasets & removal of non-converged traits</a></li> <li><a href="#removal-of-traits">Removal of traits</a></li> </ul></li> <li><a href="#meta-analysis-condensing-non-independent-traits">2. Meta-analysis: condensing non-independent traits</a><ul> <li><a href="#dealing-with-correlated-parameters-preparation">Dealing with Correlated Parameters, preparation</a></li> </ul></li> <li><a href="#table-numbers-of-correlated-and-uncorrelated-traits">Table: Numbers of correlated and uncorrelated traits</a></li> </ul></li> <li><a href="#table-for-shiny-app">Table for SHINY APP</a><ul> <li><a href="#second-order-meta-analysis-for-functional-groups">3. Second-order meta analysis for functional groups</a><ul> <li><a href="#performing-meta-analyses-3-for-each-of-the-9-grouping-terms-lncvr-lnvr-lnrr">Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</a></li> <li><a href="#re-structuring-the-data-for-each-grouping-term">Re-structuring the data for each grouping term</a></li> </ul></li> </ul></li> <li><a href="#visualisation">Visualisation</a><ul> <li><a href="#figure-4">Figure 4</a><ul> <li><a href="#overall-results-of-second-order-meta-analysis-figure-4-panel-b">Overall results of second order meta analysis (Figure 4, Panel B)</a></li> <li><a href="#fig-4">Fig 4</a></li> <li><a href="#figure-4-1">Figure 4:</a></li> </ul></li> <li><a href="#figure-5">Figure 5</a><ul> <li><a href="#preparation-for-plots-on-significant-sex-bias-second-order-meta-analysis-results">Preparation for Plots on significant sex-bias (Second-order meta analysis results</a></li> </ul></li> <li><a href="#join-code-missing">JOIN!!! CODE MISSING??</a></li> </ul></li> <li><a href="#supplemental-plots">Supplemental Plots</a><ul> <li><a href="#figure-s1">Figure S1</a><ul> <li><a href="#including-lnvr">Including lnVR</a></li> <li><a href="#count-data-including-lnvr-fig-s1-panel-a">Count data, including lnVR (Fig S1 panel A)</a></li> <li><a href="#overall-results-of-second-order-meta-analysis-including-vr">Overall results of second order meta analysis, INCLUDING VR</a></li> <li><a href="#heterogeneity">Heterogeneity</a></li> </ul></li> <li><a href="#figure-s2">Figure S2</a><ul> <li><a href="#prepare-data-for-traits-with-effect-size-ratios-10-larger-in-males-supplemental-figure-s2">Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2</a></li> <li><a href="#felix-all-missing.">FELIX: “ALL” missing.</a></li> <li><a href="#over-10-male-bias-count-data-first--order-metanalysis">Over 10% male bias, count data (first- order metanalysis)</a></li> </ul></li> <li><a href="#not-sure-what-this-below-is">NOT SURE WHAT THIS BELOW IS??</a></li> <li><a href="#figure-s2-sex-bias-including-vr">Figure S2: sex-bias, including VR</a><ul> <li><a href="#perc-sex-difference-male-bias">10 % Perc sex difference, male bias</a></li> </ul></li> <li><a href="#acknowledgements">Acknowledgements</a></li> <li><a href="#r-session-information">R Session Information</a></li> </ul></li> </ul> </div> <!-- rnb-text-begin --> <div id="set-up" class="section level1"> <h1>Set-up</h1> <div id="loading-packages-custom-functions" class="section level2"> <h2>Loading packages & custom functions</h2> <!-- rnb-text-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShyZWFkcilcbmxpYnJhcnkoZHBseXIpXG5saWJyYXJ5KG1ldGFmb3IpXG5saWJyYXJ5KGRldnRvb2xzKVxubGlicmFyeShwdXJycilcbmxpYnJhcnkodGlkeXZlcnNlKVxubGlicmFyeSh0aWR5cilcbmxpYnJhcnkodGliYmxlKVxubGlicmFyeShrYWJsZUV4dHJhKVxubGlicmFyeShyb2J1bWV0YSlcbmxpYnJhcnkoZ2dwdWJyKVxubGlicmFyeShnZ3Bsb3QyKVxubGlicmFyeShoZXJlKVxuYGBgIn0= --> <pre class="r"><code>library(readr) library(dplyr) library(metafor) library(devtools) library(purrr) library(tidyverse) library(tidyr) library(tibble) library(kableExtra) library(robumeta) library(ggpubr) library(ggplot2) library(here)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Functions for preparing the data for meta analyses</p> <ol style="list-style-type: decimal"> <li>Create function for sub-setting the data to choose only one data point per individual per trait: “data_subset_parameterid_individual_by_age”</li> </ol> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min=0, age_center=100) { tmp <- mydata %>% filter( age_in_days >= age_min, id == parameter ) %>% # take results for single individual closest to age_center mutate(age_diff = abs(age_center - age_in_days)) %>% group_by(biological_sample_id) %>% filter(age_diff == min(age_diff)) %>% select(-age_diff)# %>% # filter(!duplicated(biological_sample_id)) # still some individuals with multiple records (because same individual appear under different procedures, so filter to one record) j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id) tmp[j, ] }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <ol start="2" style="list-style-type: decimal"> <li>“Population statistics”: “calculate_population_stats” This function groups animals from the same strain and same insitiution together. This is done for each trait seoarately, and only for traits that have been measured in both sexes. Any group containing fewer than 5 individuals is excluded.</li> </ol> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY2FsY3VsYXRlX3BvcHVsYXRpb25fc3RhdHMgPC0gZnVuY3Rpb24obXlkYXRhLCBtaW5faW5kaXZpZHVhbHMgPSA1KSB7XG4gIG15ZGF0YSAlPiVcbiAgICBncm91cF9ieShwb3B1bGF0aW9uLCBzdHJhaW5fbmFtZSwgcHJvZHVjdGlvbl9jZW50ZXIsIHNleCkgJT4lXG4gICAgc3VtbWFyaXNlKFxuICAgICAgdHJhaXQgPSBwYXJhbWV0ZXJfbmFtZVsxXSxcbiAgICAgIHhfYmFyID0gbWVhbihkYXRhX3BvaW50KSxcbiAgICAgIHhfc2QgPSBzZChkYXRhX3BvaW50KSxcbiAgICAgIG5faW5kID0gbigpXG4gICAgKSAlPiVcbiAgICB1bmdyb3VwKCkgJT4lXG4gICAgZmlsdGVyKG5faW5kID4gbWluX2luZGl2aWR1YWxzKSAlPiVcbiAgICAjIENoZWNrIGJvdGggc2V4ZXMgcHJlc2VudCAmIGZpbHRlciB0aG9zZSBtaXNzaW5nXG4gICAgZ3JvdXBfYnkocG9wdWxhdGlvbikgJT4lXG4gICAgbXV0YXRlKFxuICAgICAgbl9zZXggPSBuX2Rpc3RpbmN0KHNleClcbiAgICApICU+JVxuICAgIHVuZ3JvdXAoKSAlPiVcbiAgICBmaWx0ZXIobl9zZXggPT0gMikgJT4lXG4gICAgc2VsZWN0KC1uX3NleCkgJT4lXG4gICAgYXJyYW5nZShwcm9kdWN0aW9uX2NlbnRlciwgc3RyYWluX25hbWUsIHBvcHVsYXRpb24sIHNleClcbn1cbmBgYCJ9 --> <pre class="r"><code>calculate_population_stats <- function(mydata, min_individuals = 5) { mydata %>% group_by(population, strain_name, production_center, sex) %>% summarise( trait = parameter_name[1], x_bar = mean(data_point), x_sd = sd(data_point), n_ind = n() ) %>% ungroup() %>% filter(n_ind > min_individuals) %>% # Check both sexes present & filter those missing group_by(population) %>% mutate( n_sex = n_distinct(sex) ) %>% ungroup() %>% filter(n_sex == 2) %>% select(-n_sex) %>% arrange(production_center, strain_name, population, sex) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <ol start="3" style="list-style-type: decimal"> <li>Extraction of effect sizes and sample variances: “create_meta_analysis_effect_sizes”</li> </ol> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>create_meta_analysis_effect_sizes <- function(mydata) { i <- seq(1, nrow(mydata), by = 2) input <- data.frame( n1i = mydata$n_ind[i], n2i = mydata$n_ind[i + 1], x1i = mydata$x_bar[i], x2i = mydata$x_bar[i + 1], sd1i = mydata$x_sd[i], sd2i = mydata$x_sd[i + 1] ) mydata[i, ] %>% select(strain_name, production_center, trait) %>% mutate( effect_size_CVR = calculate_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_CVR = calculate_var_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), effect_size_VR = calculate_lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i), sample_variance_VR = calculate_var_lnVR(CN = input$n1i, EN = input$n2i), effect_size_RR = calculate_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), sample_variance_RR = calculate_var_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i), err = as.factor(seq_len(n())) ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <ol start="4" style="list-style-type: decimal"> <li>Calculate meta-analysis statistics</li> </ol> <p>Based on function created by A M Senior @ the University of Otago NZ 03/01/2014:</p> <ul> <li>Calculates effect sizes for meta-analysis of variance. All functions take the mean, sd and n from the control and experimental groups.</li> <li>The first function, calculate_lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.</li> <li>The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal_E_C_Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor’s Law) is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another.</li> <li>Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR (for comparison of means)</li> </ul> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> calculate_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(ESD) - log(EMean) + 1 / (2 * (EN - 1)) - (log(CSD) - log(CMean) + 1 / (2 * (CN - 1))) } calculate_var_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal_E_C_Corr = T) { if (Equal_E_C_Corr == T) { mvcorr <- 0 # cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate old, slightly incorrect S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } else { Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate Emvcorr <- cor.test(log(EMean), (ESD))$estimate S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1)))) } S2 } calculate_lnVR <- function(CSD, CN, ESD, EN) { log(ESD) - log(CSD) + 1 / (2 * (EN - 1)) - 1 / (2 * (CN - 1)) } calculate_var_lnVR <- function(CN, EN) { 1 / (2 * (EN - 1)) + 1 / (2 * (CN - 1)) } calculate_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { log(EMean) - log(CMean) } calculate_var_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) { CSD^2 / (CN * CMean^2) + ESD^2 / (EN * EMean^2) }</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="load-clean-data" class="section level2"> <h2>Load & clean data</h2> <ol style="list-style-type: decimal"> <li>Data loading and cleaning of the csv file</li> </ol> <p>This step we have already done and provide a cleaned up file which is less computing intensive and which we have saved in a folder called <code>export</code>. However, the cvs is provided in case this is preferred to be attempted, following the steps below:</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code># loads the raw data, setting some default types for various columns load_raw <- function(filename) { read_csv(filename, col_types = cols( .default = col_character(), project_id = col_character(), id = col_character(), parameter_id = col_character(), age_in_days = col_integer(), date_of_experiment = col_datetime(format = ""), weight = col_double(), phenotyping_center_id = col_character(), production_center_id = col_character(), weight_date = col_datetime(format = ""), date_of_birth = col_datetime(format = ""), procedure_id = col_character(), pipeline_id = col_character(), biological_sample_id = col_character(), biological_model_id = col_character(), weight_days_old = col_integer(), datasource_id = col_character(), experiment_id = col_character(), data_point = col_double(), age_in_weeks = col_integer(), `_version_` = col_character() ) ) } # Apply some standard cleaning to the data clean_raw_data <- function(mydata) { group <- read_csv(here("data", "ParameterGrouping.csv")) tmp <- mydata %>% # Filter to IMPC source (recommend by Jeremey in email to Susi on 20 Aug 2018) filter(datasource_name == "IMPC") %>% # standardise trait names mutate(parameter_name = tolower(parameter_name)) %>% # remove extreme ages filter(age_in_days > 0 & age_in_days < 500) %>% # remove NAs filter(!is.na(data_point)) %>% # subset to reasonable set of variables, date_of_experiment used as an indicator of batch-level effects select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% # sort arrange(production_center, biological_sample_id, age_in_days) # filter to groups with > 1 centre merge(tmp, tmp %>% group_by(parameter_name) %>% summarise(center_per_trait = length(unique(production_center, na.rm = TRUE))) )%>% filter(center_per_trait >= 2) %>% # Define population variable mutate(population = sprintf("%s-%s", production_center, strain_name)) %>% # add grouping variable: these were decided based on functional groups and procedures mutate(parameter_group = group$parameter[match(parameter_name, group$parameter_name)] ) %>% # Assign unique IDs (per trait) # each unique parameter_name (=trait,use trait variable) gets a unique number ('id') # We add a new variable, where redundant traits are combined #[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance] mutate(id = match(parameter_name, unique(parameter_name))) %>% as_tibble() } # Load raw data - save cleaned dataset as RDS for reuse data_raw <- load_raw(here("data","dr7.0_all_control_data.csv.gz")) dir.create("export", F, F) data <- data_raw %>% clean_raw_data() saveRDS(data, "export/data_clean.rds")</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>For analysis we load the RDS created above and other datasets:</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGF0YSA8LSByZWFkUkRTKGhlcmUoXCJleHBvcnRcIiwgXCJkYXRhX2NsZWFuLnJkc1wiKSkgXG5cbnByb2NlZHVyZXMgPC0gcmVhZF9jc3YoaGVyZShcImRhdGFcIiwgXCJwcm9jZWR1cmVzLmNzdlwiKSlcbmBgYCJ9 --> <pre class="r"><code>data <- readRDS(here("export", "data_clean.rds")) procedures <- read_csv(here("data", "procedures.csv"))</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUGFyc2VkIHdpdGggY29sdW1uIHNwZWNpZmljYXRpb246XG5jb2xzKFxuICBwcm9jZWR1cmUgPSBcdTAwMWJbMzFtY29sX2NoYXJhY3RlcigpXHUwMDFiWzM5bSxcbiAgR3JvdXBpbmdUZXJtID0gXHUwMDFiWzMxbWNvbF9jaGFyYWN0ZXIoKVx1MDAxYlszOW1cbilcbiJ9 --> <pre><code>Parsed with column specification: cols( procedure = [31mcol_character()[39m, GroupingTerm = [31mcol_character()[39m )</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Checking length of different variables and sample sizes.</p> </div> </div> <div id="table-1-strains-and-center-sample-sizes" class="section level1"> <h1>Table 1: “Strains and Center Sample Sizes”</h1> <p>This table summarises the available numbers of male and female mice from each strain and originating institution.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShkYXRhJHBhcmFtZXRlcl9uYW1lKSkgIyAyMzIgdHJhaXRzXG5gYGAifQ== --> <pre class="r"><code>length(unique(data$parameter_name)) # 232 traits</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIzMlxuIn0= --> <pre><code>[1] 232</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShkYXRhJHBhcmFtZXRlcl9ncm91cCkpICMgMTYxIHBhcmFtZXRlciBncm91cHNcbmBgYCJ9 --> <pre class="r"><code>length(unique(data$parameter_group)) # 161 parameter groups</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE2MVxuIn0= --> <pre><code>[1] 161</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShkYXRhJHByb2NlZHVyZV9uYW1lKSkgIyAyNiBwcm9jZWR1cmUgZ3JvdXBzXG5gYGAifQ== --> <pre class="r"><code>length(unique(data$procedure_name)) # 26 procedure groups</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDI2XG4ifQ== --> <pre><code>[1] 26</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShkYXRhJGJpb2xvZ2ljYWxfc2FtcGxlX2lkKSkgIyAyNzE0NyBpbmRpdmlkaWFsIG1pY2UgICBcbmBgYCJ9 --> <pre class="r"><code>length(unique(data$biological_sample_id)) # 27147 individial mice </code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDI3MTQ3XG4ifQ== --> <pre><code>[1] 27147</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI251bWJlciBvZiBtYWxlcyBhbmQgZmVtYWxlcyBwZXIgc3RyYWluIHBlciBwcm9kdWN0aW9uIGNlbnRlciBcbmthYmxlKGNiaW5kKGRhdGEgJT4lIGdyb3VwX2J5KHByb2R1Y3Rpb25fY2VudGVyLCBzdHJhaW5fbmFtZSkgJT4lIGNvdW50KGJpb2xvZ2ljYWxfc2FtcGxlX2lkLCBzZXgpICU+JSBjb3VudChzZXgpICU+JSBwcmludChuID0gSW5mKSkpICU+JVxuICBrYWJsZV9zdHlsaW5nKCkgJT4lXG4gIHNjcm9sbF9ib3god2lkdGggPSBcIjYwJVwiLCBoZWlnaHQgPSBcIjIwMHB4XCIpXG5gYGAifQ== --> <pre class="r"><code>#number of males and females per strain per production center kable(cbind(data %>% group_by(production_center, strain_name) %>% count(biological_sample_id, sex) %>% count(sex) %>% print(n = Inf))) %>% kable_styling() %>% scroll_box(width = "60%", height = "200px")</code></pre> <!-- rnb-source-end --> <!-- rnb-frame-begin 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--> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:60%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> production_center </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> strain_name </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> sex </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 653 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 639 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 47 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 52 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> BCM </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 5 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 313 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 311 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1045 </td> </tr> <tr> <td style="text-align:left;"> HMGU </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1062 </td> </tr> <tr> <td style="text-align:left;"> ICS </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1025 </td> </tr> <tr> <td style="text-align:left;"> ICS </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1050 </td> </tr> <tr> <td style="text-align:left;"> JAX </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2025 </td> </tr> <tr> <td style="text-align:left;"> JAX </td> <td style="text-align:left;"> C57BL/6NJ </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2022 </td> </tr> <tr> <td style="text-align:left;"> KMPC </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 271 </td> </tr> <tr> <td style="text-align:left;"> KMPC </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 266 </td> </tr> <tr> <td style="text-align:left;"> MARC </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 936 </td> </tr> <tr> <td style="text-align:left;"> MARC </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 926 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2639 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2661 </td> </tr> <tr> <td style="text-align:left;"> MRC Harwell </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> no_data </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NJcl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 222 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NJcl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 222 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 526 </td> </tr> <tr> <td style="text-align:left;"> RBRC </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 523 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 552 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 524 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL6/NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> TCP </td> <td style="text-align:left;"> C57BL6/NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1155 </td> </tr> <tr> <td style="text-align:left;"> UC Davis </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 1158 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> B6Brd;B6Dnk;B6N-Tyr<c-Brd> </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 97 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> B6Brd;B6Dnk;B6N-Tyr<c-Brd> </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 87 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6J-Tyr<c-Brd> or C57BL/6NTac/USA </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 1951 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 2008 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 41 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6N;C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NCrl </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 13 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> female </td> <td style="text-align:right;"> 49 </td> </tr> <tr> <td style="text-align:left;"> WTSI </td> <td style="text-align:left;"> C57BL/6NTac </td> <td style="text-align:left;"> male </td> <td style="text-align:right;"> 34 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="meta-analyses" class="section level1"> <h1>Meta-analyses</h1> <div id="population-as-analysis-unit" class="section level2"> <h2>1. Population as analysis unit</h2> <p>(Step C, Figure 3 in main document)</p> <div id="loop-meta-analyses-on-all-traits" class="section level3"> <h3>Loop: Meta-analyses on all traits</h3> <ul> <li>The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis.</li> <li>Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.</li> </ul> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4obiA8LSBsZW5ndGgodW5pcXVlKGRhdGEkaWQpKSlcbmBgYCJ9 --> <pre class="r"><code> (n <- length(unique(data$id)))</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIzMlxuIn0= --> <pre><code>[1] 232</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># Create dataframe to store results results_alltraits_grouping <- tibble(id = 1:n, lnCVR=0, lnCVR_lower=0, lnCVR_upper=0, lnCVR_se=0, lnVR=0, lnVR_lower=0, lnVR_upper=0, lnVR_se=0, lnRR=0, lnRR_lower=0, lnRR_upper=0, lnRR_se=0, sampleSize=0, trait=0) for (t in 1:n) { tryCatch( { results <- data %>% data_subset_parameterid_individual_by_age(t) %>% calculate_population_stats() %>% create_meta_analysis_effect_sizes() # lnCVR, log repsonse-ratio of the coefficient of variance cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # lnVR, comparison of standard deviations cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) # for means, lnRR means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F, data = results) f <- function(x) unlist(x[c("b", "ci.lb", "ci.ub", "se")]) results_alltraits_grouping[t, 2:14] <- c(f(cvr), f(cv), f(means), means$k) results_alltraits_grouping[t, 15] <- unique(results$trait) }, error = function(e) { cat("ERROR :", t, conditionMessage(e), "\n") } ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiA4NCBPcHRpbWl6ZXIgKG9wdGltKSBkaWQgbm90IGFjaGlldmUgY29udmVyZ2VuY2UgKGNvbnZlcmdlbmNlID0gMTApLiBcbiJ9 --> <pre><code>ERROR : 84 Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin 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 --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNTggT3B0aW1pemVyIChvcHRpbSkgZGlkIG5vdCBhY2hpZXZlIGNvbnZlcmdlbmNlIChjb252ZXJnZW5jZSA9IDEwKS4gXG4ifQ== --> <pre><code>ERROR : 158 Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjAgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 160 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjEgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 161 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjIgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 162 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjMgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 163 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjUgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 165 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjYgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 166 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5Sb3dzIHdpdGggTkFzIG9taXR0ZWQgZnJvbSBtb2RlbCBmaXR0aW5nLlRoZXJlIGFyZSBvdXRjb21lcyB3aXRoIG5vbi1wb3NpdGl2ZSBzYW1wbGluZyB2YXJpYW5jZXMuJ1YnIGFwcGVhcnMgdG8gYmUgbm90IHBvc2l0aXZlIGRlZmluaXRlLlJvd3Mgd2l0aCBOQXMgb21pdHRlZCBmcm9tIG1vZGVsIGZpdHRpbmcuXG4ifQ== --> <pre><code>Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAxNjggTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 168 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin 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 --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiAyMzEgTkEvTmFOL0luZiBpbiAneScgXG4ifQ== --> <pre><code>ERROR : 231 NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>In the above function, we use ‘tryCatch’ and ‘conditionMessage’ to prevent the loop from aborting when the first error at row 84 is produced. As convergence in the two listed non-converging cases can’t be achieved by sensibly tweaking (other optim etc.), and we only learn about non-convergence in the loop, it is not possible to exclude the traits (N=2) beforehand. Similarly, there are 8 traits with very low variation, which can not be excluded prior running the loop.</p> <p>The produced “Warnings” indicate cases where variance components are set to zero during likelihood optimization.</p> </div> <div id="merging-datasets-removal-of-non-converged-traits" class="section level3"> <h3>Merging datasets & removal of non-converged traits</h3> <p>Procedure names, grouping variables and trait names (“parameter_names”) are merged back together with the results from the metafor analysis above.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>results_alltraits_grouping2 <- results_alltraits_grouping %>% left_join(by="id", data %>% select(id, parameter_group, procedure = procedure_name, procedure_name, parameter_name) %>% # We filter duplicated id's to get only one unique row per id (and there is one id per parameter_name) filter(!duplicated(id)) ) %>% # Below we add 'procedure' (from the previously loaded 'procedures.csv') as a variable left_join(by="procedure", procedures %>% distinct() ) (n <- length(unique(results_alltraits_grouping2$parameter_name))) # 232</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIzMlxuIn0= --> <pre><code>[1] 232</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="removal-of-traits" class="section level3"> <h3>Removal of traits</h3> <p>14 traits from the originally 232 that had been included are removed because they either did not achieve convergence or are nonsensical for analysis of variance (such as traits that show no variation, see list below).</p> <p>Not converged: “dp t cells”, “mzb (cd21/35 high)”</p> <p>Not enough variation: “number of caudal vertebrae”, “number of cervical vertebrae”, “number of digits”, “number of lumbar vertebrae”, “number of pelvic vertebrae”, “number of ribs left”,“number of ribs right”, “number of signals”, “number of thoracic vertebrae”, “total number of acquired events in panel a”,“total number of acquired events in panel b”, “whole arena permanence”.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code># We exclude 14 parameter names for which metafor models didn't converge ("dp t cells", "mzb (cd21/35 high)"), and of parameters that don't harbour enough variation meta_clean <- results_alltraits_grouping2 %>% filter(!parameter_name %in% c("dp t cells", "mzb (cd21/35 high)", "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left", "number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a", "total number of acquired events in panel b", "whole arena permanence")) </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p><strong>Reveiw</strong>: check against old script – identical, remove once fixed # #Felix: not sure</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjpbIm1ldGFfY2xlYW4udGVzdCA8LSByZWFkUkRTKGhlcmUoXCJleHBvcnRcIiwgXCJtZXRhX2NsZWFuLnRlc3QucmRzXCIpKSAgIiwiYWxsLmVxdWFsKG1ldGFfY2xlYW4sIG1ldGFfY2xlYW4udGVzdCAlPiUgbXV0YXRlKGlkPWFzLmludGVnZXIoaWQpLCBwYXJhbWV0ZXJfZ3JvdXAgPSBhcy5jaGFyYWN0ZXIocGFyYW1ldGVyX2dyb3VwKSwgR3JvdXBpbmdUZXJtID0gYXMuY2hhcmFjdGVyKEdyb3VwaW5nVGVybSkpKSJdfQ== --> <pre class="r"><code>meta_clean.test <- readRDS(here("export", "meta_clean.test.rds")) all.equal(meta_clean, meta_clean.test %>% mutate(id=as.integer(id), parameter_group = as.character(parameter_group), GroupingTerm = as.character(GroupingTerm)))</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>[1] “Rows in x but not y: 162, 161. Rows in y but not x: 162, 161.” Not sure??</p> </div> </div> <div id="meta-analysis-condensing-non-independent-traits" class="section level2"> <h2>2. Meta-analysis: condensing non-independent traits</h2> <p>(Step F in Figure 3 in main article)</p> <div id="dealing-with-correlated-parameters-preparation" class="section level3"> <h3>Dealing with Correlated Parameters, preparation</h3> <p>This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example hierarchical parameterization within immunological assays). As those data-points are not independent of each other, we conducted meta analyses on these correlated parameters to collapse the number of levels.</p> <div id="collapsing-and-merging-correlated-parameters" class="section level4"> <h4>Collapsing and merging correlated parameters</h4> <p>Here we double check numbers of trait parameters in the dataset</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG5tZXRhMSA8LSBtZXRhX2NsZWFuIFxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwcm9jZWR1cmUpKSAjMThcbmBgYCJ9 --> <pre class="r"><code> meta1 <- meta_clean length(unique(meta1$procedure)) #18</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE4XG4ifQ== --> <pre><code>[1] 18</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRHcm91cGluZ1Rlcm0pKSAjOVxuYGBgIn0= --> <pre class="r"><code>length(unique(meta1$GroupingTerm)) #9</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDlcbiJ9 --> <pre><code>[1] 9</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwYXJhbWV0ZXJfZ3JvdXApKSAjIDE0OCBsZXZlbHMuIFRvIGJlIHVzZWQgYXMgZ3JvdXBpbmcgZmFjdG9yIGZvciBtZXRhLW1ldGEgYW5hbHlzaXMgLyBjb2xsYXBzaW5nIGRvd24gYmFzZWQgb24gdGhpbmdzIHRoYXQgYXJlIGNsYXNzaWZpZWQgaWRlbnRpY2FsbHkgaW4gXCJwYXJhbWV0ZXJfZ3JvdXBcIiBidXQgaGF2ZSBkaWZmZXJlbnQgXCJwYXJhbWV0ZXJfbmFtZVwiXG5gYGAifQ== --> <pre class="r"><code>length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name"</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0OFxuIn0= --> <pre><code>[1] 148</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGVuZ3RoKHVuaXF1ZShtZXRhMSRwYXJhbWV0ZXJfbmFtZSkpICMyMThcbmBgYCJ9 --> <pre class="r"><code>length(unique(meta1$parameter_name)) #218</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIxOFxuIn0= --> <pre><code>[1] 218</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="count-of-number-of-parameter-names-correlated-sub-traits-in-each-parameter-group-par_group_size" class="section level4"> <h4>Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)</h4> </div> </div> </div> <div id="table-numbers-of-correlated-and-uncorrelated-traits" class="section level2"> <h2>Table: Numbers of correlated and uncorrelated traits</h2> <p>This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given “parameter group” equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same parameter group (90 cases), are selected for a “mini-meta analysis”, which removes the issue of correlation.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxua2FibGUoY2JpbmQobWV0YTEgJT4lIGNvdW50KHBhcmFtZXRlcl9ncm91cCkpKSAlPiVcbiAga2FibGVfc3R5bGluZygpICU+JVxuICBzY3JvbGxfYm94KHdpZHRoID0gXCI2MCVcIiwgaGVpZ2h0ID0gXCIyMDBweFwiKVxuYGBgIn0= --> <pre class="r"><code>kable(cbind(meta1 %>% count(parameter_group))) %>% kable_styling() %>% scroll_box(width = "60%", height = "200px")</code></pre> <!-- rnb-source-end --> <!-- rnb-htmlwidget-begin 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 --> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:60%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta1_sub <- meta1 %>% # add summary of number of parameter names in each parameter group group_by(parameter_group) %>% mutate(par_group_size = length(unique(parameter_name)), sampleSize = as.numeric(sampleSize)) %>% ungroup() %>% # Create subsets with > 1 count (par_group_size > 1) filter(par_group_size > 1) # 90 observations</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="meta-analyses-on-correlated-sub-traits-using-robumeta" class="section level4"> <h4>Meta-analyses on correlated (sub-)traits, using robumeta`</h4> <p>Here we pepare the subset of the data (using nest()), and in this first step the model of the meta analysis effect sizes are calculated</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code> meta1b <- meta1 %>% group_by(parameter_group) %>% summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE))) #this gives a summary of number of parameter names in each parameter group, now it neeeds to get merged it back together meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) meta1_sub <- subset(meta1,par_group_size >1) # 90 observations meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize) # nesting n_count <- meta1_sub %>% group_by(parameter_group) %>% mutate(raw_N = sum(sampleSize)) %>% nest() %>% ungroup() model_count <- n_count %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2)), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2)), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2)) )</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="extract-and-save-parameter-estimates" class="section level4"> <h4>Extract and save parameter estimates:</h4> <p>Function to collect the outcomes of the “mini” meta analysis</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY291bnRfZnVuIDwtIGZ1bmN0aW9uKG1vZF9zdWIpIHtcbiAgcmV0dXJuKGMobW9kX3N1YiRyZWdfdGFibGUkYi5yLCBtb2Rfc3ViJHJlZ190YWJsZSRDSS5MLCBtb2Rfc3ViJHJlZ190YWJsZSRDSS5VLCBtb2Rfc3ViJHJlZ190YWJsZSRTRSkpXG59ICMgZXN0aW1hdGUsIGxvd2VyIGNpLCB1cHBlciBjaSwgU0VcbmBgYCJ9 --> <pre class="r"><code>count_fun <- function(mod_sub) { return(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE)) } # estimate, lower ci, upper ci, SE</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Extraction of values created during Meta analysis using robu meta:</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucm9idXN1Yl9SUiA8LSBtb2RlbF9jb3VudCAlPiVcbiAgdHJhbnNtdXRlKHBhcmFtZXRlcl9ncm91cCwgZXN0aW1hdGVsblJSID0gbWFwKG1vZGVsX2xuUlIsIGNvdW50X2Z1bikpICU+JVxuICBtdXRhdGUociA9IG1hcChlc3RpbWF0ZWxuUlIsIH4gZGF0YS5mcmFtZSh0KC4pKSkpICU+JVxuICB1bm5lc3QocikgJT4lXG4gIHNlbGVjdCgtZXN0aW1hdGVsblJSKSAlPiVcbiAgcHVycnI6OnNldF9uYW1lcyhjKFwicGFyYW1ldGVyX2dyb3VwXCIsIFwibG5SUlwiLCBcImxuUlJfbG93ZXJcIiwgXCJsblJSX3VwcGVyXCIsIFwibG5SUl9zZVwiKSlcblxucm9idXN1Yl9DVlIgPC0gbW9kZWxfY291bnQgJT4lXG4gIHRyYW5zbXV0ZShwYXJhbWV0ZXJfZ3JvdXAsIGVzdGltYXRlbG5DVlIgPSBtYXAobW9kZWxfbG5DVlIsIGNvdW50X2Z1bikpICU+JVxuICBtdXRhdGUociA9IG1hcChlc3RpbWF0ZWxuQ1ZSLCB+IGRhdGEuZnJhbWUodCguKSkpKSAlPiVcbiAgdW5uZXN0KHIpICU+JVxuICBzZWxlY3QoLWVzdGltYXRlbG5DVlIpICU+JVxuICBwdXJycjo6c2V0X25hbWVzKGMoXCJwYXJhbWV0ZXJfZ3JvdXBcIiwgXCJsbkNWUlwiLCBcImxuQ1ZSX2xvd2VyXCIsIFwibG5DVlJfdXBwZXJcIiwgXCJsbkNWUl9zZVwiKSlcblxucm9idXN1Yl9WUiA8LSBtb2RlbF9jb3VudCAlPiVcbiAgdHJhbnNtdXRlKHBhcmFtZXRlcl9ncm91cCwgZXN0aW1hdGVsblZSID0gbWFwKG1vZGVsX2xuVlIsIGNvdW50X2Z1bikpICU+JVxuICBtdXRhdGUociA9IG1hcChlc3RpbWF0ZWxuVlIsIH4gZGF0YS5mcmFtZSh0KC4pKSkpICU+JVxuICB1bm5lc3QocikgJT4lXG4gIHNlbGVjdCgtZXN0aW1hdGVsblZSKSAlPiVcbiAgcHVycnI6OnNldF9uYW1lcyhjKFwicGFyYW1ldGVyX2dyb3VwXCIsIFwibG5WUlwiLCBcImxuVlJfbG93ZXJcIiwgXCJsblZSX3VwcGVyXCIsIFwibG5WUl9zZVwiKSlcblxucm9idV9hbGwgPC0gZnVsbF9qb2luKHJvYnVzdWJfQ1ZSLCByb2J1c3ViX1ZSKSAlPiUgZnVsbF9qb2luKC4sIHJvYnVzdWJfUlIpXG5gYGAifQ== --> <pre class="r"><code>robusub_RR <- model_count %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se")) robusub_CVR <- model_count %>% transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>% mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnCVR) %>% purrr::set_names(c("parameter_group", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se")) robusub_VR <- model_count %>% transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>% mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnVR) %>% purrr::set_names(c("parameter_group", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se")) robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcInBhcmFtZXRlcl9ncm91cFwiXG5Kb2luaW5nLCBieSA9IFwicGFyYW1ldGVyX2dyb3VwXCJcbiJ9 --> <pre><code>Joining, by = "parameter_group" Joining, by = "parameter_group"</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="combine-data" class="section level4"> <h4>Combine data</h4> <p>Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1])</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>meta_all <- meta1 %>% filter(par_group_size == 1) %>% as_tibble() # str(meta_all) # str(robu_all) # which(is.na(match(names(meta_all),names(robu_all)))) # check #Step1: Columns are matched by name (in our case, 'parameter_group'), and any missing columns will be filled with NA combinedmeta <- bind_rows(robu_all, meta_all) # glimpse(combinedmeta) # Steps 2&3 (add information about number of traits in a parameter group, procedure, and grouping term) metacombo <- combinedmeta metacombo$counts <- meta1$par_group_size[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$procedure2 <- meta1$procedure[match(metacombo$parameter_group, meta1$parameter_group)] metacombo$GroupingTerm2 <- meta1$GroupingTerm[match(metacombo$parameter_group, meta1$parameter_group)] </code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Clean-up, reorder, and rename</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metacombo <- metacombo[c("parameter_group", "counts","procedure2","GroupingTerm2", "lnCVR","lnCVR_lower","lnCVR_upper","lnCVR_se","lnVR","lnVR_lower","lnVR_upper","lnVR_se","lnRR","lnRR_lower","lnRR_upper","lnRR_se")] names(metacombo)[names(metacombo)=="procedure2"] <- "procedure" names(metacombo)[names(metacombo)=="GroupingTerm2"] <- "GroupingTerm" # Quick pre-check before doing plots metacombo %>% group_by(GroupingTerm) %>% dplyr::summarize(MeanCVR = mean(lnCVR), MeanVR = mean(lnVR), MeanRR = mean(lnRR))</code></pre> <!-- rnb-source-end --> <!-- rnb-frame-begin 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</script> </div> <!-- rnb-frame-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="table-for-shiny-app" class="section level1"> <h1>Table for SHINY APP</h1> <p>We use this corrected (for correlated traits) “results” table, which contains each of the meta-analytic means for all effect sizes of interest, for further analyses. We further use this table as part of the Shiny App, which is able to provide the percentage differences between males and females for mean, variance and coefficient of variance.</p> <p>This is the full result dataset</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxua2FibGUobWV0YWNvbWJvKSAlPiVcbiAga2FibGVfc3R5bGluZygpICU+JVxuICBzY3JvbGxfYm94KHdpZHRoID0gXCIxMDAlXCIsIGhlaWdodCA9IFwiMjAwcHhcIilcbmBgYCJ9 --> <pre class="r"><code>kable(metacombo) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px")</code></pre> <!-- rnb-source-end --> <p> <!-- rnb-htmlwidget-begin 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 --> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter_group </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> counts </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> procedure </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnCVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnVR_se </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_lower </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_upper </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> lnRR_se </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> pre-pulse inhibition </td> <td style="text-align:right;"> 5 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0232963 </td> <td style="text-align:right;"> -0.0802563 </td> <td style="text-align:right;"> 0.1268488 </td> <td style="text-align:right;"> 0.0370507 </td> <td style="text-align:right;"> 0.0091028 </td> <td style="text-align:right;"> -0.0364640 </td> <td style="text-align:right;"> 0.0546695 </td> <td style="text-align:right;"> 0.0143431 </td> <td style="text-align:right;"> -0.0052156 </td> <td style="text-align:right;"> -0.0427126 </td> <td style="text-align:right;"> 0.0322815 </td> <td style="text-align:right;"> 0.0128092 </td> </tr> <tr> <td style="text-align:left;"> B cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0938959 </td> <td style="text-align:right;"> -0.2500020 </td> <td style="text-align:right;"> 0.0622103 </td> <td style="text-align:right;"> 0.0426972 </td> <td style="text-align:right;"> -0.0995337 </td> <td style="text-align:right;"> -0.2068001 </td> <td style="text-align:right;"> 0.0077328 </td> <td style="text-align:right;"> 0.0250132 </td> <td style="text-align:right;"> -0.0026281 </td> <td style="text-align:right;"> -0.1298230 </td> <td style="text-align:right;"> 0.1245668 </td> <td style="text-align:right;"> 0.0393018 </td> </tr> <tr> <td style="text-align:left;"> cd4 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0287688 </td> <td style="text-align:right;"> -0.0566987 </td> <td style="text-align:right;"> -0.0008389 </td> <td style="text-align:right;"> 0.0101634 </td> <td style="text-align:right;"> -0.2018746 </td> <td style="text-align:right;"> -0.3102294 </td> <td style="text-align:right;"> -0.0935198 </td> <td style="text-align:right;"> 0.0331161 </td> <td style="text-align:right;"> -0.2344450 </td> <td style="text-align:right;"> -0.4005266 </td> <td style="text-align:right;"> -0.0683635 </td> <td style="text-align:right;"> 0.0633501 </td> </tr> <tr> <td style="text-align:left;"> cd4 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1507387 </td> <td style="text-align:right;"> -0.2427976 </td> <td style="text-align:right;"> -0.0586798 </td> <td style="text-align:right;"> 0.0360690 </td> <td style="text-align:right;"> -0.1699213 </td> <td style="text-align:right;"> -0.2629450 </td> <td style="text-align:right;"> -0.0768975 </td> <td style="text-align:right;"> 0.0348324 </td> <td style="text-align:right;"> -0.0031242 </td> <td style="text-align:right;"> -0.0411564 </td> <td style="text-align:right;"> 0.0349081 </td> <td style="text-align:right;"> 0.0148989 </td> </tr> <tr> <td style="text-align:left;"> cd8 nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0424402 </td> <td style="text-align:right;"> -0.0782046 </td> <td style="text-align:right;"> -0.0066759 </td> <td style="text-align:right;"> 0.0119223 </td> <td style="text-align:right;"> -0.0300442 </td> <td style="text-align:right;"> -0.1823594 </td> <td style="text-align:right;"> 0.1222710 </td> <td style="text-align:right;"> 0.0533765 </td> <td style="text-align:right;"> 0.0035372 </td> <td style="text-align:right;"> -0.0573749 </td> <td style="text-align:right;"> 0.0644494 </td> <td style="text-align:right;"> 0.0205272 </td> </tr> <tr> <td style="text-align:left;"> cd8 t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1223681 </td> <td style="text-align:right;"> -0.2179976 </td> <td style="text-align:right;"> -0.0267387 </td> <td style="text-align:right;"> 0.0358727 </td> <td style="text-align:right;"> -0.1581698 </td> <td style="text-align:right;"> -0.2342579 </td> <td style="text-align:right;"> -0.0820816 </td> <td style="text-align:right;"> 0.0270229 </td> <td style="text-align:right;"> -0.0415806 </td> <td style="text-align:right;"> -0.0510391 </td> <td style="text-align:right;"> -0.0321221 </td> <td style="text-align:right;"> 0.0023119 </td> </tr> <tr> <td style="text-align:left;"> cdcs </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0362947 </td> <td style="text-align:right;"> -0.3588637 </td> <td style="text-align:right;"> 0.2862742 </td> <td style="text-align:right;"> 0.0253867 </td> <td style="text-align:right;"> 0.1080248 </td> <td style="text-align:right;"> -0.0565718 </td> <td style="text-align:right;"> 0.2726213 </td> <td style="text-align:right;"> 0.0129540 </td> <td style="text-align:right;"> 0.1642541 </td> <td style="text-align:right;"> -0.1701520 </td> <td style="text-align:right;"> 0.4986601 </td> <td style="text-align:right;"> 0.0263183 </td> </tr> <tr> <td style="text-align:left;"> dn nkt </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0619371 </td> <td style="text-align:right;"> -0.1359380 </td> <td style="text-align:right;"> 0.0120637 </td> <td style="text-align:right;"> 0.0257746 </td> <td style="text-align:right;"> -0.1572129 </td> <td style="text-align:right;"> -0.2814342 </td> <td style="text-align:right;"> -0.0329915 </td> <td style="text-align:right;"> 0.0447163 </td> <td style="text-align:right;"> -0.1727105 </td> <td style="text-align:right;"> -0.2906356 </td> <td style="text-align:right;"> -0.0547854 </td> <td style="text-align:right;"> 0.0441034 </td> </tr> <tr> <td style="text-align:left;"> dn t </td> <td style="text-align:right;"> 7 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0796127 </td> <td style="text-align:right;"> -0.1844481 </td> <td style="text-align:right;"> 0.0252227 </td> <td style="text-align:right;"> 0.0420063 </td> <td style="text-align:right;"> -0.2421038 </td> <td style="text-align:right;"> -0.3431678 </td> <td style="text-align:right;"> -0.1410397 </td> <td style="text-align:right;"> 0.0406314 </td> <td style="text-align:right;"> -0.2298147 </td> <td style="text-align:right;"> -0.2519708 </td> <td style="text-align:right;"> -0.2076586 </td> <td style="text-align:right;"> 0.0072373 </td> </tr> <tr> <td style="text-align:left;"> eosinophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0662225 </td> <td style="text-align:right;"> -0.2806631 </td> <td style="text-align:right;"> 0.1482181 </td> <td style="text-align:right;"> 0.0325859 </td> <td style="text-align:right;"> -0.0154112 </td> <td style="text-align:right;"> -0.4051652 </td> <td style="text-align:right;"> 0.3743427 </td> <td style="text-align:right;"> 0.0865366 </td> <td style="text-align:right;"> -0.0042422 </td> <td style="text-align:right;"> -0.2409206 </td> <td style="text-align:right;"> 0.2324362 </td> <td style="text-align:right;"> 0.0508093 </td> </tr> <tr> <td style="text-align:left;"> follicular b cells </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1160077 </td> <td style="text-align:right;"> -0.7256692 </td> <td style="text-align:right;"> 0.4936538 </td> <td style="text-align:right;"> 0.0479814 </td> <td style="text-align:right;"> -0.1050194 </td> <td style="text-align:right;"> -0.6946364 </td> <td style="text-align:right;"> 0.4845977 </td> <td style="text-align:right;"> 0.0464039 </td> <td style="text-align:right;"> 0.0052427 </td> <td style="text-align:right;"> -0.1872381 </td> <td style="text-align:right;"> 0.1977236 </td> <td style="text-align:right;"> 0.0151486 </td> </tr> <tr> <td style="text-align:left;"> luc </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0180436 </td> <td style="text-align:right;"> -0.2038464 </td> <td style="text-align:right;"> 0.2399336 </td> <td style="text-align:right;"> 0.0174631 </td> <td style="text-align:right;"> 0.2657035 </td> <td style="text-align:right;"> -1.2251358 </td> <td style="text-align:right;"> 1.7565428 </td> <td style="text-align:right;"> 0.1173316 </td> <td style="text-align:right;"> 0.2215497 </td> <td style="text-align:right;"> -1.4136389 </td> <td style="text-align:right;"> 1.8567382 </td> <td style="text-align:right;"> 0.1286921 </td> </tr> <tr> <td style="text-align:left;"> lymphocytes </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0805230 </td> <td style="text-align:right;"> -2.2618128 </td> <td style="text-align:right;"> 2.4228588 </td> <td style="text-align:right;"> 0.1843458 </td> <td style="text-align:right;"> 0.1550159 </td> <td style="text-align:right;"> -1.0892706 </td> <td style="text-align:right;"> 1.3993024 </td> <td style="text-align:right;"> 0.0979275 </td> <td style="text-align:right;"> 0.0602144 </td> <td style="text-align:right;"> -1.0131287 </td> <td style="text-align:right;"> 1.1335576 </td> <td style="text-align:right;"> 0.0844739 </td> </tr> <tr> <td style="text-align:left;"> monocytes </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0214677 </td> <td style="text-align:right;"> -0.2033706 </td> <td style="text-align:right;"> 0.1604352 </td> <td style="text-align:right;"> 0.0420605 </td> <td style="text-align:right;"> 0.0784876 </td> <td style="text-align:right;"> -0.1811005 </td> <td style="text-align:right;"> 0.3380757 </td> <td style="text-align:right;"> 0.0585593 </td> <td style="text-align:right;"> 0.1025193 </td> <td style="text-align:right;"> -0.1483375 </td> <td style="text-align:right;"> 0.3533762 </td> <td style="text-align:right;"> 0.0571438 </td> </tr> <tr> <td style="text-align:left;"> neutrophils </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.2587446 </td> <td style="text-align:right;"> 0.0130803 </td> <td style="text-align:right;"> 0.5044089 </td> <td style="text-align:right;"> 0.0557516 </td> <td style="text-align:right;"> 0.3799805 </td> <td style="text-align:right;"> -0.2060446 </td> <td style="text-align:right;"> 0.9660057 </td> <td style="text-align:right;"> 0.1317980 </td> <td style="text-align:right;"> 0.1319372 </td> <td style="text-align:right;"> -0.2669324 </td> <td style="text-align:right;"> 0.5308068 </td> <td style="text-align:right;"> 0.0924336 </td> </tr> <tr> <td style="text-align:left;"> nk cells </td> <td style="text-align:right;"> 6 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0414772 </td> <td style="text-align:right;"> -0.0960406 </td> <td style="text-align:right;"> 0.0130862 </td> <td style="text-align:right;"> 0.0200411 </td> <td style="text-align:right;"> 0.0156533 </td> <td style="text-align:right;"> -0.0703789 </td> <td style="text-align:right;"> 0.1016856 </td> <td style="text-align:right;"> 0.0315487 </td> <td style="text-align:right;"> 0.0471757 </td> <td style="text-align:right;"> -0.0162213 </td> <td style="text-align:right;"> 0.1105728 </td> <td style="text-align:right;"> 0.0231831 </td> </tr> <tr> <td style="text-align:left;"> nkt cells </td> <td style="text-align:right;"> 4 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.0033757 </td> <td style="text-align:right;"> -0.1069890 </td> <td style="text-align:right;"> 0.1137404 </td> <td style="text-align:right;"> 0.0294661 </td> <td style="text-align:right;"> -0.2458705 </td> <td style="text-align:right;"> -0.4452333 </td> <td style="text-align:right;"> -0.0465077 </td> <td style="text-align:right;"> 0.0426738 </td> <td style="text-align:right;"> -0.1823355 </td> <td style="text-align:right;"> -0.3233946 </td> <td style="text-align:right;"> -0.0412763 </td> <td style="text-align:right;"> 0.0314580 </td> </tr> <tr> <td style="text-align:left;"> percentage of live gated events </td> <td style="text-align:right;"> 2 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0934933 </td> <td style="text-align:right;"> -0.3037340 </td> <td style="text-align:right;"> 0.1167473 </td> <td style="text-align:right;"> 0.0165463 </td> <td style="text-align:right;"> -0.0412606 </td> <td style="text-align:right;"> -0.1414443 </td> <td style="text-align:right;"> 0.0589231 </td> <td style="text-align:right;"> 0.0078846 </td> <td style="text-align:right;"> 0.0500941 </td> <td style="text-align:right;"> 0.0081191 </td> <td style="text-align:right;"> 0.0920690 </td> <td style="text-align:right;"> 0.0033035 </td> </tr> <tr> <td style="text-align:left;"> response amplitude </td> <td style="text-align:right;"> 10 </td> <td style="text-align:left;"> Acoustic Startle and Pre-pulse Inhibition (PPI) </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0333147 </td> <td style="text-align:right;"> -0.0127585 </td> <td style="text-align:right;"> 0.0793879 </td> <td style="text-align:right;"> 0.0202947 </td> <td style="text-align:right;"> 0.2549274 </td> <td style="text-align:right;"> 0.1969787 </td> <td style="text-align:right;"> 0.3128761 </td> <td style="text-align:right;"> 0.0255003 </td> <td style="text-align:right;"> 0.2016062 </td> <td style="text-align:right;"> 0.1108136 </td> <td style="text-align:right;"> 0.2923987 </td> <td style="text-align:right;"> 0.0401164 </td> </tr> <tr> <td style="text-align:left;"> t cells </td> <td style="text-align:right;"> 3 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1338701 </td> <td style="text-align:right;"> -0.2750284 </td> <td style="text-align:right;"> 0.0072883 </td> <td style="text-align:right;"> 0.0326594 </td> <td style="text-align:right;"> -0.1240786 </td> <td style="text-align:right;"> -0.4120104 </td> <td style="text-align:right;"> 0.1638531 </td> <td style="text-align:right;"> 0.0668611 </td> <td style="text-align:right;"> -0.0005749 </td> <td style="text-align:right;"> -0.1663201 </td> <td style="text-align:right;"> 0.1651702 </td> <td style="text-align:right;"> 0.0374233 </td> </tr> <tr> <td style="text-align:left;"> 12khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0538655 </td> <td style="text-align:right;"> -0.0056830 </td> <td style="text-align:right;"> 0.1134139 </td> <td style="text-align:right;"> 0.0303824 </td> <td style="text-align:right;"> 0.0869649 </td> <td style="text-align:right;"> 0.0065802 </td> <td style="text-align:right;"> 0.1673497 </td> <td style="text-align:right;"> 0.0410134 </td> <td style="text-align:right;"> 0.0024851 </td> <td style="text-align:right;"> -0.0214504 </td> <td style="text-align:right;"> 0.0264205 </td> <td style="text-align:right;"> 0.0122122 </td> </tr> <tr> <td style="text-align:left;"> 18khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0238241 </td> <td style="text-align:right;"> -0.0331809 </td> <td style="text-align:right;"> 0.0808292 </td> <td style="text-align:right;"> 0.0290848 </td> <td style="text-align:right;"> 0.0250266 </td> <td style="text-align:right;"> -0.0488450 </td> <td style="text-align:right;"> 0.0988982 </td> <td style="text-align:right;"> 0.0376903 </td> <td style="text-align:right;"> -0.0200763 </td> <td style="text-align:right;"> -0.0431508 </td> <td style="text-align:right;"> 0.0029982 </td> <td style="text-align:right;"> 0.0117729 </td> </tr> <tr> <td style="text-align:left;"> 24khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0518127 </td> <td style="text-align:right;"> -0.0148242 </td> <td style="text-align:right;"> 0.1184497 </td> <td style="text-align:right;"> 0.0339991 </td> <td style="text-align:right;"> -0.0891510 </td> <td style="text-align:right;"> -0.3321998 </td> <td style="text-align:right;"> 0.1538977 </td> <td style="text-align:right;"> 0.1240067 </td> <td style="text-align:right;"> -0.0224536 </td> <td style="text-align:right;"> -0.0444163 </td> <td style="text-align:right;"> -0.0004910 </td> <td style="text-align:right;"> 0.0112057 </td> </tr> <tr> <td style="text-align:left;"> 30khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> 0.0170933 </td> <td style="text-align:right;"> -0.0533187 </td> <td style="text-align:right;"> 0.0875053 </td> <td style="text-align:right;"> 0.0359252 </td> <td style="text-align:right;"> -0.0344797 </td> <td style="text-align:right;"> -0.1017901 </td> <td style="text-align:right;"> 0.0328306 </td> <td style="text-align:right;"> 0.0343426 </td> <td style="text-align:right;"> -0.0497874 </td> <td style="text-align:right;"> -0.0748197 </td> <td style="text-align:right;"> -0.0247550 </td> <td style="text-align:right;"> 0.0127718 </td> </tr> <tr> <td style="text-align:left;"> 6khz-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0077678 </td> <td style="text-align:right;"> -0.0418582 </td> <td style="text-align:right;"> 0.0263226 </td> <td style="text-align:right;"> 0.0173934 </td> <td style="text-align:right;"> 0.0141682 </td> <td style="text-align:right;"> -0.0189973 </td> <td style="text-align:right;"> 0.0473337 </td> <td style="text-align:right;"> 0.0169215 </td> <td style="text-align:right;"> 0.0184043 </td> <td style="text-align:right;"> 0.0056897 </td> <td style="text-align:right;"> 0.0311189 </td> <td style="text-align:right;"> 0.0064872 </td> </tr> <tr> <td style="text-align:left;"> alanine aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0684217 </td> <td style="text-align:right;"> -0.1895020 </td> <td style="text-align:right;"> 0.0526586 </td> <td style="text-align:right;"> 0.0617768 </td> <td style="text-align:right;"> 0.0585179 </td> <td style="text-align:right;"> -0.1322507 </td> <td style="text-align:right;"> 0.2492866 </td> <td style="text-align:right;"> 0.0973327 </td> <td style="text-align:right;"> 0.1069442 </td> <td style="text-align:right;"> 0.0319934 </td> <td style="text-align:right;"> 0.1818950 </td> <td style="text-align:right;"> 0.0382409 </td> </tr> <tr> <td style="text-align:left;"> albumin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1133080 </td> <td style="text-align:right;"> 0.0451475 </td> <td style="text-align:right;"> 0.1814685 </td> <td style="text-align:right;"> 0.0347764 </td> <td style="text-align:right;"> 0.0559995 </td> <td style="text-align:right;"> -0.0080678 </td> <td style="text-align:right;"> 0.1200668 </td> <td style="text-align:right;"> 0.0326880 </td> <td style="text-align:right;"> -0.0567840 </td> <td style="text-align:right;"> -0.0732083 </td> <td style="text-align:right;"> -0.0403597 </td> <td style="text-align:right;"> 0.0083799 </td> </tr> <tr> <td style="text-align:left;"> alkaline phosphatase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.1043649 </td> <td style="text-align:right;"> 0.0451585 </td> <td style="text-align:right;"> 0.1635713 </td> <td style="text-align:right;"> 0.0302079 </td> <td style="text-align:right;"> -0.3112471 </td> <td style="text-align:right;"> -0.3980164 </td> <td style="text-align:right;"> -0.2244778 </td> <td style="text-align:right;"> 0.0442709 </td> <td style="text-align:right;"> -0.4216032 </td> <td style="text-align:right;"> -0.4694832 </td> <td style="text-align:right;"> -0.3737231 </td> <td style="text-align:right;"> 0.0244290 </td> </tr> <tr> <td style="text-align:left;"> alpha-amylase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0383407 </td> <td style="text-align:right;"> -0.0423419 </td> <td style="text-align:right;"> 0.1190232 </td> <td style="text-align:right;"> 0.0411653 </td> <td style="text-align:right;"> 0.2795566 </td> <td style="text-align:right;"> 0.1615777 </td> <td style="text-align:right;"> 0.3975355 </td> <td style="text-align:right;"> 0.0601944 </td> <td style="text-align:right;"> 0.2246987 </td> <td style="text-align:right;"> 0.1793151 </td> <td style="text-align:right;"> 0.2700822 </td> <td style="text-align:right;"> 0.0231553 </td> </tr> <tr> <td style="text-align:left;"> area under glucose response curve </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1531723 </td> <td style="text-align:right;"> -0.2210551 </td> <td style="text-align:right;"> -0.0852895 </td> <td style="text-align:right;"> 0.0346347 </td> <td style="text-align:right;"> 0.2748396 </td> <td style="text-align:right;"> 0.1950895 </td> <td style="text-align:right;"> 0.3545898 </td> <td style="text-align:right;"> 0.0406896 </td> <td style="text-align:right;"> 0.4357738 </td> <td style="text-align:right;"> 0.3655882 </td> <td style="text-align:right;"> 0.5059595 </td> <td style="text-align:right;"> 0.0358097 </td> </tr> <tr> <td style="text-align:left;"> aspartate aminotransferase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0119165 </td> <td style="text-align:right;"> -0.1228287 </td> <td style="text-align:right;"> 0.1466617 </td> <td style="text-align:right;"> 0.0687488 </td> <td style="text-align:right;"> -0.0566968 </td> <td style="text-align:right;"> -0.2457779 </td> <td style="text-align:right;"> 0.1323843 </td> <td style="text-align:right;"> 0.0964717 </td> <td style="text-align:right;"> -0.0585577 </td> <td style="text-align:right;"> -0.1331777 </td> <td style="text-align:right;"> 0.0160624 </td> <td style="text-align:right;"> 0.0380722 </td> </tr> <tr> <td style="text-align:left;"> basophil cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0917931 </td> <td style="text-align:right;"> -0.2022487 </td> <td style="text-align:right;"> 0.0186624 </td> <td style="text-align:right;"> 0.0563559 </td> <td style="text-align:right;"> 0.2031265 </td> <td style="text-align:right;"> -0.0131549 </td> <td style="text-align:right;"> 0.4194079 </td> <td style="text-align:right;"> 0.1103497 </td> <td style="text-align:right;"> 0.2675772 </td> <td style="text-align:right;"> 0.0643028 </td> <td style="text-align:right;"> 0.4708516 </td> <td style="text-align:right;"> 0.1037133 </td> </tr> <tr> <td style="text-align:left;"> basophil differential count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0934739 </td> <td style="text-align:right;"> -0.1787512 </td> <td style="text-align:right;"> -0.0081966 </td> <td style="text-align:right;"> 0.0435096 </td> <td style="text-align:right;"> -0.0639511 </td> <td style="text-align:right;"> -0.2828066 </td> <td style="text-align:right;"> 0.1549044 </td> <td style="text-align:right;"> 0.1116630 </td> <td style="text-align:right;"> -0.0156339 </td> <td style="text-align:right;"> -0.1102310 </td> <td style="text-align:right;"> 0.0789633 </td> <td style="text-align:right;"> 0.0482647 </td> </tr> <tr> <td style="text-align:left;"> bmc/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1314998 </td> <td style="text-align:right;"> 0.0329846 </td> <td style="text-align:right;"> 0.2300151 </td> <td style="text-align:right;"> 0.0502638 </td> <td style="text-align:right;"> -0.0448684 </td> <td style="text-align:right;"> -0.1340146 </td> <td style="text-align:right;"> 0.0442777 </td> <td style="text-align:right;"> 0.0454836 </td> <td style="text-align:right;"> -0.1722378 </td> <td style="text-align:right;"> -0.2207030 </td> <td style="text-align:right;"> -0.1237726 </td> <td style="text-align:right;"> 0.0247276 </td> </tr> <tr> <td style="text-align:left;"> body length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.0347988 </td> <td style="text-align:right;"> -0.0824528 </td> <td style="text-align:right;"> 0.0128552 </td> <td style="text-align:right;"> 0.0243137 </td> <td style="text-align:right;"> -0.0059677 </td> <td style="text-align:right;"> -0.0526221 </td> <td style="text-align:right;"> 0.0406866 </td> <td style="text-align:right;"> 0.0238037 </td> <td style="text-align:right;"> 0.0282722 </td> <td style="text-align:right;"> 0.0233254 </td> <td style="text-align:right;"> 0.0332189 </td> <td style="text-align:right;"> 0.0025239 </td> </tr> <tr> <td style="text-align:left;"> body temp </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0325368 </td> <td style="text-align:right;"> -0.1066429 </td> <td style="text-align:right;"> 0.0415693 </td> <td style="text-align:right;"> 0.0378099 </td> <td style="text-align:right;"> -0.0303742 </td> <td style="text-align:right;"> -0.1044537 </td> <td style="text-align:right;"> 0.0437054 </td> <td style="text-align:right;"> 0.0377964 </td> <td style="text-align:right;"> 0.0018532 </td> <td style="text-align:right;"> -0.0005002 </td> <td style="text-align:right;"> 0.0042066 </td> <td style="text-align:right;"> 0.0012008 </td> </tr> <tr> <td style="text-align:left;"> body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0245675 </td> <td style="text-align:right;"> -0.0420402 </td> <td style="text-align:right;"> 0.0911752 </td> <td style="text-align:right;"> 0.0339841 </td> <td style="text-align:right;"> 0.2335793 </td> <td style="text-align:right;"> 0.1694979 </td> <td style="text-align:right;"> 0.2976607 </td> <td style="text-align:right;"> 0.0326952 </td> <td style="text-align:right;"> 0.2096770 </td> <td style="text-align:right;"> 0.1938727 </td> <td style="text-align:right;"> 0.2254813 </td> <td style="text-align:right;"> 0.0080636 </td> </tr> <tr> <td style="text-align:left;"> body weight after experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.0853708 </td> <td style="text-align:right;"> 0.0299665 </td> <td style="text-align:right;"> 0.1407751 </td> <td style="text-align:right;"> 0.0282680 </td> <td style="text-align:right;"> 0.2849370 </td> <td style="text-align:right;"> 0.2328875 </td> <td style="text-align:right;"> 0.3369866 </td> <td style="text-align:right;"> 0.0265564 </td> <td style="text-align:right;"> 0.2030973 </td> <td style="text-align:right;"> 0.1864076 </td> <td style="text-align:right;"> 0.2197871 </td> <td style="text-align:right;"> 0.0085153 </td> </tr> <tr> <td style="text-align:left;"> body weight before experiment </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> 0.1053511 </td> <td style="text-align:right;"> 0.0412461 </td> <td style="text-align:right;"> 0.1694562 </td> <td style="text-align:right;"> 0.0327073 </td> <td style="text-align:right;"> 0.3038998 </td> <td style="text-align:right;"> 0.2435428 </td> <td style="text-align:right;"> 0.3642568 </td> <td style="text-align:right;"> 0.0307949 </td> <td style="text-align:right;"> 0.2008638 </td> <td style="text-align:right;"> 0.1816362 </td> <td style="text-align:right;"> 0.2200914 </td> <td style="text-align:right;"> 0.0098102 </td> </tr> <tr> <td style="text-align:left;"> bone area </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0981587 </td> <td style="text-align:right;"> 0.0272824 </td> <td style="text-align:right;"> 0.1690349 </td> <td style="text-align:right;"> 0.0361620 </td> <td style="text-align:right;"> 0.1286546 </td> <td style="text-align:right;"> 0.0533659 </td> <td style="text-align:right;"> 0.2039432 </td> <td style="text-align:right;"> 0.0384133 </td> <td style="text-align:right;"> 0.0315241 </td> <td style="text-align:right;"> 0.0003806 </td> <td style="text-align:right;"> 0.0626676 </td> <td style="text-align:right;"> 0.0158898 </td> </tr> <tr> <td style="text-align:left;"> bone mineral content (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1709230 </td> <td style="text-align:right;"> 0.0625642 </td> <td style="text-align:right;"> 0.2792818 </td> <td style="text-align:right;"> 0.0552861 </td> <td style="text-align:right;"> 0.2091372 </td> <td style="text-align:right;"> 0.1015600 </td> <td style="text-align:right;"> 0.3167143 </td> <td style="text-align:right;"> 0.0548873 </td> <td style="text-align:right;"> 0.0372537 </td> <td style="text-align:right;"> -0.0130828 </td> <td style="text-align:right;"> 0.0875902 </td> <td style="text-align:right;"> 0.0256824 </td> </tr> <tr> <td style="text-align:left;"> bone mineral density (excluding skull) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0542638 </td> <td style="text-align:right;"> -0.0881612 </td> <td style="text-align:right;"> 0.1966887 </td> <td style="text-align:right;"> 0.0726671 </td> <td style="text-align:right;"> 0.0492830 </td> <td style="text-align:right;"> -0.1087868 </td> <td style="text-align:right;"> 0.2073528 </td> <td style="text-align:right;"> 0.0806494 </td> <td style="text-align:right;"> 0.0012286 </td> <td style="text-align:right;"> -0.0187942 </td> <td style="text-align:right;"> 0.0212514 </td> <td style="text-align:right;"> 0.0102159 </td> </tr> <tr> <td style="text-align:left;"> calcium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0097946 </td> <td style="text-align:right;"> -0.0464600 </td> <td style="text-align:right;"> 0.0660492 </td> <td style="text-align:right;"> 0.0287018 </td> <td style="text-align:right;"> 0.0135683 </td> <td style="text-align:right;"> -0.0424600 </td> <td style="text-align:right;"> 0.0695966 </td> <td style="text-align:right;"> 0.0285864 </td> <td style="text-align:right;"> 0.0036564 </td> <td style="text-align:right;"> -0.0000609 </td> <td style="text-align:right;"> 0.0073737 </td> <td style="text-align:right;"> 0.0018966 </td> </tr> <tr> <td style="text-align:left;"> cardiac output </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0133816 </td> <td style="text-align:right;"> -0.0797535 </td> <td style="text-align:right;"> 0.1065166 </td> <td style="text-align:right;"> 0.0475188 </td> <td style="text-align:right;"> 0.1017991 </td> <td style="text-align:right;"> 0.0206287 </td> <td style="text-align:right;"> 0.1829694 </td> <td style="text-align:right;"> 0.0414142 </td> <td style="text-align:right;"> 0.0934439 </td> <td style="text-align:right;"> 0.0580233 </td> <td style="text-align:right;"> 0.1288645 </td> <td style="text-align:right;"> 0.0180721 </td> </tr> <tr> <td style="text-align:left;"> center average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0167300 </td> <td style="text-align:right;"> -0.0404735 </td> <td style="text-align:right;"> 0.0739335 </td> <td style="text-align:right;"> 0.0291860 </td> <td style="text-align:right;"> -0.0588515 </td> <td style="text-align:right;"> -0.1004209 </td> <td style="text-align:right;"> -0.0172820 </td> <td style="text-align:right;"> 0.0212093 </td> <td style="text-align:right;"> -0.0724619 </td> <td style="text-align:right;"> -0.1149622 </td> <td style="text-align:right;"> -0.0299616 </td> <td style="text-align:right;"> 0.0216842 </td> </tr> <tr> <td style="text-align:left;"> center distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0162603 </td> <td style="text-align:right;"> -0.0733243 </td> <td style="text-align:right;"> 0.0408038 </td> <td style="text-align:right;"> 0.0291149 </td> <td style="text-align:right;"> -0.1060637 </td> <td style="text-align:right;"> -0.2023343 </td> <td style="text-align:right;"> -0.0097930 </td> <td style="text-align:right;"> 0.0491186 </td> <td style="text-align:right;"> -0.0940204 </td> <td style="text-align:right;"> -0.1945774 </td> <td style="text-align:right;"> 0.0065366 </td> <td style="text-align:right;"> 0.0513055 </td> </tr> <tr> <td style="text-align:left;"> center permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0253715 </td> <td style="text-align:right;"> -0.0826435 </td> <td style="text-align:right;"> 0.0319004 </td> <td style="text-align:right;"> 0.0292209 </td> <td style="text-align:right;"> -0.0255734 </td> <td style="text-align:right;"> -0.1014389 </td> <td style="text-align:right;"> 0.0502922 </td> <td style="text-align:right;"> 0.0387076 </td> <td style="text-align:right;"> -0.0035151 </td> <td style="text-align:right;"> -0.0902886 </td> <td style="text-align:right;"> 0.0832585 </td> <td style="text-align:right;"> 0.0442730 </td> </tr> <tr> <td style="text-align:left;"> center resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0244492 </td> <td style="text-align:right;"> -0.0737922 </td> <td style="text-align:right;"> 0.1226906 </td> <td style="text-align:right;"> 0.0501241 </td> <td style="text-align:right;"> -0.0228690 </td> <td style="text-align:right;"> -0.1548339 </td> <td style="text-align:right;"> 0.1090960 </td> <td style="text-align:right;"> 0.0673303 </td> <td style="text-align:right;"> -0.0630751 </td> <td style="text-align:right;"> -0.2215457 </td> <td style="text-align:right;"> 0.0953955 </td> <td style="text-align:right;"> 0.0808538 </td> </tr> <tr> <td style="text-align:left;"> chloride </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0321555 </td> <td style="text-align:right;"> -0.1270972 </td> <td style="text-align:right;"> 0.1914083 </td> <td style="text-align:right;"> 0.0812529 </td> <td style="text-align:right;"> 0.0241491 </td> <td style="text-align:right;"> -0.1438502 </td> <td style="text-align:right;"> 0.1921485 </td> <td style="text-align:right;"> 0.0857155 </td> <td style="text-align:right;"> -0.0127047 </td> <td style="text-align:right;"> -0.0177349 </td> <td style="text-align:right;"> -0.0076745 </td> <td style="text-align:right;"> 0.0025665 </td> </tr> <tr> <td style="text-align:left;"> click-evoked abr threshold </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Auditory Brain Stem Response </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:right;"> -0.0529450 </td> <td style="text-align:right;"> -0.1534816 </td> <td style="text-align:right;"> 0.0475915 </td> <td style="text-align:right;"> 0.0512951 </td> <td style="text-align:right;"> -0.0561198 </td> <td style="text-align:right;"> -0.1827679 </td> <td style="text-align:right;"> 0.0705282 </td> <td style="text-align:right;"> 0.0646176 </td> <td style="text-align:right;"> -0.0154221 </td> <td style="text-align:right;"> -0.0577200 </td> <td style="text-align:right;"> 0.0268757 </td> <td style="text-align:right;"> 0.0215809 </td> </tr> <tr> <td style="text-align:left;"> creatine kinase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0241232 </td> <td style="text-align:right;"> -0.1071457 </td> <td style="text-align:right;"> 0.1553920 </td> <td style="text-align:right;"> 0.0669751 </td> <td style="text-align:right;"> -0.1318792 </td> <td style="text-align:right;"> -0.3968974 </td> <td style="text-align:right;"> 0.1331390 </td> <td style="text-align:right;"> 0.1352159 </td> <td style="text-align:right;"> -0.1344413 </td> <td style="text-align:right;"> -0.3838303 </td> <td style="text-align:right;"> 0.1149476 </td> <td style="text-align:right;"> 0.1272416 </td> </tr> <tr> <td style="text-align:left;"> creatinine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0352315 </td> <td style="text-align:right;"> -0.0229205 </td> <td style="text-align:right;"> 0.0933835 </td> <td style="text-align:right;"> 0.0296699 </td> <td style="text-align:right;"> 0.1066373 </td> <td style="text-align:right;"> -0.2200831 </td> <td style="text-align:right;"> 0.4333578 </td> <td style="text-align:right;"> 0.1666972 </td> <td style="text-align:right;"> -0.0844078 </td> <td style="text-align:right;"> -0.1320251 </td> <td style="text-align:right;"> -0.0367905 </td> <td style="text-align:right;"> 0.0242950 </td> </tr> <tr> <td style="text-align:left;"> cv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1874544 </td> <td style="text-align:right;"> 0.0716631 </td> <td style="text-align:right;"> 0.3032457 </td> <td style="text-align:right;"> 0.0590783 </td> <td style="text-align:right;"> -0.0895722 </td> <td style="text-align:right;"> -0.2484833 </td> <td style="text-align:right;"> 0.0693388 </td> <td style="text-align:right;"> 0.0810786 </td> <td style="text-align:right;"> -0.2401301 </td> <td style="text-align:right;"> -0.3410322 </td> <td style="text-align:right;"> -0.1392280 </td> <td style="text-align:right;"> 0.0514816 </td> </tr> <tr> <td style="text-align:left;"> distance travelled - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0187819 </td> <td style="text-align:right;"> -0.0858957 </td> <td style="text-align:right;"> 0.0483318 </td> <td style="text-align:right;"> 0.0342423 </td> <td style="text-align:right;"> -0.1272582 </td> <td style="text-align:right;"> -0.1997426 </td> <td style="text-align:right;"> -0.0547738 </td> <td style="text-align:right;"> 0.0369825 </td> <td style="text-align:right;"> -0.1121373 </td> <td style="text-align:right;"> -0.1816322 </td> <td style="text-align:right;"> -0.0426424 </td> <td style="text-align:right;"> 0.0354572 </td> </tr> <tr> <td style="text-align:left;"> ejection fraction </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0300111 </td> <td style="text-align:right;"> -0.1345066 </td> <td style="text-align:right;"> 0.0744844 </td> <td style="text-align:right;"> 0.0533150 </td> <td style="text-align:right;"> -0.0525735 </td> <td style="text-align:right;"> -0.1483174 </td> <td style="text-align:right;"> 0.0431705 </td> <td style="text-align:right;"> 0.0488499 </td> <td style="text-align:right;"> -0.0284086 </td> <td style="text-align:right;"> -0.0492579 </td> <td style="text-align:right;"> -0.0075592 </td> <td style="text-align:right;"> 0.0106376 </td> </tr> <tr> <td style="text-align:left;"> end-diastolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1120972 </td> <td style="text-align:right;"> 0.0431489 </td> <td style="text-align:right;"> 0.1810454 </td> <td style="text-align:right;"> 0.0351783 </td> <td style="text-align:right;"> 0.1743929 </td> <td style="text-align:right;"> 0.0875252 </td> <td style="text-align:right;"> 0.2612607 </td> <td style="text-align:right;"> 0.0443211 </td> <td style="text-align:right;"> 0.0600907 </td> <td style="text-align:right;"> 0.0354923 </td> <td style="text-align:right;"> 0.0846891 </td> <td style="text-align:right;"> 0.0125504 </td> </tr> <tr> <td style="text-align:left;"> end-systolic diameter </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0084176 </td> <td style="text-align:right;"> -0.0780811 </td> <td style="text-align:right;"> 0.0612459 </td> <td style="text-align:right;"> 0.0355433 </td> <td style="text-align:right;"> 0.0668966 </td> <td style="text-align:right;"> -0.0016692 </td> <td style="text-align:right;"> 0.1354624 </td> <td style="text-align:right;"> 0.0349832 </td> <td style="text-align:right;"> 0.0763195 </td> <td style="text-align:right;"> 0.0451136 </td> <td style="text-align:right;"> 0.1075254 </td> <td style="text-align:right;"> 0.0159217 </td> </tr> <tr> <td style="text-align:left;"> fasted blood glucose concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0177245 </td> <td style="text-align:right;"> -0.1256855 </td> <td style="text-align:right;"> 0.0902366 </td> <td style="text-align:right;"> 0.0550832 </td> <td style="text-align:right;"> 0.0702824 </td> <td style="text-align:right;"> -0.0302439 </td> <td style="text-align:right;"> 0.1708087 </td> <td style="text-align:right;"> 0.0512899 </td> <td style="text-align:right;"> 0.0868420 </td> <td style="text-align:right;"> 0.0493007 </td> <td style="text-align:right;"> 0.1243832 </td> <td style="text-align:right;"> 0.0191541 </td> </tr> <tr> <td style="text-align:left;"> fat mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0408799 </td> <td style="text-align:right;"> -0.0430149 </td> <td style="text-align:right;"> 0.1247746 </td> <td style="text-align:right;"> 0.0428042 </td> <td style="text-align:right;"> 0.3714313 </td> <td style="text-align:right;"> 0.2698790 </td> <td style="text-align:right;"> 0.4729837 </td> <td style="text-align:right;"> 0.0518134 </td> <td style="text-align:right;"> 0.3282080 </td> <td style="text-align:right;"> 0.2669032 </td> <td style="text-align:right;"> 0.3895129 </td> <td style="text-align:right;"> 0.0312786 </td> </tr> <tr> <td style="text-align:left;"> fat/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0777327 </td> <td style="text-align:right;"> -0.0119735 </td> <td style="text-align:right;"> 0.1674390 </td> <td style="text-align:right;"> 0.0457693 </td> <td style="text-align:right;"> 0.2020776 </td> <td style="text-align:right;"> 0.1083557 </td> <td style="text-align:right;"> 0.2957996 </td> <td style="text-align:right;"> 0.0478182 </td> <td style="text-align:right;"> 0.1235292 </td> <td style="text-align:right;"> 0.0638629 </td> <td style="text-align:right;"> 0.1831955 </td> <td style="text-align:right;"> 0.0304425 </td> </tr> <tr> <td style="text-align:left;"> forelimb and hindlimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0578158 </td> <td style="text-align:right;"> 0.0039998 </td> <td style="text-align:right;"> 0.1116318 </td> <td style="text-align:right;"> 0.0274577 </td> <td style="text-align:right;"> 0.1145986 </td> <td style="text-align:right;"> 0.0530521 </td> <td style="text-align:right;"> 0.1761451 </td> <td style="text-align:right;"> 0.0314018 </td> <td style="text-align:right;"> 0.0541888 </td> <td style="text-align:right;"> 0.0294838 </td> <td style="text-align:right;"> 0.0788938 </td> <td style="text-align:right;"> 0.0126048 </td> </tr> <tr> <td style="text-align:left;"> forelimb grip strength measurement mean </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Grip Strength </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0265051 </td> <td style="text-align:right;"> -0.0187240 </td> <td style="text-align:right;"> 0.0717341 </td> <td style="text-align:right;"> 0.0230765 </td> <td style="text-align:right;"> 0.0995076 </td> <td style="text-align:right;"> 0.0539740 </td> <td style="text-align:right;"> 0.1450413 </td> <td style="text-align:right;"> 0.0232319 </td> <td style="text-align:right;"> 0.0697061 </td> <td style="text-align:right;"> 0.0438625 </td> <td style="text-align:right;"> 0.0955496 </td> <td style="text-align:right;"> 0.0131857 </td> </tr> <tr> <td style="text-align:left;"> fractional shortening </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0148852 </td> <td style="text-align:right;"> -0.1161666 </td> <td style="text-align:right;"> 0.0863961 </td> <td style="text-align:right;"> 0.0516751 </td> <td style="text-align:right;"> -0.0575326 </td> <td style="text-align:right;"> -0.1558559 </td> <td style="text-align:right;"> 0.0407907 </td> <td style="text-align:right;"> 0.0501659 </td> <td style="text-align:right;"> -0.0413498 </td> <td style="text-align:right;"> -0.0567105 </td> <td style="text-align:right;"> -0.0259891 </td> <td style="text-align:right;"> 0.0078372 </td> </tr> <tr> <td style="text-align:left;"> free fatty acids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0281576 </td> <td style="text-align:right;"> -0.1002531 </td> <td style="text-align:right;"> 0.1565683 </td> <td style="text-align:right;"> 0.0655169 </td> <td style="text-align:right;"> 0.0554109 </td> <td style="text-align:right;"> -0.0736861 </td> <td style="text-align:right;"> 0.1845079 </td> <td style="text-align:right;"> 0.0658670 </td> <td style="text-align:right;"> 0.0193783 </td> <td style="text-align:right;"> -0.0093700 </td> <td style="text-align:right;"> 0.0481266 </td> <td style="text-align:right;"> 0.0146678 </td> </tr> <tr> <td style="text-align:left;"> fructosamine </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0397864 </td> <td style="text-align:right;"> -0.1198801 </td> <td style="text-align:right;"> 0.0403073 </td> <td style="text-align:right;"> 0.0408649 </td> <td style="text-align:right;"> -0.0678231 </td> <td style="text-align:right;"> -0.1513538 </td> <td style="text-align:right;"> 0.0157075 </td> <td style="text-align:right;"> 0.0426184 </td> <td style="text-align:right;"> -0.0283579 </td> <td style="text-align:right;"> -0.0692447 </td> <td style="text-align:right;"> 0.0125289 </td> <td style="text-align:right;"> 0.0208610 </td> </tr> <tr> <td style="text-align:left;"> glucose </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0692601 </td> <td style="text-align:right;"> 0.0184025 </td> <td style="text-align:right;"> 0.1201176 </td> <td style="text-align:right;"> 0.0259482 </td> <td style="text-align:right;"> 0.1279473 </td> <td style="text-align:right;"> 0.0423001 </td> <td style="text-align:right;"> 0.2135946 </td> <td style="text-align:right;"> 0.0436984 </td> <td style="text-align:right;"> 0.0650887 </td> <td style="text-align:right;"> 0.0218496 </td> <td style="text-align:right;"> 0.1083279 </td> <td style="text-align:right;"> 0.0220612 </td> </tr> <tr> <td style="text-align:left;"> hdl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0650177 </td> <td style="text-align:right;"> -0.1255786 </td> <td style="text-align:right;"> -0.0044568 </td> <td style="text-align:right;"> 0.0308990 </td> <td style="text-align:right;"> 0.1724354 </td> <td style="text-align:right;"> 0.0701062 </td> <td style="text-align:right;"> 0.2747646 </td> <td style="text-align:right;"> 0.0522097 </td> <td style="text-align:right;"> 0.2606961 </td> <td style="text-align:right;"> 0.2180421 </td> <td style="text-align:right;"> 0.3033501 </td> <td style="text-align:right;"> 0.0217626 </td> </tr> <tr> <td style="text-align:left;"> heart weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1766832 </td> <td style="text-align:right;"> 0.0672843 </td> <td style="text-align:right;"> 0.2860820 </td> <td style="text-align:right;"> 0.0558168 </td> <td style="text-align:right;"> 0.3651806 </td> <td style="text-align:right;"> 0.2169840 </td> <td style="text-align:right;"> 0.5133772 </td> <td style="text-align:right;"> 0.0756119 </td> <td style="text-align:right;"> 0.1737615 </td> <td style="text-align:right;"> 0.1409037 </td> <td style="text-align:right;"> 0.2066193 </td> <td style="text-align:right;"> 0.0167645 </td> </tr> <tr> <td style="text-align:left;"> heart weight normalised against body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.0794303 </td> <td style="text-align:right;"> -0.0060591 </td> <td style="text-align:right;"> 0.1649198 </td> <td style="text-align:right;"> 0.0436179 </td> <td style="text-align:right;"> 0.0355574 </td> <td style="text-align:right;"> -0.0973272 </td> <td style="text-align:right;"> 0.1684419 </td> <td style="text-align:right;"> 0.0677995 </td> <td style="text-align:right;"> -0.0495578 </td> <td style="text-align:right;"> -0.0835809 </td> <td style="text-align:right;"> -0.0155346 </td> <td style="text-align:right;"> 0.0173591 </td> </tr> <tr> <td style="text-align:left;"> hematocrit </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0566356 </td> <td style="text-align:right;"> -0.0516862 </td> <td style="text-align:right;"> 0.1649575 </td> <td style="text-align:right;"> 0.0552673 </td> <td style="text-align:right;"> 0.0737071 </td> <td style="text-align:right;"> -0.0328632 </td> <td style="text-align:right;"> 0.1802774 </td> <td style="text-align:right;"> 0.0543736 </td> <td style="text-align:right;"> 0.0173967 </td> <td style="text-align:right;"> 0.0035179 </td> <td style="text-align:right;"> 0.0312754 </td> <td style="text-align:right;"> 0.0070811 </td> </tr> <tr> <td style="text-align:left;"> hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0867000 </td> <td style="text-align:right;"> 0.0269936 </td> <td style="text-align:right;"> 0.1464064 </td> <td style="text-align:right;"> 0.0304630 </td> <td style="text-align:right;"> 0.0867345 </td> <td style="text-align:right;"> 0.0194022 </td> <td style="text-align:right;"> 0.1540668 </td> <td style="text-align:right;"> 0.0343538 </td> <td style="text-align:right;"> 0.0051992 </td> <td style="text-align:right;"> -0.0080216 </td> <td style="text-align:right;"> 0.0184199 </td> <td style="text-align:right;"> 0.0067454 </td> </tr> <tr> <td style="text-align:left;"> hr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0634490 </td> <td style="text-align:right;"> -0.1734699 </td> <td style="text-align:right;"> 0.0465718 </td> <td style="text-align:right;"> 0.0561341 </td> <td style="text-align:right;"> -0.0140315 </td> <td style="text-align:right;"> -0.1488474 </td> <td style="text-align:right;"> 0.1207843 </td> <td style="text-align:right;"> 0.0687849 </td> <td style="text-align:right;"> 0.0406617 </td> <td style="text-align:right;"> -0.0139214 </td> <td style="text-align:right;"> 0.0952448 </td> <td style="text-align:right;"> 0.0278490 </td> </tr> <tr> <td style="text-align:left;"> hrv </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1722593 </td> <td style="text-align:right;"> 0.1094294 </td> <td style="text-align:right;"> 0.2350892 </td> <td style="text-align:right;"> 0.0320567 </td> <td style="text-align:right;"> -0.0813225 </td> <td style="text-align:right;"> -0.2125462 </td> <td style="text-align:right;"> 0.0499011 </td> <td style="text-align:right;"> 0.0669521 </td> <td style="text-align:right;"> -0.2504990 </td> <td style="text-align:right;"> -0.3657436 </td> <td style="text-align:right;"> -0.1352545 </td> <td style="text-align:right;"> 0.0587993 </td> </tr> <tr> <td style="text-align:left;"> initial response to glucose challenge </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Intraperitoneal glucose tolerance test (IPGTT) </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0968821 </td> <td style="text-align:right;"> -0.1503780 </td> <td style="text-align:right;"> -0.0433861 </td> <td style="text-align:right;"> 0.0272943 </td> <td style="text-align:right;"> 0.0429971 </td> <td style="text-align:right;"> 0.0141807 </td> <td style="text-align:right;"> 0.0718136 </td> <td style="text-align:right;"> 0.0147026 </td> <td style="text-align:right;"> 0.1183626 </td> <td style="text-align:right;"> 0.0853242 </td> <td style="text-align:right;"> 0.1514009 </td> <td style="text-align:right;"> 0.0168566 </td> </tr> <tr> <td style="text-align:left;"> insulin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Insulin Blood Level </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0993292 </td> <td style="text-align:right;"> -0.3721975 </td> <td style="text-align:right;"> 0.1735391 </td> <td style="text-align:right;"> 0.1392211 </td> <td style="text-align:right;"> 0.1774003 </td> <td style="text-align:right;"> -0.1938091 </td> <td style="text-align:right;"> 0.5486096 </td> <td style="text-align:right;"> 0.1893960 </td> <td style="text-align:right;"> 0.4445455 </td> <td style="text-align:right;"> 0.0944498 </td> <td style="text-align:right;"> 0.7946412 </td> <td style="text-align:right;"> 0.1786236 </td> </tr> <tr> <td style="text-align:left;"> iron </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0974214 </td> <td style="text-align:right;"> -0.2141737 </td> <td style="text-align:right;"> 0.0193310 </td> <td style="text-align:right;"> 0.0595686 </td> <td style="text-align:right;"> -0.2534898 </td> <td style="text-align:right;"> -0.3963648 </td> <td style="text-align:right;"> -0.1106147 </td> <td style="text-align:right;"> 0.0728968 </td> <td style="text-align:right;"> -0.1527977 </td> <td style="text-align:right;"> -0.1930307 </td> <td style="text-align:right;"> -0.1125646 </td> <td style="text-align:right;"> 0.0205274 </td> </tr> <tr> <td style="text-align:left;"> lactate dehydrogenase </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0941249 </td> <td style="text-align:right;"> -0.0214022 </td> <td style="text-align:right;"> 0.2096519 </td> <td style="text-align:right;"> 0.0589435 </td> <td style="text-align:right;"> 0.1409270 </td> <td style="text-align:right;"> -0.0620594 </td> <td style="text-align:right;"> 0.3439133 </td> <td style="text-align:right;"> 0.1035664 </td> <td style="text-align:right;"> 0.0318801 </td> <td style="text-align:right;"> -0.1412218 </td> <td style="text-align:right;"> 0.2049819 </td> <td style="text-align:right;"> 0.0883189 </td> </tr> <tr> <td style="text-align:left;"> latency to center entry </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.1254239 </td> <td style="text-align:right;"> 0.0330185 </td> <td style="text-align:right;"> 0.2178293 </td> <td style="text-align:right;"> 0.0471465 </td> <td style="text-align:right;"> 0.3641221 </td> <td style="text-align:right;"> 0.2056000 </td> <td style="text-align:right;"> 0.5226441 </td> <td style="text-align:right;"> 0.0808801 </td> <td style="text-align:right;"> 0.2734519 </td> <td style="text-align:right;"> 0.0739366 </td> <td style="text-align:right;"> 0.4729672 </td> <td style="text-align:right;"> 0.1017954 </td> </tr> <tr> <td style="text-align:left;"> ldl-cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.4231644 </td> <td style="text-align:right;"> 0.1551776 </td> <td style="text-align:right;"> 0.6911512 </td> <td style="text-align:right;"> 0.1367305 </td> <td style="text-align:right;"> 0.2669283 </td> <td style="text-align:right;"> -0.0956833 </td> <td style="text-align:right;"> 0.6295400 </td> <td style="text-align:right;"> 0.1850093 </td> <td style="text-align:right;"> -0.1615499 </td> <td style="text-align:right;"> -0.6010478 </td> <td style="text-align:right;"> 0.2779480 </td> <td style="text-align:right;"> 0.2242378 </td> </tr> <tr> <td style="text-align:left;"> lean mass </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1435756 </td> <td style="text-align:right;"> 0.0759342 </td> <td style="text-align:right;"> 0.2112170 </td> <td style="text-align:right;"> 0.0345115 </td> <td style="text-align:right;"> 0.3382447 </td> <td style="text-align:right;"> 0.2664863 </td> <td style="text-align:right;"> 0.4100031 </td> <td style="text-align:right;"> 0.0366121 </td> <td style="text-align:right;"> 0.1928945 </td> <td style="text-align:right;"> 0.1752425 </td> <td style="text-align:right;"> 0.2105465 </td> <td style="text-align:right;"> 0.0090063 </td> </tr> <tr> <td style="text-align:left;"> lean/body weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Body Composition (DEXA lean/fat) </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> 0.1953833 </td> <td style="text-align:right;"> 0.0912480 </td> <td style="text-align:right;"> 0.2995186 </td> <td style="text-align:right;"> 0.0531312 </td> <td style="text-align:right;"> 0.1840786 </td> <td style="text-align:right;"> 0.0863764 </td> <td style="text-align:right;"> 0.2817807 </td> <td style="text-align:right;"> 0.0498490 </td> <td style="text-align:right;"> -0.0122785 </td> <td style="text-align:right;"> -0.0257504 </td> <td style="text-align:right;"> 0.0011934 </td> <td style="text-align:right;"> 0.0068736 </td> </tr> <tr> <td style="text-align:left;"> left anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1854856 </td> <td style="text-align:right;"> -0.4305058 </td> <td style="text-align:right;"> 0.0595347 </td> <td style="text-align:right;"> 0.1250126 </td> <td style="text-align:right;"> -0.1534983 </td> <td style="text-align:right;"> -0.4007283 </td> <td style="text-align:right;"> 0.0937316 </td> <td style="text-align:right;"> 0.1261401 </td> <td style="text-align:right;"> 0.0331746 </td> <td style="text-align:right;"> 0.0284172 </td> <td style="text-align:right;"> 0.0379321 </td> <td style="text-align:right;"> 0.0024273 </td> </tr> <tr> <td style="text-align:left;"> left corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1446634 </td> <td style="text-align:right;"> -0.2339950 </td> <td style="text-align:right;"> -0.0553319 </td> <td style="text-align:right;"> 0.0455782 </td> <td style="text-align:right;"> -0.1352252 </td> <td style="text-align:right;"> -0.2234178 </td> <td style="text-align:right;"> -0.0470327 </td> <td style="text-align:right;"> 0.0449970 </td> <td style="text-align:right;"> 0.0075283 </td> <td style="text-align:right;"> -0.0057082 </td> <td style="text-align:right;"> 0.0207648 </td> <td style="text-align:right;"> 0.0067535 </td> </tr> <tr> <td style="text-align:left;"> left inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0480458 </td> <td style="text-align:right;"> -0.0360706 </td> <td style="text-align:right;"> 0.1321622 </td> <td style="text-align:right;"> 0.0429173 </td> <td style="text-align:right;"> 0.0487217 </td> <td style="text-align:right;"> -0.0347622 </td> <td style="text-align:right;"> 0.1322057 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0006956 </td> <td style="text-align:right;"> -0.0095012 </td> <td style="text-align:right;"> 0.0108923 </td> <td style="text-align:right;"> 0.0052025 </td> </tr> <tr> <td style="text-align:left;"> left outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0675012 </td> <td style="text-align:right;"> -0.1511666 </td> <td style="text-align:right;"> 0.0161641 </td> <td style="text-align:right;"> 0.0426872 </td> <td style="text-align:right;"> -0.0618025 </td> <td style="text-align:right;"> -0.1452865 </td> <td style="text-align:right;"> 0.0216814 </td> <td style="text-align:right;"> 0.0425946 </td> <td style="text-align:right;"> 0.0063811 </td> <td style="text-align:right;"> 0.0011702 </td> <td style="text-align:right;"> 0.0115921 </td> <td style="text-align:right;"> 0.0026587 </td> </tr> <tr> <td style="text-align:left;"> left posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2631046 </td> <td style="text-align:right;"> -0.4734756 </td> <td style="text-align:right;"> -0.0527336 </td> <td style="text-align:right;"> 0.1073341 </td> <td style="text-align:right;"> -0.2687360 </td> <td style="text-align:right;"> -0.4790035 </td> <td style="text-align:right;"> -0.0584686 </td> <td style="text-align:right;"> 0.1072813 </td> <td style="text-align:right;"> -0.0026027 </td> <td style="text-align:right;"> -0.0146655 </td> <td style="text-align:right;"> 0.0094600 </td> <td style="text-align:right;"> 0.0061546 </td> </tr> <tr> <td style="text-align:left;"> left total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1975770 </td> <td style="text-align:right;"> -0.4386627 </td> <td style="text-align:right;"> 0.0435087 </td> <td style="text-align:right;"> 0.1230052 </td> <td style="text-align:right;"> -0.1932648 </td> <td style="text-align:right;"> -0.4269751 </td> <td style="text-align:right;"> 0.0404456 </td> <td style="text-align:right;"> 0.1192422 </td> <td style="text-align:right;"> 0.0027995 </td> <td style="text-align:right;"> -0.0034907 </td> <td style="text-align:right;"> 0.0090898 </td> <td style="text-align:right;"> 0.0032094 </td> </tr> <tr> <td style="text-align:left;"> locomotor activity </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Combined SHIRPA and Dysmorphology </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0960106 </td> <td style="text-align:right;"> 0.0224214 </td> <td style="text-align:right;"> 0.1695997 </td> <td style="text-align:right;"> 0.0375462 </td> <td style="text-align:right;"> -0.0159064 </td> <td style="text-align:right;"> -0.0579694 </td> <td style="text-align:right;"> 0.0261566 </td> <td style="text-align:right;"> 0.0214611 </td> <td style="text-align:right;"> -0.1105803 </td> <td style="text-align:right;"> -0.1761043 </td> <td style="text-align:right;"> -0.0450562 </td> <td style="text-align:right;"> 0.0334313 </td> </tr> <tr> <td style="text-align:left;"> lvawd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0228924 </td> <td style="text-align:right;"> -0.0247048 </td> <td style="text-align:right;"> 0.0704896 </td> <td style="text-align:right;"> 0.0242847 </td> <td style="text-align:right;"> 0.0454075 </td> <td style="text-align:right;"> -0.0013249 </td> <td style="text-align:right;"> 0.0921399 </td> <td style="text-align:right;"> 0.0238435 </td> <td style="text-align:right;"> 0.0246614 </td> <td style="text-align:right;"> 0.0114095 </td> <td style="text-align:right;"> 0.0379132 </td> <td style="text-align:right;"> 0.0067613 </td> </tr> <tr> <td style="text-align:left;"> lvaws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0017749 </td> <td style="text-align:right;"> -0.2517581 </td> <td style="text-align:right;"> 0.2482083 </td> <td style="text-align:right;"> 0.1275448 </td> <td style="text-align:right;"> 0.0232601 </td> <td style="text-align:right;"> -0.1776617 </td> <td style="text-align:right;"> 0.2241819 </td> <td style="text-align:right;"> 0.1025130 </td> <td style="text-align:right;"> 0.0112569 </td> <td style="text-align:right;"> -0.0306073 </td> <td style="text-align:right;"> 0.0531211 </td> <td style="text-align:right;"> 0.0213597 </td> </tr> <tr> <td style="text-align:left;"> lvidd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0453256 </td> <td style="text-align:right;"> -0.0241892 </td> <td style="text-align:right;"> 0.1148405 </td> <td style="text-align:right;"> 0.0354674 </td> <td style="text-align:right;"> 0.0981450 </td> <td style="text-align:right;"> 0.0208146 </td> <td style="text-align:right;"> 0.1754754 </td> <td style="text-align:right;"> 0.0394550 </td> <td style="text-align:right;"> 0.0528053 </td> <td style="text-align:right;"> 0.0378669 </td> <td style="text-align:right;"> 0.0677436 </td> <td style="text-align:right;"> 0.0076218 </td> </tr> <tr> <td style="text-align:left;"> lvids </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0635228 </td> <td style="text-align:right;"> -0.1990947 </td> <td style="text-align:right;"> 0.0720491 </td> <td style="text-align:right;"> 0.0691706 </td> <td style="text-align:right;"> 0.0083352 </td> <td style="text-align:right;"> -0.1335894 </td> <td style="text-align:right;"> 0.1502598 </td> <td style="text-align:right;"> 0.0724118 </td> <td style="text-align:right;"> 0.0756177 </td> <td style="text-align:right;"> 0.0525777 </td> <td style="text-align:right;"> 0.0986576 </td> <td style="text-align:right;"> 0.0117553 </td> </tr> <tr> <td style="text-align:left;"> lvpwd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0317376 </td> <td style="text-align:right;"> -0.1258062 </td> <td style="text-align:right;"> 0.0623311 </td> <td style="text-align:right;"> 0.0479951 </td> <td style="text-align:right;"> -0.0104248 </td> <td style="text-align:right;"> -0.1271922 </td> <td style="text-align:right;"> 0.1063426 </td> <td style="text-align:right;"> 0.0595763 </td> <td style="text-align:right;"> 0.0302674 </td> <td style="text-align:right;"> 0.0131900 </td> <td style="text-align:right;"> 0.0473448 </td> <td style="text-align:right;"> 0.0087131 </td> </tr> <tr> <td style="text-align:left;"> lvpws </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0190522 </td> <td style="text-align:right;"> -0.1014670 </td> <td style="text-align:right;"> 0.0633627 </td> <td style="text-align:right;"> 0.0420492 </td> <td style="text-align:right;"> 0.0089592 </td> <td style="text-align:right;"> -0.0823356 </td> <td style="text-align:right;"> 0.1002540 </td> <td style="text-align:right;"> 0.0465798 </td> <td style="text-align:right;"> 0.0268487 </td> <td style="text-align:right;"> 0.0063146 </td> <td style="text-align:right;"> 0.0473828 </td> <td style="text-align:right;"> 0.0104768 </td> </tr> <tr> <td style="text-align:left;"> magnesium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Urinalysis </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0161699 </td> <td style="text-align:right;"> -0.0231196 </td> <td style="text-align:right;"> 0.0554593 </td> <td style="text-align:right;"> 0.0200460 </td> <td style="text-align:right;"> -0.0513056 </td> <td style="text-align:right;"> -0.1167021 </td> <td style="text-align:right;"> 0.0140909 </td> <td style="text-align:right;"> 0.0333662 </td> <td style="text-align:right;"> -0.0413354 </td> <td style="text-align:right;"> -0.1135580 </td> <td style="text-align:right;"> 0.0308871 </td> <td style="text-align:right;"> 0.0368489 </td> </tr> <tr> <td style="text-align:left;"> mean cell hemoglobin concentration </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0378015 </td> <td style="text-align:right;"> -0.0880637 </td> <td style="text-align:right;"> 0.1636666 </td> <td style="text-align:right;"> 0.0642181 </td> <td style="text-align:right;"> 0.0253063 </td> <td style="text-align:right;"> -0.1086076 </td> <td style="text-align:right;"> 0.1592202 </td> <td style="text-align:right;"> 0.0683247 </td> <td style="text-align:right;"> -0.0113450 </td> <td style="text-align:right;"> -0.0150702 </td> <td style="text-align:right;"> -0.0076199 </td> <td style="text-align:right;"> 0.0019006 </td> </tr> <tr> <td style="text-align:left;"> mean cell volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0039175 </td> <td style="text-align:right;"> -0.0957495 </td> <td style="text-align:right;"> 0.1035845 </td> <td style="text-align:right;"> 0.0508514 </td> <td style="text-align:right;"> -0.0030447 </td> <td style="text-align:right;"> -0.0961742 </td> <td style="text-align:right;"> 0.0900848 </td> <td style="text-align:right;"> 0.0475159 </td> <td style="text-align:right;"> -0.0063502 </td> <td style="text-align:right;"> -0.0099649 </td> <td style="text-align:right;"> -0.0027355 </td> <td style="text-align:right;"> 0.0018443 </td> </tr> <tr> <td style="text-align:left;"> mean corpuscular hemoglobin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0025833 </td> <td style="text-align:right;"> -0.0653065 </td> <td style="text-align:right;"> 0.0601398 </td> <td style="text-align:right;"> 0.0320022 </td> <td style="text-align:right;"> -0.0193465 </td> <td style="text-align:right;"> -0.0824670 </td> <td style="text-align:right;"> 0.0437741 </td> <td style="text-align:right;"> 0.0322049 </td> <td style="text-align:right;"> -0.0169768 </td> <td style="text-align:right;"> -0.0197231 </td> <td style="text-align:right;"> -0.0142305 </td> <td style="text-align:right;"> 0.0014012 </td> </tr> <tr> <td style="text-align:left;"> mean platelet volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0487366 </td> <td style="text-align:right;"> -0.0044688 </td> <td style="text-align:right;"> 0.1019419 </td> <td style="text-align:right;"> 0.0271461 </td> <td style="text-align:right;"> 0.0353913 </td> <td style="text-align:right;"> -0.0210323 </td> <td style="text-align:right;"> 0.0918150 </td> <td style="text-align:right;"> 0.0287881 </td> <td style="text-align:right;"> -0.0174066 </td> <td style="text-align:right;"> -0.0276044 </td> <td style="text-align:right;"> -0.0072089 </td> <td style="text-align:right;"> 0.0052030 </td> </tr> <tr> <td style="text-align:left;"> mean r amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0084703 </td> <td style="text-align:right;"> -0.0282092 </td> <td style="text-align:right;"> 0.0451499 </td> <td style="text-align:right;"> 0.0187144 </td> <td style="text-align:right;"> -0.0948208 </td> <td style="text-align:right;"> -0.1630495 </td> <td style="text-align:right;"> -0.0265922 </td> <td style="text-align:right;"> 0.0348112 </td> <td style="text-align:right;"> -0.0835612 </td> <td style="text-align:right;"> -0.1503108 </td> <td style="text-align:right;"> -0.0168116 </td> <td style="text-align:right;"> 0.0340565 </td> </tr> <tr> <td style="text-align:left;"> mean sr amplitude </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0284617 </td> <td style="text-align:right;"> -0.0131943 </td> <td style="text-align:right;"> 0.0701178 </td> <td style="text-align:right;"> 0.0212535 </td> <td style="text-align:right;"> -0.0876811 </td> <td style="text-align:right;"> -0.1270777 </td> <td style="text-align:right;"> -0.0482845 </td> <td style="text-align:right;"> 0.0201007 </td> <td style="text-align:right;"> -0.1130259 </td> <td style="text-align:right;"> -0.1558048 </td> <td style="text-align:right;"> -0.0702470 </td> <td style="text-align:right;"> 0.0218264 </td> </tr> <tr> <td style="text-align:left;"> number of center entries </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> 0.0150703 </td> <td style="text-align:right;"> -0.0534907 </td> <td style="text-align:right;"> 0.0836313 </td> <td style="text-align:right;"> 0.0349807 </td> <td style="text-align:right;"> -0.0361259 </td> <td style="text-align:right;"> -0.0952472 </td> <td style="text-align:right;"> 0.0229955 </td> <td style="text-align:right;"> 0.0301645 </td> <td style="text-align:right;"> -0.0588092 </td> <td style="text-align:right;"> -0.1679907 </td> <td style="text-align:right;"> 0.0503723 </td> <td style="text-align:right;"> 0.0557059 </td> </tr> <tr> <td style="text-align:left;"> number of rears - total </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0011326 </td> <td style="text-align:right;"> -0.1141113 </td> <td style="text-align:right;"> 0.1118461 </td> <td style="text-align:right;"> 0.0576432 </td> <td style="text-align:right;"> 0.1869490 </td> <td style="text-align:right;"> -0.0392422 </td> <td style="text-align:right;"> 0.4131402 </td> <td style="text-align:right;"> 0.1154058 </td> <td style="text-align:right;"> 0.1794328 </td> <td style="text-align:right;"> 0.0568682 </td> <td style="text-align:right;"> 0.3019974 </td> <td style="text-align:right;"> 0.0625341 </td> </tr> <tr> <td style="text-align:left;"> others </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1684902 </td> <td style="text-align:right;"> -0.2596648 </td> <td style="text-align:right;"> -0.0773156 </td> <td style="text-align:right;"> 0.0465185 </td> <td style="text-align:right;"> -0.1515195 </td> <td style="text-align:right;"> -0.2435956 </td> <td style="text-align:right;"> -0.0594434 </td> <td style="text-align:right;"> 0.0469785 </td> <td style="text-align:right;"> 0.0196158 </td> <td style="text-align:right;"> 0.0049349 </td> <td style="text-align:right;"> 0.0342967 </td> <td style="text-align:right;"> 0.0074904 </td> </tr> <tr> <td style="text-align:left;"> pdcs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.1732553 </td> <td style="text-align:right;"> -0.4003845 </td> <td style="text-align:right;"> 0.0538738 </td> <td style="text-align:right;"> 0.1158844 </td> <td style="text-align:right;"> -0.2572491 </td> <td style="text-align:right;"> -0.7186201 </td> <td style="text-align:right;"> 0.2041219 </td> <td style="text-align:right;"> 0.2353977 </td> <td style="text-align:right;"> -0.0915619 </td> <td style="text-align:right;"> -0.2522236 </td> <td style="text-align:right;"> 0.0690997 </td> <td style="text-align:right;"> 0.0819717 </td> </tr> <tr> <td style="text-align:left;"> percentage center time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0219679 </td> <td style="text-align:right;"> -0.0863184 </td> <td style="text-align:right;"> 0.0423826 </td> <td style="text-align:right;"> 0.0328325 </td> <td style="text-align:right;"> -0.0188907 </td> <td style="text-align:right;"> -0.0912088 </td> <td style="text-align:right;"> 0.0534274 </td> <td style="text-align:right;"> 0.0368977 </td> <td style="text-align:right;"> -0.0061802 </td> <td style="text-align:right;"> -0.0972542 </td> <td style="text-align:right;"> 0.0848938 </td> <td style="text-align:right;"> 0.0464672 </td> </tr> <tr> <td style="text-align:left;"> periphery average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0444272 </td> <td style="text-align:right;"> -0.1082870 </td> <td style="text-align:right;"> 0.0194327 </td> <td style="text-align:right;"> 0.0325822 </td> <td style="text-align:right;"> -0.1401304 </td> <td style="text-align:right;"> -0.2117709 </td> <td style="text-align:right;"> -0.0684898 </td> <td style="text-align:right;"> 0.0365520 </td> <td style="text-align:right;"> -0.0963838 </td> <td style="text-align:right;"> -0.1446043 </td> <td style="text-align:right;"> -0.0481633 </td> <td style="text-align:right;"> 0.0246028 </td> </tr> <tr> <td style="text-align:left;"> periphery distance travelled </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0313217 </td> <td style="text-align:right;"> -0.0918314 </td> <td style="text-align:right;"> 0.0291879 </td> <td style="text-align:right;"> 0.0308728 </td> <td style="text-align:right;"> -0.1342236 </td> <td style="text-align:right;"> -0.1874097 </td> <td style="text-align:right;"> -0.0810376 </td> <td style="text-align:right;"> 0.0271362 </td> <td style="text-align:right;"> -0.1037239 </td> <td style="text-align:right;"> -0.1714836 </td> <td style="text-align:right;"> -0.0359643 </td> <td style="text-align:right;"> 0.0345719 </td> </tr> <tr> <td style="text-align:left;"> periphery permanence time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0369177 </td> <td style="text-align:right;"> -0.1277076 </td> <td style="text-align:right;"> 0.0538721 </td> <td style="text-align:right;"> 0.0463222 </td> <td style="text-align:right;"> -0.0294978 </td> <td style="text-align:right;"> -0.1006346 </td> <td style="text-align:right;"> 0.0416390 </td> <td style="text-align:right;"> 0.0362950 </td> <td style="text-align:right;"> 0.0077038 </td> <td style="text-align:right;"> -0.0137850 </td> <td style="text-align:right;"> 0.0291927 </td> <td style="text-align:right;"> 0.0109639 </td> </tr> <tr> <td style="text-align:left;"> periphery resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0536346 </td> <td style="text-align:right;"> -0.1266045 </td> <td style="text-align:right;"> 0.0193353 </td> <td style="text-align:right;"> 0.0372302 </td> <td style="text-align:right;"> -0.0572459 </td> <td style="text-align:right;"> -0.1071515 </td> <td style="text-align:right;"> -0.0073404 </td> <td style="text-align:right;"> 0.0254625 </td> <td style="text-align:right;"> 0.0026007 </td> <td style="text-align:right;"> -0.0558538 </td> <td style="text-align:right;"> 0.0610552 </td> <td style="text-align:right;"> 0.0298243 </td> </tr> <tr> <td style="text-align:left;"> phosphorus </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0485897 </td> <td style="text-align:right;"> -0.0839101 </td> <td style="text-align:right;"> -0.0132693 </td> <td style="text-align:right;"> 0.0180209 </td> <td style="text-align:right;"> -0.0826120 </td> <td style="text-align:right;"> -0.1576473 </td> <td style="text-align:right;"> -0.0075767 </td> <td style="text-align:right;"> 0.0382840 </td> <td style="text-align:right;"> -0.0420616 </td> <td style="text-align:right;"> -0.0813582 </td> <td style="text-align:right;"> -0.0027650 </td> <td style="text-align:right;"> 0.0200497 </td> </tr> <tr> <td style="text-align:left;"> platelet count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0737198 </td> <td style="text-align:right;"> 0.0205862 </td> <td style="text-align:right;"> 0.1268534 </td> <td style="text-align:right;"> 0.0271095 </td> <td style="text-align:right;"> 0.2415135 </td> <td style="text-align:right;"> 0.1865330 </td> <td style="text-align:right;"> 0.2964940 </td> <td style="text-align:right;"> 0.0280518 </td> <td style="text-align:right;"> 0.1642192 </td> <td style="text-align:right;"> 0.1369820 </td> <td style="text-align:right;"> 0.1914563 </td> <td style="text-align:right;"> 0.0138968 </td> </tr> <tr> <td style="text-align:left;"> pnn5(6>ms) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.2906905 </td> <td style="text-align:right;"> 0.1716202 </td> <td style="text-align:right;"> 0.4097607 </td> <td style="text-align:right;"> 0.0607512 </td> <td style="text-align:right;"> -0.2926013 </td> <td style="text-align:right;"> -0.5272121 </td> <td style="text-align:right;"> -0.0579905 </td> <td style="text-align:right;"> 0.1197016 </td> <td style="text-align:right;"> -0.6004767 </td> <td style="text-align:right;"> -0.9244113 </td> <td style="text-align:right;"> -0.2765420 </td> <td style="text-align:right;"> 0.1652758 </td> </tr> <tr> <td style="text-align:left;"> potassium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0705522 </td> <td style="text-align:right;"> -0.2214989 </td> <td style="text-align:right;"> 0.0803945 </td> <td style="text-align:right;"> 0.0770150 </td> <td style="text-align:right;"> -0.0074675 </td> <td style="text-align:right;"> -0.1729366 </td> <td style="text-align:right;"> 0.1580015 </td> <td style="text-align:right;"> 0.0844245 </td> <td style="text-align:right;"> 0.0704162 </td> <td style="text-align:right;"> 0.0476647 </td> <td style="text-align:right;"> 0.0931676 </td> <td style="text-align:right;"> 0.0116081 </td> </tr> <tr> <td style="text-align:left;"> pq </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0650960 </td> <td style="text-align:right;"> -0.1538776 </td> <td style="text-align:right;"> 0.0236857 </td> <td style="text-align:right;"> 0.0452976 </td> <td style="text-align:right;"> -0.0648322 </td> <td style="text-align:right;"> -0.1270688 </td> <td style="text-align:right;"> -0.0025955 </td> <td style="text-align:right;"> 0.0317540 </td> <td style="text-align:right;"> 0.0015656 </td> <td style="text-align:right;"> -0.0259865 </td> <td style="text-align:right;"> 0.0291178 </td> <td style="text-align:right;"> 0.0140575 </td> </tr> <tr> <td style="text-align:left;"> pr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0564860 </td> <td style="text-align:right;"> -0.1048371 </td> <td style="text-align:right;"> -0.0081349 </td> <td style="text-align:right;"> 0.0246694 </td> <td style="text-align:right;"> -0.0754718 </td> <td style="text-align:right;"> -0.1235224 </td> <td style="text-align:right;"> -0.0274213 </td> <td style="text-align:right;"> 0.0245160 </td> <td style="text-align:right;"> -0.0183785 </td> <td style="text-align:right;"> -0.0319887 </td> <td style="text-align:right;"> -0.0047684 </td> <td style="text-align:right;"> 0.0069441 </td> </tr> <tr> <td style="text-align:left;"> qrs </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0725454 </td> <td style="text-align:right;"> 0.0354722 </td> <td style="text-align:right;"> 0.1096185 </td> <td style="text-align:right;"> 0.0189152 </td> <td style="text-align:right;"> 0.0681074 </td> <td style="text-align:right;"> 0.0300869 </td> <td style="text-align:right;"> 0.1061278 </td> <td style="text-align:right;"> 0.0193986 </td> <td style="text-align:right;"> -0.0054233 </td> <td style="text-align:right;"> -0.0154885 </td> <td style="text-align:right;"> 0.0046418 </td> <td style="text-align:right;"> 0.0051354 </td> </tr> <tr> <td style="text-align:left;"> qtc </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0328106 </td> <td style="text-align:right;"> -0.0101032 </td> <td style="text-align:right;"> 0.0757244 </td> <td style="text-align:right;"> 0.0218952 </td> <td style="text-align:right;"> 0.0310473 </td> <td style="text-align:right;"> -0.0207365 </td> <td style="text-align:right;"> 0.0828310 </td> <td style="text-align:right;"> 0.0264208 </td> <td style="text-align:right;"> -0.0005046 </td> <td style="text-align:right;"> -0.0085696 </td> <td style="text-align:right;"> 0.0075604 </td> <td style="text-align:right;"> 0.0041149 </td> </tr> <tr> <td style="text-align:left;"> qtc dispersion </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0031258 </td> <td style="text-align:right;"> -0.0523919 </td> <td style="text-align:right;"> 0.0586435 </td> <td style="text-align:right;"> 0.0283259 </td> <td style="text-align:right;"> -0.0046501 </td> <td style="text-align:right;"> -0.1060530 </td> <td style="text-align:right;"> 0.0967528 </td> <td style="text-align:right;"> 0.0517371 </td> <td style="text-align:right;"> -0.0077373 </td> <td style="text-align:right;"> -0.0510162 </td> <td style="text-align:right;"> 0.0355416 </td> <td style="text-align:right;"> 0.0220815 </td> </tr> <tr> <td style="text-align:left;"> red blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.0773455 </td> <td style="text-align:right;"> 0.0071933 </td> <td style="text-align:right;"> 0.1474977 </td> <td style="text-align:right;"> 0.0357926 </td> <td style="text-align:right;"> 0.0997278 </td> <td style="text-align:right;"> 0.0316996 </td> <td style="text-align:right;"> 0.1677560 </td> <td style="text-align:right;"> 0.0347089 </td> <td style="text-align:right;"> 0.0228493 </td> <td style="text-align:right;"> 0.0088583 </td> <td style="text-align:right;"> 0.0368404 </td> <td style="text-align:right;"> 0.0071384 </td> </tr> <tr> <td style="text-align:left;"> red blood cell distribution width </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> 0.1248464 </td> <td style="text-align:right;"> -0.0035148 </td> <td style="text-align:right;"> 0.2532076 </td> <td style="text-align:right;"> 0.0654916 </td> <td style="text-align:right;"> 0.1353460 </td> <td style="text-align:right;"> -0.0035862 </td> <td style="text-align:right;"> 0.2742782 </td> <td style="text-align:right;"> 0.0708851 </td> <td style="text-align:right;"> 0.0104789 </td> <td style="text-align:right;"> -0.0032056 </td> <td style="text-align:right;"> 0.0241635 </td> <td style="text-align:right;"> 0.0069821 </td> </tr> <tr> <td style="text-align:left;"> respiration rate </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.1384843 </td> <td style="text-align:right;"> -0.2178736 </td> <td style="text-align:right;"> -0.0590950 </td> <td style="text-align:right;"> 0.0405055 </td> <td style="text-align:right;"> -0.0703570 </td> <td style="text-align:right;"> -0.1795875 </td> <td style="text-align:right;"> 0.0388735 </td> <td style="text-align:right;"> 0.0557309 </td> <td style="text-align:right;"> 0.0611034 </td> <td style="text-align:right;"> 0.0227141 </td> <td style="text-align:right;"> 0.0994926 </td> <td style="text-align:right;"> 0.0195867 </td> </tr> <tr> <td style="text-align:left;"> respiratory exchange ratio </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.0116565 </td> <td style="text-align:right;"> -0.0896490 </td> <td style="text-align:right;"> 0.0663361 </td> <td style="text-align:right;"> 0.0397928 </td> <td style="text-align:right;"> -0.0106530 </td> <td style="text-align:right;"> -0.0878483 </td> <td style="text-align:right;"> 0.0665424 </td> <td style="text-align:right;"> 0.0393861 </td> <td style="text-align:right;"> 0.0017027 </td> <td style="text-align:right;"> -0.0057348 </td> <td style="text-align:right;"> 0.0091402 </td> <td style="text-align:right;"> 0.0037947 </td> </tr> <tr> <td style="text-align:left;"> right anterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.4491432 </td> <td style="text-align:right;"> -1.3293546 </td> <td style="text-align:right;"> 0.4310682 </td> <td style="text-align:right;"> 0.4490957 </td> <td style="text-align:right;"> -0.4157377 </td> <td style="text-align:right;"> -1.2918620 </td> <td style="text-align:right;"> 0.4603867 </td> <td style="text-align:right;"> 0.4470104 </td> <td style="text-align:right;"> 0.0316098 </td> <td style="text-align:right;"> 0.0264512 </td> <td style="text-align:right;"> 0.0367685 </td> <td style="text-align:right;"> 0.0026320 </td> </tr> <tr> <td style="text-align:left;"> right corneal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0355898 </td> <td style="text-align:right;"> -0.2280522 </td> <td style="text-align:right;"> 0.1568726 </td> <td style="text-align:right;"> 0.0981969 </td> <td style="text-align:right;"> -0.0306550 </td> <td style="text-align:right;"> -0.1963692 </td> <td style="text-align:right;"> 0.1350592 </td> <td style="text-align:right;"> 0.0845496 </td> <td style="text-align:right;"> -0.0013855 </td> <td style="text-align:right;"> -0.0237830 </td> <td style="text-align:right;"> 0.0210121 </td> <td style="text-align:right;"> 0.0114275 </td> </tr> <tr> <td style="text-align:left;"> right inner nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.2545083 </td> <td style="text-align:right;"> -0.7633116 </td> <td style="text-align:right;"> 0.2542949 </td> <td style="text-align:right;"> 0.2595983 </td> <td style="text-align:right;"> -0.2785114 </td> <td style="text-align:right;"> -0.8373133 </td> <td style="text-align:right;"> 0.2802906 </td> <td style="text-align:right;"> 0.2851083 </td> <td style="text-align:right;"> -0.0175090 </td> <td style="text-align:right;"> -0.0664158 </td> <td style="text-align:right;"> 0.0313978 </td> <td style="text-align:right;"> 0.0249529 </td> </tr> <tr> <td style="text-align:left;"> right outer nuclear layer </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> 0.0061253 </td> <td style="text-align:right;"> -0.0781241 </td> <td style="text-align:right;"> 0.0903746 </td> <td style="text-align:right;"> 0.0429851 </td> <td style="text-align:right;"> 0.0109098 </td> <td style="text-align:right;"> -0.0731427 </td> <td style="text-align:right;"> 0.0949622 </td> <td style="text-align:right;"> 0.0428847 </td> <td style="text-align:right;"> 0.0055513 </td> <td style="text-align:right;"> 0.0000519 </td> <td style="text-align:right;"> 0.0110508 </td> <td style="text-align:right;"> 0.0028059 </td> </tr> <tr> <td style="text-align:left;"> right posterior chamber depth </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.0775673 </td> <td style="text-align:right;"> -0.2905688 </td> <td style="text-align:right;"> 0.1354341 </td> <td style="text-align:right;"> 0.1086762 </td> <td style="text-align:right;"> -0.0764571 </td> <td style="text-align:right;"> -0.2893152 </td> <td style="text-align:right;"> 0.1364010 </td> <td style="text-align:right;"> 0.1086031 </td> <td style="text-align:right;"> 0.0071990 </td> <td style="text-align:right;"> -0.0178434 </td> <td style="text-align:right;"> 0.0322413 </td> <td style="text-align:right;"> 0.0127769 </td> </tr> <tr> <td style="text-align:left;"> right total retinal thickness </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Eye Morphology </td> <td style="text-align:left;"> Eye </td> <td style="text-align:right;"> -0.1987993 </td> <td style="text-align:right;"> -0.6457320 </td> <td style="text-align:right;"> 0.2481333 </td> <td style="text-align:right;"> 0.2280310 </td> <td style="text-align:right;"> -0.1925482 </td> <td style="text-align:right;"> -0.6285715 </td> <td style="text-align:right;"> 0.2434750 </td> <td style="text-align:right;"> 0.2224649 </td> <td style="text-align:right;"> 0.0052882 </td> <td style="text-align:right;"> -0.0045957 </td> <td style="text-align:right;"> 0.0151720 </td> <td style="text-align:right;"> 0.0050429 </td> </tr> <tr> <td style="text-align:left;"> rmssd </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.1800273 </td> <td style="text-align:right;"> -0.0882317 </td> <td style="text-align:right;"> 0.4482864 </td> <td style="text-align:right;"> 0.1368694 </td> <td style="text-align:right;"> -0.0161048 </td> <td style="text-align:right;"> -0.4112809 </td> <td style="text-align:right;"> 0.3790712 </td> <td style="text-align:right;"> 0.2016241 </td> <td style="text-align:right;"> -0.1178703 </td> <td style="text-align:right;"> -0.2449843 </td> <td style="text-align:right;"> 0.0092436 </td> <td style="text-align:right;"> 0.0648552 </td> </tr> <tr> <td style="text-align:left;"> rp macrophage (cd19- cd11c-) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> -0.0765771 </td> <td style="text-align:right;"> -0.3398075 </td> <td style="text-align:right;"> 0.1866533 </td> <td style="text-align:right;"> 0.1343037 </td> <td style="text-align:right;"> -0.0747691 </td> <td style="text-align:right;"> -0.3351316 </td> <td style="text-align:right;"> 0.1855933 </td> <td style="text-align:right;"> 0.1328404 </td> <td style="text-align:right;"> -0.0746396 </td> <td style="text-align:right;"> -0.2072980 </td> <td style="text-align:right;"> 0.0580188 </td> <td style="text-align:right;"> 0.0676841 </td> </tr> <tr> <td style="text-align:left;"> rr </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> -0.0761505 </td> <td style="text-align:right;"> -0.1876687 </td> <td style="text-align:right;"> 0.0353678 </td> <td style="text-align:right;"> 0.0568981 </td> <td style="text-align:right;"> -0.0896869 </td> <td style="text-align:right;"> -0.2063458 </td> <td style="text-align:right;"> 0.0269721 </td> <td style="text-align:right;"> 0.0595210 </td> <td style="text-align:right;"> -0.0125023 </td> <td style="text-align:right;"> -0.0214082 </td> <td style="text-align:right;"> -0.0035963 </td> <td style="text-align:right;"> 0.0045440 </td> </tr> <tr> <td style="text-align:left;"> sodium </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0262100 </td> <td style="text-align:right;"> -0.1171674 </td> <td style="text-align:right;"> 0.1695873 </td> <td style="text-align:right;"> 0.0731531 </td> <td style="text-align:right;"> 0.0338228 </td> <td style="text-align:right;"> -0.1337162 </td> <td style="text-align:right;"> 0.2013618 </td> <td style="text-align:right;"> 0.0854806 </td> <td style="text-align:right;"> 0.0099680 </td> <td style="text-align:right;"> 0.0065815 </td> <td style="text-align:right;"> 0.0133545 </td> <td style="text-align:right;"> 0.0017278 </td> </tr> <tr> <td style="text-align:left;"> spleen weight </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Immunophenotyping </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:right;"> 0.1874259 </td> <td style="text-align:right;"> -0.0500875 </td> <td style="text-align:right;"> 0.4249393 </td> <td style="text-align:right;"> 0.1211825 </td> <td style="text-align:right;"> 0.1133706 </td> <td style="text-align:right;"> -0.1604807 </td> <td style="text-align:right;"> 0.3872220 </td> <td style="text-align:right;"> 0.1397227 </td> <td style="text-align:right;"> -0.1542349 </td> <td style="text-align:right;"> -0.2104415 </td> <td style="text-align:right;"> -0.0980283 </td> <td style="text-align:right;"> 0.0286774 </td> </tr> <tr> <td style="text-align:left;"> st </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Electrocardiogram (ECG) </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0032888 </td> <td style="text-align:right;"> -0.0544512 </td> <td style="text-align:right;"> 0.0610288 </td> <td style="text-align:right;"> 0.0294597 </td> <td style="text-align:right;"> -0.0054976 </td> <td style="text-align:right;"> -0.0811810 </td> <td style="text-align:right;"> 0.0701858 </td> <td style="text-align:right;"> 0.0386147 </td> <td style="text-align:right;"> -0.0034902 </td> <td style="text-align:right;"> -0.0175917 </td> <td style="text-align:right;"> 0.0106113 </td> <td style="text-align:right;"> 0.0071948 </td> </tr> <tr> <td style="text-align:left;"> stroke volume </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Echo </td> <td style="text-align:left;"> Heart </td> <td style="text-align:right;"> 0.0594276 </td> <td style="text-align:right;"> -0.0782445 </td> <td style="text-align:right;"> 0.1970997 </td> <td style="text-align:right;"> 0.0702422 </td> <td style="text-align:right;"> 0.1574330 </td> <td style="text-align:right;"> 0.0091891 </td> <td style="text-align:right;"> 0.3056769 </td> <td style="text-align:right;"> 0.0756360 </td> <td style="text-align:right;"> 0.0937375 </td> <td style="text-align:right;"> 0.0775587 </td> <td style="text-align:right;"> 0.1099162 </td> <td style="text-align:right;"> 0.0082546 </td> </tr> <tr> <td style="text-align:left;"> tibia length </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Heart Weight </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:right;"> -0.1475403 </td> <td style="text-align:right;"> -0.4396127 </td> <td style="text-align:right;"> 0.1445320 </td> <td style="text-align:right;"> 0.1490192 </td> <td style="text-align:right;"> -0.1374401 </td> <td style="text-align:right;"> -0.4261352 </td> <td style="text-align:right;"> 0.1512551 </td> <td style="text-align:right;"> 0.1472961 </td> <td style="text-align:right;"> 0.0095199 </td> <td style="text-align:right;"> 0.0059199 </td> <td style="text-align:right;"> 0.0131200 </td> <td style="text-align:right;"> 0.0018368 </td> </tr> <tr> <td style="text-align:left;"> total bilirubin </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0605449 </td> <td style="text-align:right;"> -0.0097669 </td> <td style="text-align:right;"> 0.1308567 </td> <td style="text-align:right;"> 0.0358740 </td> <td style="text-align:right;"> 0.0022671 </td> <td style="text-align:right;"> -0.0859910 </td> <td style="text-align:right;"> 0.0905252 </td> <td style="text-align:right;"> 0.0450305 </td> <td style="text-align:right;"> -0.0550333 </td> <td style="text-align:right;"> -0.0979518 </td> <td style="text-align:right;"> -0.0121148 </td> <td style="text-align:right;"> 0.0218976 </td> </tr> <tr> <td style="text-align:left;"> total cholesterol </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0942595 </td> <td style="text-align:right;"> -0.0751596 </td> <td style="text-align:right;"> 0.2636786 </td> <td style="text-align:right;"> 0.0864399 </td> <td style="text-align:right;"> 0.3142208 </td> <td style="text-align:right;"> 0.1125613 </td> <td style="text-align:right;"> 0.5158803 </td> <td style="text-align:right;"> 0.1028894 </td> <td style="text-align:right;"> 0.2027583 </td> <td style="text-align:right;"> 0.1750477 </td> <td style="text-align:right;"> 0.2304688 </td> <td style="text-align:right;"> 0.0141383 </td> </tr> <tr> <td style="text-align:left;"> total food intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1192293 </td> <td style="text-align:right;"> -0.2542902 </td> <td style="text-align:right;"> 0.0158316 </td> <td style="text-align:right;"> 0.0689099 </td> <td style="text-align:right;"> -0.0964842 </td> <td style="text-align:right;"> -0.2564912 </td> <td style="text-align:right;"> 0.0635228 </td> <td style="text-align:right;"> 0.0816377 </td> <td style="text-align:right;"> 0.0267691 </td> <td style="text-align:right;"> -0.0233285 </td> <td style="text-align:right;"> 0.0768667 </td> <td style="text-align:right;"> 0.0255605 </td> </tr> <tr> <td style="text-align:left;"> total protein </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0422347 </td> <td style="text-align:right;"> -0.0623878 </td> <td style="text-align:right;"> -0.0220816 </td> <td style="text-align:right;"> 0.0102824 </td> <td style="text-align:right;"> -0.0355909 </td> <td style="text-align:right;"> -0.0619127 </td> <td style="text-align:right;"> -0.0092692 </td> <td style="text-align:right;"> 0.0134297 </td> <td style="text-align:right;"> 0.0092660 </td> <td style="text-align:right;"> -0.0008158 </td> <td style="text-align:right;"> 0.0193478 </td> <td style="text-align:right;"> 0.0051439 </td> </tr> <tr> <td style="text-align:left;"> total water intake </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Indirect Calorimetry </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:right;"> -0.1457383 </td> <td style="text-align:right;"> -0.2373165 </td> <td style="text-align:right;"> -0.0541601 </td> <td style="text-align:right;"> 0.0467244 </td> <td style="text-align:right;"> -0.2097443 </td> <td style="text-align:right;"> -0.2681948 </td> <td style="text-align:right;"> -0.1512937 </td> <td style="text-align:right;"> 0.0298223 </td> <td style="text-align:right;"> -0.0654284 </td> <td style="text-align:right;"> -0.1374220 </td> <td style="text-align:right;"> 0.0065653 </td> <td style="text-align:right;"> 0.0367321 </td> </tr> <tr> <td style="text-align:left;"> triglycerides </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.0320020 </td> <td style="text-align:right;"> -0.1233659 </td> <td style="text-align:right;"> 0.0593619 </td> <td style="text-align:right;"> 0.0466151 </td> <td style="text-align:right;"> 0.3268957 </td> <td style="text-align:right;"> 0.2087111 </td> <td style="text-align:right;"> 0.4450803 </td> <td style="text-align:right;"> 0.0602994 </td> <td style="text-align:right;"> 0.3473552 </td> <td style="text-align:right;"> 0.2592006 </td> <td style="text-align:right;"> 0.4355098 </td> <td style="text-align:right;"> 0.0449777 </td> </tr> <tr> <td style="text-align:left;"> urea (blood urea nitrogen - bun) </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> -0.1405306 </td> <td style="text-align:right;"> -0.2664120 </td> <td style="text-align:right;"> -0.0146491 </td> <td style="text-align:right;"> 0.0642264 </td> <td style="text-align:right;"> -0.0950040 </td> <td style="text-align:right;"> -0.2507897 </td> <td style="text-align:right;"> 0.0607817 </td> <td style="text-align:right;"> 0.0794840 </td> <td style="text-align:right;"> 0.0403162 </td> <td style="text-align:right;"> 0.0051883 </td> <td style="text-align:right;"> 0.0754441 </td> <td style="text-align:right;"> 0.0179227 </td> </tr> <tr> <td style="text-align:left;"> uric acid </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Clinical Chemistry </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:right;"> 0.0367062 </td> <td style="text-align:right;"> -0.0660619 </td> <td style="text-align:right;"> 0.1394744 </td> <td style="text-align:right;"> 0.0524337 </td> <td style="text-align:right;"> 0.3626957 </td> <td style="text-align:right;"> 0.0914512 </td> <td style="text-align:right;"> 0.6339402 </td> <td style="text-align:right;"> 0.1383926 </td> <td style="text-align:right;"> 0.4472349 </td> <td style="text-align:right;"> -0.0801891 </td> <td style="text-align:right;"> 0.9746588 </td> <td style="text-align:right;"> 0.2690988 </td> </tr> <tr> <td style="text-align:left;"> white blood cell count </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:right;"> -0.0907957 </td> <td style="text-align:right;"> -0.1703063 </td> <td style="text-align:right;"> -0.0112852 </td> <td style="text-align:right;"> 0.0405673 </td> <td style="text-align:right;"> 0.1168446 </td> <td style="text-align:right;"> -0.0023934 </td> <td style="text-align:right;"> 0.2360826 </td> <td style="text-align:right;"> 0.0608368 </td> <td style="text-align:right;"> 0.1978876 </td> <td style="text-align:right;"> 0.1368305 </td> <td style="text-align:right;"> 0.2589447 </td> <td style="text-align:right;"> 0.0311521 </td> </tr> <tr> <td style="text-align:left;"> whole arena average speed </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0156634 </td> <td style="text-align:right;"> -0.0857564 </td> <td style="text-align:right;"> 0.0544296 </td> <td style="text-align:right;"> 0.0357624 </td> <td style="text-align:right;"> -0.1140149 </td> <td style="text-align:right;"> -0.1840029 </td> <td style="text-align:right;"> -0.0440269 </td> <td style="text-align:right;"> 0.0357088 </td> <td style="text-align:right;"> -0.0997437 </td> <td style="text-align:right;"> -0.1519566 </td> <td style="text-align:right;"> -0.0475307 </td> <td style="text-align:right;"> 0.0266397 </td> </tr> <tr> <td style="text-align:left;"> whole arena resting time </td> <td style="text-align:right;"> 1 </td> <td style="text-align:left;"> Open Field </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:right;"> -0.0531307 </td> <td style="text-align:right;"> -0.1011672 </td> <td style="text-align:right;"> -0.0050941 </td> <td style="text-align:right;"> 0.0245089 </td> <td style="text-align:right;"> -0.0593672 </td> <td style="text-align:right;"> -0.1076067 </td> <td style="text-align:right;"> -0.0111276 </td> <td style="text-align:right;"> 0.0246125 </td> <td style="text-align:right;"> 0.0045878 </td> <td style="text-align:right;"> -0.0513396 </td> <td style="text-align:right;"> 0.0605152 </td> <td style="text-align:right;"> 0.0285349 </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jIHRyYWl0X21ldGFfcmVzdWx0cyA8LSB3cml0ZS5jc3YobWV0YWNvbWJvLCBmaWxlID0gXCJleHBvcnQvdHJhaXRfbWV0YV9yZXN1bHRzLmNzdlwiKVxuYGBgIn0= --></p> <pre class="r"><code> # trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv")</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="second-order-meta-analysis-for-functional-groups" class="section level2"> <h2>3. Second-order meta analysis for functional groups</h2> <p>(Section H in Figure 3 in main article)</p> <div id="performing-meta-analyses-3-for-each-of-the-9-grouping-terms-lncvr-lnvr-lnrr" class="section level3"> <h3>Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR)</h3> <div id="preparation-of-data" class="section level4"> <h4>Preparation of data</h4> <p>Nesting, calculating the number of parameters within each grouping term, and running the meta-analysis</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metacombo_final <- metacombo %>% group_by(GroupingTerm) %>% nest_legacy() # we're using 'nest_legacy' to keep old syntax/functionality # **calculate number of parameters per grouping term metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow)) # For all grouping terms metacombo_final_all <- metacombo %>% nest_legacy() #'nest_legacy' to keep old syntax/functionality # **Final fixed effects meta-analyses within grouping terms, with SE of the estimate overall1 <- metacombo_final %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) ) # **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate overall_all1 <- metacombo_final_all %>% mutate( model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )), model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F )) )</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="re-structuring-the-data-for-each-grouping-term" class="section level3"> <h3>Re-structuring the data for each grouping term</h3> <p>We here delete unused variables, and select the respective effect sizes. Please note - the referencing of the cells does NOT depend on previous ordering of the data. This would only be affected if the output structure from metafor::rma.uni changes.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Behaviour <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Behaviour") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Immunology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Immunology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Hematology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hematology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Hearing <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hearing") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Physiology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Physiology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Metabolism <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Metabolism") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Morphology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Morphology") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Heart <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Heart") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] Eye <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Eye") %>% mutate( lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se, lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se, lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se ))[, c(1, 7:18)] All <- as.data.frame(overall_all1 %>% mutate( lnCVR = .[[2]][[1]]$b, lnCVR_lower = .[[2]][[1]]$ci.lb, lnCVR_upper = .[[2]][[1]]$ci.ub, lnCVR_se = .[[2]][[1]]$se, lnVR = .[[3]][[1]]$b, lnVR_lower = .[[3]][[1]]$ci.lb, lnVR_upper = .[[3]][[1]]$ci.ub, lnVR_se = .[[3]][[1]]$se, lnRR = .[[4]][[1]]$b, lnRR_lower = .[[4]][[1]]$ci.lb, lnRR_upper = .[[4]][[1]]$ci.ub, lnRR_se = .[[4]][[1]]$se ))[, c(5:16)] All$lnCVR <- as.numeric(All$lnCVR) All$lnVR <- as.numeric(All$lnVR) All$lnRR <- as.numeric(All$lnRR) All <- All %>% mutate(GroupingTerm = "All") overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All) #FZ: warnings are ok</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="visualisation" class="section level1"> <h1>Visualisation</h1> <div id="figure-4" class="section level2"> <h2>Figure 4</h2> <div id="preparation-for-plots-count-data-based-on-first-order-meta-analysis-results" class="section level4"> <h4>Preparation for plots: Count data, based on First-order meta analysis results</h4> <p>This includes all separate eligible traits. Re-ordering of grouping terms</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code> meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye")) meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm))) # *Preparing data for all traits meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) # lnVR has been removed here and in the steps below, as this is only included in the supplemental figure meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels = c("lnCVR", "lnRR")) meta.plot2.all.c <- meta.plot2.all.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.all.c$label <- "All traits" # restructure to create stacked bar plots meta.plot2.all.d <- as.data.frame(meta.plot2.all.c) meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias)) # add summary row ('All') and re-arrange rows into correct order for plotting #FZ added meta.plot2.all.f <- meta.plot2.all.e %>% group_by(trait, sex) %>% summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, label = "All traits", samplesize = sum(samplesize)) %>% mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>% bind_rows(meta.plot2.all.e, .) %>% mutate(rownumber = row_number()) %>% .[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ]</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBmYWN0b3IgYW5kIGNoYXJhY3RlciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvcmJpbmRpbmcgY2hhcmFjdGVyIGFuZCBmYWN0b3IgdmVjdG9yLCBjb2VyY2luZyBpbnRvIGNoYXJhY3RlciB2ZWN0b3JcbiJ9 --> <pre><code>binding factor and character vector, coercing into character vectorbinding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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--> <pre class="r"><code>meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, rev(levels(meta.plot2.all.f$GroupingTerm))) malebias_Fig2_alltraits <- ggplot(meta.plot2.all.f) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.all.f, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_alltraits #(panel A in Figure 4 in ms)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="overall-results-of-second-order-meta-analysis-figure-4-panel-b" class="section level3"> <h3>Overall results of second order meta analysis (Figure 4, Panel B)</h3> <div id="restructure-data-for-plotting" class="section level4"> <h4>Restructure data for plotting</h4> <p>Data are restructured, and grouping terms are being re-ordered</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>overall3 <- gather(overall2, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3 %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3 %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3 %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4 <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, # re-order Grouping Terms overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm))) overall4$label <- "All traits" kable(cbind(overall4, overall4)) %>% kable_styling() %>% scroll_box(width = "100%", height = "200px")</code></pre> <!-- 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--> <div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:200px; overflow-x: scroll; width:100%; "><table class="table" style="margin-left: auto; margin-right: auto;"> <thead> <tr> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> GroupingTerm </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> parameter </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> value </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.low </th> <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> ci.high </th> <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> label </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0035049 </td> <td style="text-align:right;"> -0.0240688 </td> <td style="text-align:right;"> 0.0170591 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0774453 </td> <td style="text-align:right;"> 0.0414171 </td> <td style="text-align:right;"> 0.1134734 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0430831 </td> <td style="text-align:right;"> -0.1125945 </td> <td style="text-align:right;"> 0.0264283 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0126792 </td> <td style="text-align:right;"> -0.0140094 </td> <td style="text-align:right;"> 0.0393678 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0681817 </td> <td style="text-align:right;"> -0.0980135 </td> <td style="text-align:right;"> -0.0383499 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0217865 </td> <td style="text-align:right;"> -0.0165045 </td> <td style="text-align:right;"> 0.0600776 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0183839 </td> <td style="text-align:right;"> -0.0128375 </td> <td style="text-align:right;"> 0.0496053 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0157302 </td> <td style="text-align:right;"> -0.0111999 </td> <td style="text-align:right;"> 0.0426603 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> -0.0817932 </td> <td style="text-align:right;"> -0.1476821 </td> <td style="text-align:right;"> -0.0159043 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnCVR </td> <td style="text-align:right;"> 0.0046553 </td> <td style="text-align:right;"> -0.0086242 </td> <td style="text-align:right;"> 0.0179348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Behaviour </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0199206 </td> <td style="text-align:right;"> -0.0634388 </td> <td style="text-align:right;"> 0.0235976 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Morphology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0678160 </td> <td style="text-align:right;"> 0.0072225 </td> <td style="text-align:right;"> 0.1284095 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Metabolism </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.1422577 </td> <td style="text-align:right;"> 0.0364352 </td> <td style="text-align:right;"> 0.2480801 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Physiology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0163695 </td> <td style="text-align:right;"> -0.0443364 </td> <td style="text-align:right;"> 0.0770753 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Immunology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0574840 </td> <td style="text-align:right;"> -0.1074213 </td> <td style="text-align:right;"> -0.0075466 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hematology </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0388537 </td> <td style="text-align:right;"> -0.0024274 </td> <td style="text-align:right;"> 0.0801348 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Heart </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0048933 </td> <td style="text-align:right;"> -0.0324240 </td> <td style="text-align:right;"> 0.0226374 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Hearing </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> -0.0132366 </td> <td style="text-align:right;"> -0.0335982 </td> <td style="text-align:right;"> 0.0071251 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> Eye </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0091186 </td> <td style="text-align:right;"> 0.0012071 </td> <td style="text-align:right;"> 0.0170302 </td> <td style="text-align:left;"> All traits </td> </tr> <tr> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> <td style="text-align:left;"> All </td> <td style="text-align:left;"> lnRR </td> <td style="text-align:right;"> 0.0124332 </td> <td style="text-align:right;"> -0.0061474 </td> <td style="text-align:right;"> 0.0310138 </td> <td style="text-align:left;"> All traits </td> </tr> </tbody> </table></div> <!-- rnb-htmlwidget-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_Fig3_alltraits <- overall4 %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_Fig3_alltraits</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="fig-4" class="section level3"> <h3>Fig 4</h3> <p>Join the different parts and #TO DO!! add M / F symbols in Metameta_Fig3_alltraits</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI1Rlc3RcbiNtYWxlIDwtIHJlYWRQTkcoc3lzdGVtLmZpbGUoXCJpbWdcIiwgXCJtYWxlXCIpKVxuI3Rlc3QgPC0gTWV0YW1ldGFfRmlnM19hbGx0cmFpdHMgXG5cbiNsaWJyYXJ5KHBuZylcbmBgYCJ9 --> <pre class="r"><code>#Test #male <- readPNG(system.file("img", "male")) #test <- Metameta_Fig3_alltraits #library(png)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnNCA8LSBnZ2FycmFuZ2UobWFsZWJpYXNfRmlnMl9hbGx0cmFpdHMsIE1ldGFtZXRhX0ZpZzNfYWxsdHJhaXRzLCAgbnJvdyA9IDIsIGFsaWduID0gXCJ2XCIsIGhlaWdodHMgPSBjKDEsIDEpLCBsYWJlbHMgPSBjKFwiQVwiLCBcIkJcIikpXG5GaWc0XG5cbmBgYCJ9 --> <pre class="r"><code>Fig4 <- ggarrange(malebias_Fig2_alltraits, Metameta_Fig3_alltraits, nrow = 2, align = "v", heights = c(1, 1), labels = c("A", "B")) Fig4 </code></pre> <!-- rnb-source-end --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MDkuMTQ3MSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjY2Mn0= --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="figure-4-1" class="section level3"> <h3>Figure 4:</h3> <p>Panel A shows the numbers of traits across functional groups that are either male-biased (blue-green) or female-biased (orange-red), as calculated in Step D (figure 3). Panel B shows effect sizes and 95% CI from separate meta-analysis for each functional group (step H in Figure 3). Both panels represent results evaluated across all traits (Phase 3, Figure 3). Traits that are male biased are Male data is shown in blue, whereas female bias data is represented in orange.</p> </div> </div> <div id="figure-5" class="section level2"> <h2>Figure 5</h2> <div id="preparing-data-for-traits-with-ci-not-overlapping-0" class="section level4"> <h4>Preparing data for traits with CI not overlapping 0</h4> <p>To further investigate sex bias in this dataset, and in particular if the extent of sex bias differs between traits, we investigate the magnitude of male- and female bias in significantly different traits on (both for means and variability)</p> <p>To do this, we select only traits that have CIs that do not overlap with zero. ### FELIX: “ALL” missing. This figure is panel A in Fig 5</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> meta.plot2.sig <- meta_clean %>% mutate( lnCVRsig = ifelse(lnCVR_lower * lnCVR_upper > 0, 1, 0), lnVRsig = ifelse(lnVR_lower * lnVR_upper > 0, 1, 0), lnRRsig = ifelse(lnRR_lower * lnRR_upper > 0, 1, 0) ) meta.plot2.sig.b <- meta.plot2.sig[, c("lnCVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] # "lnVR", meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR) meta.plot2.sig.c$sig <- "placeholder" meta.plot2.sig.c$trait <- factor(meta.plot2.sig.c$trait, levels = c("lnCVR", "lnRR")) # "lnVR", meta.plot2.sig.c$sig <- ifelse(meta.plot2.sig.c$trait == "lnCVR", meta.plot2.sig.c$lnCVRsig, ifelse(meta.plot2.sig.c$trait == "lnVR", meta.plot2.sig.c$lnVRsig, meta.plot2.sig.c$lnRRsig) ) # choosing sex biased ln-ratios significantly larger than 0 meta.plot2.sig.malebias <- meta.plot2.sig.c %>% group_by_at(vars(trait, GroupingTerm)) %>% filter(sig == 1) %>% summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig) meta.plot2.sig.malebias <- ungroup(meta.plot2.sig.malebias) %>% add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros) mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total) meta.plot2.sig.malebias$label <- "CI not overlapping zero" # restructure to create stacked bar plots meta.plot2.sig.bothsexes <- as.data.frame(meta.plot2.sig.malebias) meta.plot2.sig.bothsexes.b <- gather(meta.plot2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.sig.bothsexes.b$samplesize <- with(meta.plot2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig)) # Plot Fig2 all significant results (CI not overlapping zero): # Several grouing terms are added post-hoc (with no data to display): no significant lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no significant male-biased lnVR for 'Eye'. malebias_Fig2_sigtraits <- ggplot(meta.plot2.sig.bothsexes.b) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.sig.bothsexes.b, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip()</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="preparation-for-plots-on-significant-sex-bias-second-order-meta-analysis-results" class="section level3"> <h3>Preparation for Plots on significant sex-bias (Second-order meta analysis results</h3> <div id="figure-5-b---traits-with-ci-not-overlapping-0" class="section level4"> <h4>Figure 5 B - traits with CI not overlapping 0</h4> <p>Prepare data create column with 1= different from zero, 0= zero included in CI #### Male-biased (significant) traits</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta.male.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_lower > 0, 1, 0), sigVR = ifelse(lnVR_lower > 0, 1, 0), sigRR = ifelse(lnRR_lower > 0, 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>% filter(sigCVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuVlJcbm1ldGFjb21ib19tYWxlLnBsb3QzLlZSIDwtIG1ldGEubWFsZS5wbG90My5zaWcgJT4lXG4gIGZpbHRlcihzaWdWUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19tYWxlLnBsb3QzLlZSLmFsbCA8LSBtZXRhLm1hbGUucGxvdDMuc2lnICU+JVxuICBmaWx0ZXIoc2lnVlIgPT0gMSkgJT4lXG4gIG5lc3QoKVxuYGBgIn0= --> <pre class="r"><code># Significant subset for lnVR metacombo_male.plot3.VR <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>% filter(sigVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuUlJcbm1ldGFjb21ib19tYWxlLnBsb3QzLlJSIDwtIG1ldGEubWFsZS5wbG90My5zaWcgJT4lXG4gIGZpbHRlcihzaWdSUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19tYWxlLnBsb3QzLlJSLmFsbCA8LSBtZXRhLm1hbGUucGxvdDMuc2lnICU+JVxuICBmaWx0ZXIoc2lnUlIgPT0gMSkgJT4lXG4gIG5lc3QoKVxuYGBgIn0= --> <pre class="r"><code># Significant subset for lnRR metacombo_male.plot3.RR <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>% filter(sigRR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR <- metacombo_male.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR <- metacombo_male.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlZSIDwtIGJpbmRfcm93cyhwbG90My5tYWxlLm1ldGEuVlIsIHBsb3QzLm1hbGUubWV0YS5WUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlJSIDwtIGJpbmRfcm93cyhwbG90My5tYWxlLm1ldGEuUlIsIHBsb3QzLm1hbGUubWV0YS5SUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.b)) plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBjaGFyYWN0ZXIgYW5kIGZhY3RvciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvclxuIn0= --> <pre><code>binding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm), ] plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm), ] plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm), ] overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5tYWxlLnBsb3QzIDwtIGZ1bGxfam9pbihvdmVyYWxsLm1hbGUucGxvdDMsIHBsb3QzLm1hbGUubWV0YS5SUi5iKVxuYGBgIn0= --> <pre class="r"><code>overall.male.plot3 <- full_join(overall.male.plot3, plot3.male.meta.RR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) # add missing GroupingTerms for plot overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Behaviour") overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Immunology") overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Eye") overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm))) # str(overall.male.plot3)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3.male.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.male.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4.male.sig$label <- "CI not overlapping zero"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig5b all significant results (CI not overlapping zero) for males</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> Metameta_Fig3_male.sig <- overall4.male.sig %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(0, 0.4), breaks = c(0, 0.3), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_Fig3_male.sig</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="female-part-significant-traits" class="section level4"> <h4>Female part, significant traits</h4> <p>Female Fig5B sig</p> <p>Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> # female-biased traits meta.female.plot3.sig <- metacombo %>% mutate( sigCVR = ifelse(lnCVR_upper < 0, 1, 0), sigVR = ifelse(lnVR_upper < 0, 1, 0), sigRR = ifelse(lnRR_upper < 0, 1, 0) ) # Significant subset for lnCVR metacombo_female.plot3.CVR <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>% filter(sigCVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuVlJcblxubWV0YWNvbWJvX2ZlbWFsZS5wbG90My5WUiA8LSBtZXRhLmZlbWFsZS5wbG90My5zaWcgJT4lXG4gIGZpbHRlcihzaWdWUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19mZW1hbGUucGxvdDMuVlIuYWxsIDwtIG1ldGEuZmVtYWxlLnBsb3QzLnNpZyAlPiVcbiAgZmlsdGVyKHNpZ1ZSID09IDEpICU+JVxuICBuZXN0KClcbmBgYCJ9 --> <pre class="r"><code># Significant subset for lnVR metacombo_female.plot3.VR <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>% filter(sigVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuUlJcblxubWV0YWNvbWJvX2ZlbWFsZS5wbG90My5SUiA8LSBtZXRhLmZlbWFsZS5wbG90My5zaWcgJT4lXG4gIGZpbHRlcihzaWdSUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19mZW1hbGUucGxvdDMuUlIuYWxsIDwtIG1ldGEuZmVtYWxlLnBsb3QzLnNpZyAlPiVcbiAgZmlsdGVyKHNpZ1JSID09IDEpICU+JVxuICBuZXN0KClcbmBgYCJ9 --> <pre class="r"><code># Significant subset for lnRR metacombo_female.plot3.RR <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>% filter(sigRR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR <- metacombo_female.plot3.VR %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR <- metacombo_female.plot3.RR %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = "All") plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMuZmVtYWxlLm1ldGEuVlIgPC0gYmluZF9yb3dzKHBsb3QzLmZlbWFsZS5tZXRhLlZSLCBwbG90My5mZW1hbGUubWV0YS5WUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMuZmVtYWxlLm1ldGEuUlIgPC0gYmluZF9yb3dzKHBsb3QzLmZlbWFsZS5tZXRhLlJSLCBwbG90My5mZW1hbGUubWV0YS5SUi5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.female.meta.CVR.b)) plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYmluZGluZyBjaGFyYWN0ZXIgYW5kIGZhY3RvciB2ZWN0b3IsIGNvZXJjaW5nIGludG8gY2hhcmFjdGVyIHZlY3RvclxuIn0= --> <pre><code>binding character and factor vector, coercing into character vector</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMuZmVtYWxlLm1ldGEuQ1ZSLmIgPC0gcGxvdDMuZmVtYWxlLm1ldGEuQ1ZSLmJbb3JkZXIocGxvdDMuZmVtYWxlLm1ldGEuQ1ZSLmIkR3JvdXBpbmdUZXJtKSwgXVxuXG5wbG90My5mZW1hbGUubWV0YS5WUi5iIDwtIGFzLmRhdGEuZnJhbWUocGxvdDMuZmVtYWxlLm1ldGEuVlIgJT4lIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG11dGF0ZShcbiAgICBsblZSID0gbWFwX2RibChtb2RlbF9sblZSLCBwbHVjaygyKSksIGxuVlJfbG93ZXIgPSBtYXBfZGJsKG1vZGVsX2xuVlIsIHBsdWNrKDYpKSxcbiAgICBsblZSX3VwcGVyID0gbWFwX2RibChtb2RlbF9sblZSLCBwbHVjayg3KSksIGxuVlJfc2UgPSBtYXBfZGJsKG1vZGVsX2xuVlIsIHBsdWNrKDMpKVxuICApKVssIGMoMSwgNDo3KV1cblxucGxvdDMuZmVtYWxlLm1ldGEuVlIuYiA8LSBwbG90My5mZW1hbGUubWV0YS5WUi5iW29yZGVyKHBsb3QzLmZlbWFsZS5tZXRhLlZSLmIkR3JvdXBpbmdUZXJtKSwgXVxuXG5wbG90My5mZW1hbGUubWV0YS5SUi5iIDwtIGFzLmRhdGEuZnJhbWUocGxvdDMuZmVtYWxlLm1ldGEuUlIgJT4lIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG11dGF0ZShcbiAgICBsblJSID0gbWFwX2RibChtb2RlbF9sblJSLCBwbHVjaygyKSksIGxuUlJfbG93ZXIgPSBtYXBfZGJsKG1vZGVsX2xuUlIsIHBsdWNrKDYpKSxcbiAgICBsblJSX3VwcGVyID0gbWFwX2RibChtb2RlbF9sblJSLCBwbHVjayg3KSksIGxuUlJfc2UgPSBtYXBfZGJsKG1vZGVsX2xuUlIsIHBsdWNrKDMpKVxuICApKVssIGMoMSwgNDo3KV1cblxucGxvdDMuZmVtYWxlLm1ldGEuUlIuYiA8LSBwbG90My5mZW1hbGUubWV0YS5SUi5iW29yZGVyKHBsb3QzLmZlbWFsZS5tZXRhLlJSLmIkR3JvdXBpbmdUZXJtKSwgXVxuXG5vdmVyYWxsLmZlbWFsZS5wbG90MyA8LSBmdWxsX2pvaW4ocGxvdDMuZmVtYWxlLm1ldGEuQ1ZSLmIsIHBsb3QzLmZlbWFsZS5tZXRhLlZSLmIpXG5gYGAifQ== --> <pre class="r"><code>plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm), ] plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm), ] plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm), ] overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5mZW1hbGUucGxvdDMgPC0gZnVsbF9qb2luKG92ZXJhbGwuZmVtYWxlLnBsb3QzLCBwbG90My5mZW1hbGUubWV0YS5SUi5iKVxuYGBgIn0= --> <pre class="r"><code>overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm)))</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR, lnCVR.ci <- overall3.female.sig %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) # lnVR.ci <- overall3.female.sig %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.female.sig %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.female.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci, overall4.female.sig$label <- "CI not overlapping zero"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plotting Fig5B all significant results (CI not overlapping zero, female )</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> Metameta_Fig3_female.sig <- overall4.female.sig %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "salmon1", color = "salmon1", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.4, 0), breaks = c(-0.3, 0), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), # rows = vars(label), # labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) # Metameta_Fig3_female.sig #(Figure 5B left panel)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="join-code-missing" class="section level2"> <h2>JOIN!!! CODE MISSING??</h2> </div> </div> <div id="supplemental-plots" class="section level1"> <h1>Supplemental Plots</h1> <div id="figure-s1" class="section level2"> <h2>Figure S1</h2> <div id="including-lnvr" class="section level3"> <h3>Including lnVR</h3> </div> <div id="count-data-including-lnvr-fig-s1-panel-a" class="section level3"> <h3>Count data, including lnVR (Fig S1 panel A)</h3> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code># *Prepare data for all traits meta.plot2.all <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.all.bS1 <- gather(meta.plot2.all, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.all.bS1$trait <- factor(meta.plot2.all.bS1$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.all.cS1 <- meta.plot2.all.bS1 %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.all.cS1$label <- "All traits" # restructure to create stacked bar plots meta.plot2.all.dS1 <- as.data.frame(meta.plot2.all.cS1) meta.plot2.all.eS1 <- gather(meta.plot2.all.dS1, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.all.eS1$samplesize <- with(meta.plot2.all.eS1, ifelse(sex == "malepercent", malebias, femalebias)) malebias_FigS1_alltraits <- ggplot(meta.plot2.all.eS1) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.all.eS1, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS1_alltraits #(panel A in Figure S1)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="overall-results-of-second-order-meta-analysis-including-vr" class="section level3"> <h3>Overall results of second order meta analysis, INCLUDING VR</h3> <div id="restructure-data-for-plotting-1" class="section level4"> <h4>Restructure data for plotting</h4> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbDMubWFsZS5zaWdTIDwtIGdhdGhlcihvdmVyYWxsLm1hbGUucGxvdDMsIHBhcmFtZXRlciwgdmFsdWUsIGMobG5DVlIsIGxuVlIsIGxuUlIpLCBmYWN0b3Jfa2V5ID0gVFJVRSlcblxubG5DVlIuY2kgPC0gb3ZlcmFsbDMubWFsZS5zaWdTICU+JVxuICBmaWx0ZXIocGFyYW1ldGVyID09IFwibG5DVlJcIikgJT4lXG4gIG11dGF0ZShjaS5sb3cgPSBsbkNWUl9sb3dlciwgY2kuaGlnaCA9IGxuQ1ZSX3VwcGVyKVxubG5WUi5jaSA8LSBvdmVyYWxsMy5tYWxlLnNpZ1MgJT4lXG4gIGZpbHRlcihwYXJhbWV0ZXIgPT0gXCJsblZSXCIpICU+JVxuICBtdXRhdGUoY2kubG93ID0gbG5WUl9sb3dlciwgY2kuaGlnaCA9IGxuVlJfdXBwZXIpXG5sblJSLmNpIDwtIG92ZXJhbGwzLm1hbGUuc2lnUyAlPiVcbiAgZmlsdGVyKHBhcmFtZXRlciA9PSBcImxuUlJcIikgJT4lXG4gIG11dGF0ZShjaS5sb3cgPSBsblJSX2xvd2VyLCBjaS5oaWdoID0gbG5SUl91cHBlcilcblxub3ZlcmFsbDQubWFsZS5zaWdTIDwtIGJpbmRfcm93cyhsbkNWUi5jaSwgbG5WUi5jaSwgbG5SUi5jaSkgJT4lIHNlbGVjdChHcm91cGluZ1Rlcm0sIHBhcmFtZXRlciwgdmFsdWUsIGNpLmxvdywgY2kuaGlnaClcblxub3ZlcmFsbDQubWFsZS5zaWdTJGxhYmVsIDwtIFwiQ0kgbm90IG92ZXJsYXBwaW5nIHplcm9cIlxuXG4jIERhdGEgYXJlIHJlc3RydWN0dXJlZCwgYW5kIGdyb3VwaW5nIHRlcm1zIGFyZSBiZWluZyByZS1vcmRlcmVkXG5cbm92ZXJhbGwzUyA8LSBnYXRoZXIob3ZlcmFsbDIsIHBhcmFtZXRlciwgdmFsdWUsIGMobG5DVlIsIGxuVlIsIGxuUlIpLCBmYWN0b3Jfa2V5ID0gVFJVRSlcblxubG5DVlIuY2kgPC0gb3ZlcmFsbDNTICU+JVxuICBmaWx0ZXIocGFyYW1ldGVyID09IFwibG5DVlJcIikgJT4lXG4gIG11dGF0ZShjaS5sb3cgPSBsbkNWUl9sb3dlciwgY2kuaGlnaCA9IGxuQ1ZSX3VwcGVyKVxubG5WUi5jaSA8LSBvdmVyYWxsM1MgJT4lXG4gIGZpbHRlcihwYXJhbWV0ZXIgPT0gXCJsblZSXCIpICU+JVxuICBtdXRhdGUoY2kubG93ID0gbG5WUl9sb3dlciwgY2kuaGlnaCA9IGxuVlJfdXBwZXIpXG5sblJSLmNpIDwtIG92ZXJhbGwzUyAlPiVcbiAgZmlsdGVyKHBhcmFtZXRlciA9PSBcImxuUlJcIikgJT4lXG4gIG11dGF0ZShjaS5sb3cgPSBsblJSX2xvd2VyLCBjaS5oaWdoID0gbG5SUl91cHBlcilcblxub3ZlcmFsbDRTIDwtIGJpbmRfcm93cyhsbkNWUi5jaSwgbG5WUi5jaSwgbG5SUi5jaSkgJT4lIHNlbGVjdChHcm91cGluZ1Rlcm0sIHBhcmFtZXRlciwgdmFsdWUsIGNpLmxvdywgY2kuaGlnaClcblxuIyByZS1vcmRlciBHcm91cGluZyBUZXJtc1xuXG5vdmVyYWxsNFMkR3JvdXBpbmdUZXJtIDwtIGZhY3RvcihvdmVyYWxsNFMkR3JvdXBpbmdUZXJtLCBsZXZlbHMgPSBjKFwiQmVoYXZpb3VyXCIsIFwiTW9ycGhvbG9neVwiLCBcIk1ldGFib2xpc21cIiwgXCJQaHlzaW9sb2d5XCIsIFwiSW1tdW5vbG9neVwiLCBcIkhlbWF0b2xvZ3lcIiwgXCJIZWFydFwiLCBcIkhlYXJpbmdcIiwgXCJFeWVcIiwgXCJBbGxcIikpXG5vdmVyYWxsNFMkR3JvdXBpbmdUZXJtIDwtIGZhY3RvcihvdmVyYWxsNFMkR3JvdXBpbmdUZXJtLCByZXYobGV2ZWxzKG92ZXJhbGw0UyRHcm91cGluZ1Rlcm0pKSlcbm92ZXJhbGw0UyRsYWJlbCA8LSBcIkFsbCB0cmFpdHNcIlxuYGBgIn0= --> <pre class="r"><code>overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.male.sigS %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.male.sigS %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.male.sigS %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.sigS$label <- "CI not overlapping zero" # Data are restructured, and grouping terms are being re-ordered overall3S <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3S %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3S %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3S %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4S <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # re-order Grouping Terms overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, rev(levels(overall4S$GroupingTerm))) overall4S$label <- "All traits"</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="preparation-for-plot-including-lnvr" class="section level4"> <h4>Preparation for plot, including lnVR</h4> <p>Preparation: Sub-Plot for Figure S1: all traits (S1 B)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>Metameta_FigS1_alltraits <- overall4S %>% ggplot(aes(y = GroupingTerm, x = value)) + geom_errorbarh(aes( xmin = ci.low, xmax = ci.high ), height = 0.1, show.legend = FALSE ) + geom_point(aes(shape = parameter), fill = "black", color = "black", size = 2.2, show.legend = FALSE ) + scale_x_continuous( limits = c(-0.24, 0.25), breaks = c(-0.2, -0.1, 0, 0.1, 0.2), name = "Effect size" ) + geom_vline( xintercept = 0, color = "black", linetype = "dashed" ) + facet_grid( cols = vars(parameter), rows = vars(label), labeller = label_wrap_gen(width = 23), scales = "free", space = "free" ) + theme_bw() + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.title = element_blank(), axis.title.x = element_text(hjust = 0.5, size = 14), axis.title.y = element_blank() ) # Metameta_FigS1_alltraits</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="heterogeneity" class="section level3"> <h3>Heterogeneity</h3> <p>The analysis for heterogeneity follows the workflow of the above steps for the different meta-analyses. However, in the initial meta-analysis we extract sigma^2 and errors for mouse strains and centers (Institutions).</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcgPC0gYXMuZGF0YS5mcmFtZShjYmluZChjKDE6biksIG1hdHJpeChyZXAoMCwgbiAqIDMwKSwgbmNvbCA9IDMwKSkpXG5uYW1lcyhyZXN1bHRzLmFsbGhldGVyby5ncm91cGluZykgPC0gYyhcbiAgXCJpZFwiLCBcInNpZ21hMl9zdHJhaW4uQ1ZSXCIsIFwic2lnbWEyX2NlbnRlci5DVlJcIiwgXCJzaWdtYTJfZXJyb3IuQ1ZSXCIsIFwicy5ubGV2ZWxzLnN0cmFpbi5DVlJcIixcbiAgXCJzLm5sZXZlbHMuY2VudGVyLkNWUlwiLCBcInMubmxldmVscy5lcnJvci5DVlJcIiwgXCJzaWdtYTJfc3RyYWluLlZSXCIsIFwic2lnbWEyX2NlbnRlci5WUlwiLCBcInNpZ21hMl9lcnJvci5WUlwiLCBcInMubmxldmVscy5zdHJhaW4uVlJcIixcbiAgXCJzLm5sZXZlbHMuY2VudGVyLlZSXCIsIFwicy5ubGV2ZWxzLmVycm9yLlZSXCIsIFwic2lnbWEyX3N0cmFpbi5SUlwiLCBcInNpZ21hMl9jZW50ZXIuUlJcIiwgXCJzaWdtYTJfZXJyb3IuUlJcIiwgXCJzLm5sZXZlbHMuc3RyYWluLlJSXCIsXG4gIFwicy5ubGV2ZWxzLmNlbnRlci5SUlwiLCBcInMubmxldmVscy5lcnJvci5SUlwiLCBcImxuQ1ZSXCIsIFwibG5DVlJfbG93ZXJcIiwgXCJsbkNWUl91cHBlclwiLCBcImxuQ1ZSX3NlXCIsIFwibG5WUlwiLCBcImxuVlJfbG93ZXJcIiwgXCJsblZSX3VwcGVyXCIsXG4gIFwibG5WUl9zZVwiLCBcImxuUlJcIiwgXCJsblJSX2xvd2VyXCIsIFwibG5SUl91cHBlclwiLCBcImxuUlJfc2VcIlxuKVxuYGBgIn0= --> <pre class="r"><code>results.allhetero.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n * 30), ncol = 30))) names(results.allhetero.grouping) <- c( "id", "sigma2_strain.CVR", "sigma2_center.CVR", "sigma2_error.CVR", "s.nlevels.strain.CVR", "s.nlevels.center.CVR", "s.nlevels.error.CVR", "sigma2_strain.VR", "sigma2_center.VR", "sigma2_error.VR", "s.nlevels.strain.VR", "s.nlevels.center.VR", "s.nlevels.error.VR", "sigma2_strain.RR", "sigma2_center.RR", "sigma2_error.RR", "s.nlevels.strain.RR", "s.nlevels.center.RR", "s.nlevels.error.RR", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se" )</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>LOOP Parameters to extract from metafor (sigma2’s, s.nlevels)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> for (t in 1:n) { tryCatch( { data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100) population_stats <- calculate_population_stats(data_par_age) results <- create_meta_analysis_effect_sizes(population_stats) # lnCVR, logaritm of the ratio of male and female coefficients of variance cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 2] <- cvr.$sigma2[1] results.allhetero.grouping[t, 3] <- cvr.$sigma2[2] results.allhetero.grouping[t, 4] <- cvr.$sigma2[3] results.allhetero.grouping[t, 5] <- cvr.$s.nlevels[1] results.allhetero.grouping[t, 6] <- cvr.$s.nlevels[2] results.allhetero.grouping[t, 7] <- cvr.$s.nlevels[3] results.allhetero.grouping[t, 20] <- cvr.$b results.allhetero.grouping[t, 21] <- cvr.$ci.lb results.allhetero.grouping[t, 22] <- cvr.$ci.ub results.allhetero.grouping[t, 23] <- cvr.$se # lnVR, male to female variability ratio (logarithm of male and female standard deviations) vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 8] <- vr.$sigma2[1] results.allhetero.grouping[t, 9] <- vr.$sigma2[2] results.allhetero.grouping[t, 10] <- vr.$sigma2[3] results.allhetero.grouping[t, 11] <- vr.$s.nlevels[1] results.allhetero.grouping[t, 12] <- vr.$s.nlevels[2] results.allhetero.grouping[t, 13] <- vr.$s.nlevels[3] results.allhetero.grouping[t, 24] <- vr.$b results.allhetero.grouping[t, 25] <- vr.$ci.lb results.allhetero.grouping[t, 26] <- vr.$ci.ub results.allhetero.grouping[t, 27] <- vr.$se # lnRR, response ratio (logarithm of male and female means) rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list( ~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results) results.allhetero.grouping[t, 14] <- rr.$sigma2[1] results.allhetero.grouping[t, 15] <- rr.$sigma2[2] results.allhetero.grouping[t, 16] <- rr.$sigma2[3] results.allhetero.grouping[t, 17] <- rr.$s.nlevels[1] results.allhetero.grouping[t, 18] <- rr.$s.nlevels[2] results.allhetero.grouping[t, 19] <- rr.$s.nlevels[3] results.allhetero.grouping[t, 28] <- rr.$b results.allhetero.grouping[t, 29] <- rr.$ci.lb results.allhetero.grouping[t, 30] <- rr.$ci.ub results.allhetero.grouping[t, 31] <- rr.$se }, error = function(e) { cat("ERROR :", conditionMessage(e), "\n") } ) }</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBPcHRpbWl6ZXIgKG9wdGltKSBkaWQgbm90IGFjaGlldmUgY29udmVyZ2VuY2UgKGNvbnZlcmdlbmNlID0gMTApLiBcbiJ9 --> <pre><code>ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin 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 --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBPcHRpbWl6ZXIgKG9wdGltKSBkaWQgbm90IGFjaGlldmUgY29udmVyZ2VuY2UgKGNvbnZlcmdlbmNlID0gMTApLiBcbiJ9 --> <pre><code>ERROR : Optimizer (optim) did not achieve convergence (convergence = 10). </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5cbiJ9 --> <pre><code>Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiUm93cyB3aXRoIE5BcyBvbWl0dGVkIGZyb20gbW9kZWwgZml0dGluZy5Sb3dzIHdpdGggTkFzIG9taXR0ZWQgZnJvbSBtb2RlbCBmaXR0aW5nLlRoZXJlIGFyZSBvdXRjb21lcyB3aXRoIG5vbi1wb3NpdGl2ZSBzYW1wbGluZyB2YXJpYW5jZXMuJ1YnIGFwcGVhcnMgdG8gYmUgbm90IHBvc2l0aXZlIGRlZmluaXRlLlJvd3Mgd2l0aCBOQXMgb21pdHRlZCBmcm9tIG1vZGVsIGZpdHRpbmcuXG4ifQ== --> <pre><code>Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin 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 --> <pre><code>Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Single-level factor(s) found in 'random' argument. Corresponding 'sigma2' value(s) fixed to 0.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.There are outcomes with non-positive sampling variances.'V' appears to be not positive definite.Rows with NAs omitted from model fitting.</code></pre> <!-- rnb-output-end --> <!-- rnb-output-begin eyJkYXRhIjoiRVJST1IgOiBOQS9OYU4vSW5mIGluICd5JyBcbiJ9 --> <pre><code>ERROR : NA/NaN/Inf in 'y' </code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="exclude-traits-merge-datasets" class="section level4"> <h4>Exclude traits, merge datasets</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmVzdWx0cy5hbGxoZXRlcm8uZ3JvdXBpbmcyIDwtIHJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nW3Jlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nJHMubmxldmVscy5zdHJhaW4uVlIgIT0gMCwgXVxuIyBucm93KHJlc3VsdHMuYWxsaGV0ZXJvLmdyb3VwaW5nMikgIzIxOCAgU1ogMjIzPz8/XG5gYGAifQ== --> <pre class="r"><code>results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != 0, ] # nrow(results.allhetero.grouping2) #218 SZ 223???</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Merge data sets containing metafor results with procedure etc. names</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code># procedures <- read.csv(here("export", "procedures.csv")) results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)] results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)] results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)]</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="correlated-parameters" class="section level4"> <h4>Correlated parameters</h4> <p>##FELIX : check? numbers don’t add up??</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metahetero1 <- results.allhetero.grouping2 # length(unique(metahetero1$procedure)) #18 SZ 19 # length(unique(metahetero1$GroupingTerm)) #9 Sz ok # length(unique(metahetero1$parameter_group)) # 149 SZ 152 # length(unique(metahetero1$parameter_name)) #218 SZ 223 # Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) metahetero1b <- metahetero1 %>% group_by(parameter_group) %>% mutate(par_group_size = n_distinct(parameter_name)) metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)] # Create subsets with > 1 count (par_group_size > 1) metahetero1_sub <- subset(metahetero1, par_group_size > 1) # 90 observations # str(metahetero1_sub) # metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version # Nest data n_count. <- metahetero1_sub %>% group_by(parameter_group) %>% # mutate(raw_N = sum(sampleSize)) %>% #don't think is necessary: delete in final version nest() # meta-analysis preparation model_count. <- n_count. %>% mutate( model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2 )), model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2 )), model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2 )) ) # Robumeta object details: # str(model_count.$model_lnCVR[[1]]) ## *Perform meta-analyses on correlated sub-traits, using robumeta # Susi / FELIX: what's this below? # Shinichi: We think we want to use these for further analyses: # residual variance: as.numeric(robu_fit$mod_info$term1) (same as 'mod_info$tau.sq') # sample size: robu_fit$N ## **Extract and save parameter estimates # Felix: doesn't work , error message: #!!!!!!!!!!! ERROR!!!!!!!!!!!!!!!!!!!! #Error: Column `parameter_group` can't be modified because it's a grouping variable count_fun. <- function(mod_sub) { return(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N)) } robusub_RR. <- model_count. %>% transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun.)) %>% mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>% unnest(r) %>% select(-estimatelnRR) %>% purrr::set_names(c("parameter_group", "var.RR", "N.RR"))</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRXJyb3I6IENvbHVtbiBgcGFyYW1ldGVyX2dyb3VwYCBjYW4ndCBiZSBtb2RpZmllZCBiZWNhdXNlIGl0J3MgYSBncm91cGluZyB2YXJpYWJsZVxuIn0= --> <pre><code>Error: Column `parameter_group` can't be modified because it's a grouping variable</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])</p> <p>In this step, we<br /> 1) merge the N from robumeta and the N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR) 2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>metahetero_all <- metahetero1 %>% filter(par_group_size == 1) %>% as_tibble() metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR)) metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR)) metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR)) # str(metahetero_all) # str(robu_all.) metahetero_all <- metahetero_all %>% mutate( var.RR = if_else(var.RR == -Inf, -7, var.RR), var.VR = if_else(var.VR == -Inf, -5, var.VR), var.CVR = if_else(var.CVR == -Inf, -6, var.CVR) ) # **Combine data ## Step1 combinedmetahetero <- bind_rows(robu_all., metahetero_all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRXJyb3IgaW4gZG90c192YWx1ZXMoLi4uKSA6IG9iamVjdCAncm9idV9hbGwuJyBub3QgZm91bmRcbiJ9 --> <pre><code>Error in dots_values(...) : object 'robu_all.' not found</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="meta-analysis-of-heterogeneity" class="section level4"> <h4>Meta-analysis of heterogeneity</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgUGVyZm9ybSBtZXRhLW1ldGEtYW5hbHlzaXMgKDMgZm9yIGVhY2ggb2YgdGhlIDkgZ3JvdXBpbmcgdGVybXM6IHZhci5DVlIsIHZhci5WUiwgdmFyLlJSKVxuXG5tZXRhY29tYm9oZXRlcm9fZmluYWwgPC0gbWV0YWNvbWJvaGV0ZXJvICU+JVxuICBncm91cF9ieShHcm91cGluZ1Rlcm0pICU+JVxuICBuZXN0KClcbmBgYCJ9 --> <pre class="r"><code>## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR) metacombohetero_final <- metacombohetero %>% group_by(GroupingTerm) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRXJyb3IgaW4gZXZhbChsaHMsIHBhcmVudCwgcGFyZW50KSA6IG9iamVjdCAnbWV0YWNvbWJvaGV0ZXJvJyBub3QgZm91bmRcbiJ9 --> <pre><code>Error in eval(lhs, parent, parent) : object 'metacombohetero' not found</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="heterogeneity-plot" class="section level4"> <h4>Heterogeneity PLOT</h4> <p>Restructure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="plot-s1-c-second-order-meta-analysis-on-heterogeneity" class="section level4"> <h4>Plot S1 C (Second-order meta analysis on heterogeneity)</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="combined-figure-s1-overall-count-data-meta-anlysis-results-heterogeneity" class="section level4"> <h4>Combined Figure S1: overall Count data, Meta anlysis results, Heterogeneity)</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSGV0ZXJvUzFcblxuYGBgIn0= --> <pre class="r"><code>HeteroS1 </code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiRXJyb3I6IG9iamVjdCAnSGV0ZXJvUzEnIG5vdCBmb3VuZFxuIn0= --> <pre><code>Error: object 'HeteroS1' not found</code></pre> <!-- rnb-output-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="figure-s2" class="section level2"> <h2>Figure S2</h2> <p>Plot FigS2 all significant results (CI not overlapping zero) for males ### FELIX: “ALL” missing.</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgMiByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKHBvc2l0aW9uX3N0YWNrKS4iXV0sImhlaWdodCI6NDA5LjE0NzEsInNpemVfYmVoYXZpb3IiOjAsIndpZHRoIjo2NjJ9 --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="prepare-data-for-traits-with-effect-size-ratios-10-larger-in-males-supplemental-figure-s2" class="section level3"> <h3>Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2</h3> </div> <div id="felix-all-missing." class="section level3"> <h3>FELIX: “ALL” missing.</h3> <p>This Figure extends Figure 4, as it includes results not only for lnCVR and lnRR but also lnCVR. In addition, we compare two different assessments of sex-bias, significance (CI not overlapping zero) and sex differences in male / female ratios > 10%</p> </div> <div id="over-10-male-bias-count-data-first--order-metanalysis" class="section level3"> <h3>Over 10% male bias, count data (first- order metanalysis)</h3> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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--> <pre class="r"><code>meta.plot2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plot2.over10.c <- meta.plot2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plot2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c) meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # *Plot Fig2 Sex difference in m/f ratio > 10% malebias_Fig2_over10 <- ggplot(meta.plot2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_Fig2_over10 (supplemental Figure S2)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="fig-s2-second-order-meta-analysis-male-traits" class="section level4"> <h4>Fig S2, second-order meta-analysis, male traits</h4> </div> <div id="female-figure-significant-traits" class="section level4"> <h4>Female Figure, significant traits</h4> <p>Female FigS2 B sig</p> <p>Prepare data for traits with CI not overlapping 0 create column with 1= different from zero, 0= zero included in CI</p> <p>Restructure data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgOCByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCA3IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQwOS4xNDcxLCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NjYyfQ== --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Prepare data for traits with m/f difference > 10%</p> <p>Create column with 1= larger, 0= difference not larger than 10% between male/female ratios</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta.male.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR > log(11 / 10), 1, 0), percVR = ifelse(lnVR > log(11 / 10), 1, 0), percRR = ifelse(lnRR > log(11 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>% filter(percCVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuVlJcbm1ldGFjb21ib19tYWxlLnBsb3QzLlZSLnBlcmMgPC0gbWV0YS5tYWxlLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjVlIgPT0gMSkgJT4lXG4gIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG5lc3QoKVxuXG5tZXRhY29tYm9fbWFsZS5wbG90My5WUi5wZXJjLmFsbCA8LSBtZXRhLm1hbGUucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNWUiA9PSAxKSAlPiVcbiAgbmVzdCgpXG5gYGAifQ== --> <pre class="r"><code># Significant subset for lnVR metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>% filter(percVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuUlJcbm1ldGFjb21ib19tYWxlLnBsb3QzLlJSLnBlcmMgPC0gbWV0YS5tYWxlLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjUlIgPT0gMSkgJT4lXG4gIGdyb3VwX2J5KEdyb3VwaW5nVGVybSkgJT4lXG4gIG5lc3QoKVxuXG5tZXRhY29tYm9fbWFsZS5wbG90My5SUi5wZXJjLmFsbCA8LSBtZXRhLm1hbGUucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNSUiA9PSAxKSAlPiVcbiAgbmVzdCgpXG5gYGAifQ== --> <pre class="r"><code># Significant subset for lnRR metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>% filter(percRR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlZSLnBlcmMgPC0gYmluZF9yb3dzKHBsb3QzLm1hbGUubWV0YS5WUi5wZXJjLCBwbG90My5tYWxlLm1ldGEuVlIucGVyYy5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWFsZS5tZXRhLlJSLnBlcmMgPC0gYmluZF9yb3dzKHBsb3QzLm1hbGUubWV0YS5SUi5wZXJjLCBwbG90My5tYWxlLm1ldGEuUlIucGVyYy5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b)) plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing) plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ] plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b)) plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing) plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ] plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing) add.row.eye <- as.data.frame(t(c("Eye", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.RR.perc.b)) plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye) plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ] plot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5tYWxlLnBsb3QzLnBlcmMgPC0gZnVsbF9qb2luKHBsb3QzLm1hbGUubWV0YS5DVlIuVnIucGVyYywgcGxvdDMubWFsZS5tZXRhLlJSLnBlcmMuYilcbmBgYCJ9 --> <pre class="r"><code>overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm)))</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting : Male biased, 10% difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.male.perc <- bind_rows(lnCVR.ci,lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.male.perc$label <- "Sex difference in m/f ratios > 10%" overall4.male.perc$value <- as.numeric(overall4.male.perc$value) overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low) overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig S2 all >10% difference (male bias)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgNyByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCA3IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQwOS4xNDcxLCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NjYyfQ== --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="female-fig-s2-10" class="section level4"> <h4>Female Fig S2 >10%</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code> meta.plot3.perc <- metacombo %>% mutate( percCVR = ifelse(lnCVR < log(9 / 10), 1, 0), percVR = ifelse(lnVR < log(9 / 10), 1, 0), percRR = ifelse(lnRR < log(9 / 10), 1, 0) ) # Significant subset for lnCVR metacombo_plot3.CVR.perc <- meta.plot3.perc %>% filter(percCVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>% filter(percCVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuVlJcbm1ldGFjb21ib19wbG90My5WUi5wZXJjIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNWUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19wbG90My5WUi5wZXJjLmFsbCA8LSBtZXRhLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjVlIgPT0gMSkgJT4lXG4gIG5lc3QoKVxuYGBgIn0= --> <pre class="r"><code># Significant subset for lnVR metacombo_plot3.VR.perc <- meta.plot3.perc %>% filter(percVR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.VR.perc.all <- meta.plot3.perc %>% filter(percVR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTaWduaWZpY2FudCBzdWJzZXQgZm9yIGxuUlJcbm1ldGFjb21ib19wbG90My5SUi5wZXJjIDwtIG1ldGEucGxvdDMucGVyYyAlPiVcbiAgZmlsdGVyKHBlcmNSUiA9PSAxKSAlPiVcbiAgZ3JvdXBfYnkoR3JvdXBpbmdUZXJtKSAlPiVcbiAgbmVzdCgpXG5cbm1ldGFjb21ib19wbG90My5SUi5wZXJjLmFsbCA8LSBtZXRhLnBsb3QzLnBlcmMgJT4lXG4gIGZpbHRlcihwZXJjUlIgPT0gMSkgJT4lXG4gIG5lc3QoKVxuYGBgIn0= --> <pre class="r"><code># Significant subset for lnRR metacombo_plot3.RR.perc <- meta.plot3.perc %>% filter(percRR == 1) %>% group_by(GroupingTerm) %>% nest() metacombo_plot3.RR.perc.all <- meta.plot3.perc %>% filter(percRR == 1) %>% nest()</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiYC4uLmAgbXVzdCBub3QgYmUgZW1wdHkgZm9yIHVuZ3JvdXBlZCBkYXRhIGZyYW1lcy5cbkRpZCB5b3Ugd2FudCBgZGF0YSA9IGV2ZXJ5dGhpbmcoKWA/XG4ifQ== --> <pre><code>`...` must not be empty for ungrouped data frames. Did you want `data = everything()`?</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Final fixed effects meta-analyses within grouping terms, with SE of the estimate plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) # Across all grouping terms # plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>% mutate(model_lnCVR = map(data, ~ metafor::rma.uni( yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>% mutate(model_lnVR = map(data, ~ metafor::rma.uni( yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = "All") plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>% mutate(model_lnRR = map(data, ~ metafor::rma.uni( yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F ))) plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = "All") # Combine with separate grouping term results plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWV0YS5WUi5wZXJjIDwtIGJpbmRfcm93cyhwbG90My5tZXRhLlZSLnBlcmMsIHBsb3QzLm1ldGEuVlIucGVyYy5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdDMubWV0YS5SUi5wZXJjIDwtIGJpbmRfcm93cyhwbG90My5tZXRhLlJSLnBlcmMsIHBsb3QzLm1ldGEuUlIucGVyYy5hbGwpXG5gYGAifQ== --> <pre class="r"><code>plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiVmVjdG9yaXppbmcgJ3ZjdHJzX2xpc3Rfb2YnIGVsZW1lbnRzIG1heSBub3QgcHJlc2VydmUgdGhlaXIgYXR0cmlidXRlc1ZlY3Rvcml6aW5nICd2Y3Ryc19saXN0X29mJyBlbGVtZW50cyBtYXkgbm90IHByZXNlcnZlIHRoZWlyIGF0dHJpYnV0ZXNcbiJ9 --> <pre><code>Vectorizing 'vctrs_list_of' elements may not preserve their attributesVectorizing 'vctrs_list_of' elements may not preserve their attributes</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin 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 --> <pre class="r"><code># **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters" plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>% mutate( lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)), lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.CVR.perc.b)) plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing) plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm), ] plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>% mutate( lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)), lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.VR.perc.b)) plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing) plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm), ] plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>% mutate( lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)), lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3)) ))[, c(1, 4:7)] add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing) add.row.hematology <- as.data.frame(t(c("Hematology", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b)) plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology) plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm), ] plot3.meta.CVR.perc.c <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5wbG90My5wZXJjIDwtIGZ1bGxfam9pbihwbG90My5tZXRhLkNWUi5wZXJjLmMsIHBsb3QzLm1ldGEuUlIucGVyYy5iKVxuYGBgIn0= --> <pre class="r"><code>overall.plot3.perc <- full_join(plot3.meta.CVR.perc.c, plot3.meta.RR.perc.b)</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiSm9pbmluZywgYnkgPSBcIkdyb3VwaW5nVGVybVwiXG4ifQ== --> <pre><code>Joining, by = "GroupingTerm"</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3ZlcmFsbC5wbG90My5wZXJjJEdyb3VwaW5nVGVybSA8LSBmYWN0b3Iob3ZlcmFsbC5wbG90My5wZXJjJEdyb3VwaW5nVGVybSwgbGV2ZWxzID0gYyhcIkJlaGF2aW91clwiLCBcIk1vcnBob2xvZ3lcIiwgXCJNZXRhYm9saXNtXCIsIFwiUGh5c2lvbG9neVwiLCBcIkltbXVub2xvZ3lcIiwgXCJIZW1hdG9sb2d5XCIsIFwiSGVhcnRcIiwgXCJIZWFyaW5nXCIsIFwiRXllXCIsIFwiQWxsXCIpKVxub3ZlcmFsbC5wbG90My5wZXJjJEdyb3VwaW5nVGVybSA8LSBmYWN0b3Iob3ZlcmFsbC5wbG90My5wZXJjJEdyb3VwaW5nVGVybSwgcmV2KGxldmVscyhvdmVyYWxsLnBsb3QzLnBlcmMkR3JvdXBpbmdUZXJtKSkpXG5gYGAifQ== --> <pre class="r"><code>overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm)))</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting Female bias, 10 percent difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) lnCVR.ci <- overall3.perc %>% filter(parameter == "lnCVR") %>% mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper) lnVR.ci <- overall3.perc %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper) lnRR.ci <- overall3.perc %>% filter(parameter == "lnRR") %>% mutate(ci.low = lnRR_lower, ci.high = lnRR_upper) overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) overall4.perc$label <- "Sex difference in m/f ratios > 10%" overall4.perc$value <- as.numeric(overall4.perc$value) overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low) overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all >10% difference (female)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgOSByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS4iXSxbMSwiUmVtb3ZlZCA5IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9wb2ludCkuIl1dLCJoZWlnaHQiOjQwOS4xNDcxLCJzaXplX2JlaGF2aW9yIjowLCJ3aWR0aCI6NjYyfQ== --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> <div id="plot-fig-s2-plots-combined" class="section level4"> <h4>Plot Fig S2: plots combined</h4> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShnZ3B1YnIpXG5GaWdTMmIgPC0gZ2dhcnJhbmdlKE1ldGFtZXRhX0ZpZzNfZmVtYWxlLnNpZywgTWV0YW1ldGFfRmlnM19tYWxlLnNpZyxcbiAgbmNvbCA9IDIsIG5yb3cgPSAxLCB3aWR0aHMgPSBjKDEsIDEuMjApLCBoZWlnaHRzID0gYygxLCAxKVxuKVxuYGBgIn0= --> <pre class="r"><code>library(ggpubr) FigS2b <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) )</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUmVtb3ZlZCA1IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9lcnJvcmJhcmgpLlJlbW92ZWQgNCByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fcG9pbnQpLlJlbW92ZWQgOSByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS5SZW1vdmVkIDkgcm93cyBjb250YWluaW5nIG1pc3NpbmcgdmFsdWVzIChnZW9tX3BvaW50KS5cbiJ9 --> <pre><code>Removed 5 rows containing missing values (geom_errorbarh).Removed 4 rows containing missing values (geom_point).Removed 9 rows containing missing values (geom_errorbarh).Removed 9 rows containing missing values (geom_point).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnUzJkIDwtIGdnYXJyYW5nZShNZXRhbWV0YV9GaWczX2ZlbWFsZS5wZXJjLCBNZXRhbWV0YV9GaWczX21hbGUucGVyYyxcbiAgbmNvbCA9IDIsIG5yb3cgPSAxLCB3aWR0aHMgPSBjKDEsIDEuMjApLCBoZWlnaHRzID0gYygxLCAxKVxuKVxuYGBgIn0= --> <pre class="r"><code>FigS2d <- ggarrange(Metameta_Fig3_female.perc, Metameta_Fig3_male.perc, ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1) )</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUmVtb3ZlZCA5IHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAoZ2VvbV9lcnJvcmJhcmgpLlJlbW92ZWQgOSByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fcG9pbnQpLlJlbW92ZWQgNyByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKGdlb21fZXJyb3JiYXJoKS5SZW1vdmVkIDcgcm93cyBjb250YWluaW5nIG1pc3NpbmcgdmFsdWVzIChnZW9tX3BvaW50KS5cbiJ9 --> <pre><code>Removed 9 rows containing missing values (geom_errorbarh).Removed 9 rows containing missing values (geom_point).Removed 7 rows containing missing values (geom_errorbarh).Removed 7 rows containing missing values (geom_point).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBlbmQgY29tYmluYXRpb24gRmlndXJlIDVcbkZpZ1MyIDwtIGdnYXJyYW5nZShtYWxlYmlhc19GaWdTMl9zaWd0cmFpdHMsIG1hbGViaWFzX0ZpZzJfb3ZlcjEwLCBGaWdTMmIsIEZpZ1MyZCwgbmNvbCA9IDEsIG5yb3cgPSA0LCBoZWlnaHRzID0gYygyLjMsIDIsIDIuMSwgMiksIGxhYmVscyA9IGMoXCJBXCIsIFwiIFwiLCBcIkJcIiwgXCIgXCIpKVxuYGBgIn0= --> <pre class="r"><code># end combination Figure 5 FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_Fig2_over10, FigS2b, FigS2d, ncol = 1, nrow = 4, heights = c(2.3, 2, 2.1, 2), labels = c("A", " ", "B", " "))</code></pre> <!-- rnb-source-end --> <!-- rnb-output-begin eyJkYXRhIjoiUmVtb3ZlZCAyIHJvd3MgY29udGFpbmluZyBtaXNzaW5nIHZhbHVlcyAocG9zaXRpb25fc3RhY2spLlJlbW92ZWQgOCByb3dzIGNvbnRhaW5pbmcgbWlzc2luZyB2YWx1ZXMgKHBvc2l0aW9uX3N0YWNrKS5cbiJ9 --> <pre><code>Removed 2 rows containing missing values (position_stack).Removed 8 rows containing missing values (position_stack).</code></pre> <!-- rnb-output-end --> <!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRmlnUzJcbmBgYCJ9 --> <pre class="r"><code>FigS2</code></pre> <!-- rnb-source-end --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MDkuMTQ3MSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjY2Mn0= --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> </div> <div id="not-sure-what-this-below-is" class="section level2"> <h2>NOT SURE WHAT THIS BELOW IS??</h2> </div> <div id="figure-s2-sex-bias-including-vr" class="section level2"> <h2>Figure S2: sex-bias, including VR</h2> <p>Prepare data for traits with effect size ratios > 10% larger in males</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-source-begin 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 --> <pre class="r"><code>meta.plotS2.over10 <- meta_clean %>% select(lnCVR, lnVR, lnRR, GroupingTerm) %>% arrange(GroupingTerm) meta.plotS2.over10.b <- gather(meta.plotS2.over10, trait, value, c(lnCVR, lnVR, lnRR)) meta.plotS2.over10.b$trait <- factor(meta.plotS2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) meta.plotS2.over10.c <- meta.plotS2.over10.b %>% group_by_at(vars(trait, GroupingTerm)) %>% summarise( malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias, malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total ) meta.plotS2.over10.c$label <- "Sex difference in m/f ratios > 10%" # restructure to create stacked bar plots meta.plotS2.over10.c <- as.data.frame(meta.plotS2.over10.c) meta.plotS2.over10.d <- gather(meta.plotS2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE) # create new sample size variable meta.plotS2.over10.d$samplesize <- with(meta.plotS2.over10.d, ifelse(sex == "malepercent", malebias, femalebias)) # *Plot FigS2 Sex difference in m/f ratio > 10% malebias_FigS2_over10 <- ggplot(meta.plotS2.over10.d) + aes(x = GroupingTerm, y = percent, fill = sex) + geom_col() + geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text( data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5), color = "white", size = 3.5 ) + facet_grid( cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18), scales = "free", space = "free" ) + scale_fill_brewer(palette = "Set2") + theme_bw(base_size = 18) + theme( strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)), strip.text.x = element_blank(), strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5, "lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid", color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank() ) + coord_flip() # malebias_FigS2_over10 #(Panel B in Fig S2 in ms)</code></pre> <!-- rnb-source-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>#Metameta_FigS2_male.sig (Figure 5B right panel)</p> <p>Restructure MALE data for plotting</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all significant results (CI not overlapping zero, male )</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <div id="perc-sex-difference-male-bias" class="section level3"> <h3>10 % Perc sex difference, male bias</h3> <p>Restructure data for plotting : Male biased, 10% difference</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot FigS2 all >10% difference (male bias)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Restructure data for plotting: Female bias, 10 percent difference, including VR</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Plot Fig5D all >10% difference (female)</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <p>Figure S2</p> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MDkuMTQ3MSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjY2Mn0= --> <p><img 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" /></p> <!-- rnb-plot-end --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> </div> </div> <div id="acknowledgements" class="section level2"> <h2>Acknowledgements</h2> <p>tbd</p> </div> <div id="r-session-information" class="section level2"> <h2>R Session Information</h2> <!-- rnb-text-end --> <!-- rnb-chunk-begin --> <!-- rnb-chunk-end --> <!-- rnb-text-begin --> <!-- rnb-text-end --> </div> </div> <div id="rmd-source-code">---
title: "IMPC Mouse data - Variance in sex differences"
author: "Susanne Zajitschek,  Felix Zajitschek, Russell Bonduriansky,Robert  Brooks, Will Cornwell, Daniel Falster, Malgortaza Lagisz, Jeremy Mason, Daniel Noble, Alistair Senior & Shinichi Nakagawa"
date: "August 2019"
output:
  html_document:
    code_download: true
    code_folding: hide
    depth: 4
    number_sections: no
    theme:  flatly
    toc: yes
    toc_depth: 4
    toc_float: yes
  html_notebook:
    toc: yes
  pdf_document:
    toc: yes
    toc_depth: '4'
subtitle: Electronic Supplementary Material
---

# Set-up

## Loading packages & custom functions

```{r, include=FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  warning = FALSE,
  message = FALSE,
  cache = TRUE,
  tidy = TRUE
)
```

```{r}
library(readr)
library(dplyr)
library(metafor)
library(devtools)
library(purrr)
library(tidyverse)
library(tidyr)
library(tibble)
library(kableExtra)
library(robumeta)
library(ggpubr)
library(ggplot2)
library(here)
```

Functions for preparing the data for meta analyses

1) Create function for sub-setting the data to choose only one data point per individual per trait: "data_subset_parameterid_individual_by_age"

```{r}
data_subset_parameterid_individual_by_age <- function(mydata, parameter, age_min=0, age_center=100) {
  tmp <- mydata %>%
    filter(
      age_in_days >= age_min,
      id == parameter
    ) %>%
    # take results for single individual closest to age_center
    mutate(age_diff = abs(age_center - age_in_days)) %>%
    group_by(biological_sample_id) %>%
    filter(age_diff == min(age_diff)) %>%
    select(-age_diff)# %>% 
#    filter(!duplicated(biological_sample_id))
    
  # still some individuals with multiple records (because same individual appear under different procedures, so filter to one record)
  j <- match(unique(tmp$biological_sample_id), tmp$biological_sample_id)
  tmp[j, ] 
  }
```

2) "Population statistics": "calculate_population_stats"
This function groups animals from the same strain and same insitiution together. This is done for each trait seoarately, and only for traits that have been measured in both sexes. Any group containing fewer than 5 individuals is excluded.

```{r}
calculate_population_stats <- function(mydata, min_individuals = 5) {
  mydata %>%
    group_by(population, strain_name, production_center, sex) %>%
    summarise(
      trait = parameter_name[1],
      x_bar = mean(data_point),
      x_sd = sd(data_point),
      n_ind = n()
    ) %>%
    ungroup() %>%
    filter(n_ind > min_individuals) %>%
    # Check both sexes present & filter those missing
    group_by(population) %>%
    mutate(
      n_sex = n_distinct(sex)
    ) %>%
    ungroup() %>%
    filter(n_sex == 2) %>%
    select(-n_sex) %>%
    arrange(production_center, strain_name, population, sex)
}
```

3) Extraction of effect sizes and sample variances: "create_meta_analysis_effect_sizes"

```{r}
create_meta_analysis_effect_sizes <- function(mydata) {
  i <- seq(1, nrow(mydata), by = 2)
  input <- data.frame(
    n1i = mydata$n_ind[i],
    n2i = mydata$n_ind[i + 1],
    x1i = mydata$x_bar[i],
    x2i = mydata$x_bar[i + 1],
    sd1i = mydata$x_sd[i],
    sd2i = mydata$x_sd[i + 1]
  )

  mydata[i, ] %>%
    select(strain_name, production_center, trait) %>%
    mutate(
      effect_size_CVR = calculate_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_CVR = calculate_var_lnCVR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      effect_size_VR = calculate_lnVR(CSD = input$sd1i, CN = input$n1i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_VR = calculate_var_lnVR(CN = input$n1i, EN = input$n2i),
      effect_size_RR = calculate_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      sample_variance_RR = calculate_var_lnRR(CMean = input$x1i, CSD = input$sd1i, CN = input$n1i, EMean = input$x2i, ESD = input$sd2i, EN = input$n2i),
      err = as.factor(seq_len(n()))
    )
}
```

 
4) Calculate meta-analysis statistics

Based on function created by A M Senior @ the University of Otago NZ 03/01/2014: 

* Calculates effect sizes for meta-analysis of variance.  All functions take the mean, sd and n from the control and experimental groups.
* The first function, calculate_lnCVR, calculates the the log response-ratio of the coefficient of variance (lnCVR) - see Nakagawa et al 2015.
* The second function calculates the measurement error variance for lnCVR. As well as the aforementioned parameters, this function also takes Equal_E_C_Corr (default = T), which must be True or False. If true, the function assumes that the correlation between mean and sd (Taylor's Law)  is equal for the mean and control groups, and, thus these data are pooled. If False the mean-SD correlation for the experimental and control groups are calculated separately from one another.
* Similar functions are then implemented for lnVR (for comparison of standard deviations) and ln RR  (for comparison of means) 
 
```{r}

calculate_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  log(ESD) - log(EMean) + 1 / (2 * (EN - 1)) - (log(CSD) - log(CMean) + 1 / (2 * (CN - 1)))
}

calculate_var_lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal_E_C_Corr = T) {
  if (Equal_E_C_Corr == T) {
    mvcorr <- 0 # cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate   old, slightly incorrect
    S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
  }
  else {
    Cmvcorr <- cor.test(log(CMean), log(CSD))$estimate
    Emvcorr <- cor.test(log(EMean), (ESD))$estimate
    S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
  }
  S2
}

calculate_lnVR <- function(CSD, CN, ESD, EN) {
  log(ESD) - log(CSD) + 1 / (2 * (EN - 1)) - 1 / (2 * (CN - 1))
}

calculate_var_lnVR <- function(CN, EN) {
  1 / (2 * (EN - 1)) + 1 / (2 * (CN - 1))
}

calculate_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  log(EMean) - log(CMean)
}

calculate_var_lnRR <- function(CMean, CSD, CN, EMean, ESD, EN) {
  CSD^2 / (CN * CMean^2) + ESD^2 / (EN * EMean^2)
}
```

## Load & clean data

1) Data loading and cleaning of the csv file

This step we have already done and provide a cleaned up file which is less computing intensive and which we have saved in a folder called `export`. However, the cvs is provided in case this is preferred to be attempted, following the steps below:

```{r clean, eval=FALSE, include=TRUE}
# loads the raw data, setting some default types for various columns

load_raw <- function(filename) {
  read_csv(filename,
    col_types = cols(
      .default = col_character(),
      project_id = col_character(),
      id = col_character(),
      parameter_id = col_character(),
      age_in_days = col_integer(),
      date_of_experiment = col_datetime(format = ""),
      weight = col_double(),
      phenotyping_center_id = col_character(),
      production_center_id = col_character(),
      weight_date = col_datetime(format = ""),
      date_of_birth = col_datetime(format = ""),
      procedure_id = col_character(),
      pipeline_id = col_character(),
      biological_sample_id = col_character(),
      biological_model_id = col_character(),
      weight_days_old = col_integer(),
      datasource_id = col_character(),
      experiment_id = col_character(),
      data_point = col_double(),
      age_in_weeks = col_integer(),
      `_version_` = col_character()
    )
  )
}

# Apply some standard cleaning to the data
clean_raw_data <- function(mydata) {
  
  group <- read_csv(here("data", "ParameterGrouping.csv"))
  
  tmp <- 
    mydata %>%

    # Filter to IMPC source (recommend by Jeremey in email to Susi on 20 Aug 2018)
    filter(datasource_name == "IMPC") %>%

    # standardise trait names
    mutate(parameter_name = tolower(parameter_name)) %>%

    # remove extreme ages
    filter(age_in_days > 0 & age_in_days < 500) %>%

    # remove NAs
    filter(!is.na(data_point)) %>%

    # subset to reasonable set of variables, date_of_experiment used as an indicator of batch-level effects
    select(production_center, strain_name, strain_accession_id, biological_sample_id, pipeline_stable_id, procedure_group, procedure_name, sex, date_of_experiment, age_in_days, weight, parameter_name, data_point) %>% 

    # sort
    arrange(production_center, biological_sample_id, age_in_days)
      
    # filter to groups with > 1 centre  
    merge(tmp, 
          tmp %>% group_by(parameter_name) %>%
    summarise(center_per_trait = length(unique(production_center, na.rm = TRUE)))
        )%>%
    filter(center_per_trait >= 2) %>% 

    # Define population variable
    mutate(population = sprintf("%s-%s", production_center, strain_name)) %>% 

    # add grouping variable: these were decided based on functional groups and procedures 
    mutate(parameter_group = group$parameter[match(parameter_name, group$parameter_name)] ) %>%
    
    # Assign unique IDs (per trait)
    # each unique parameter_name (=trait,use trait variable) gets a unique number ('id')

    # We add a new variable, where redundant traits are combined
    #[note however, at this stage the dataset still contains nonsensical traits, i.e. traits that may not contain any information on variance]
    mutate(id = match(parameter_name, unique(parameter_name))) %>% 
    as_tibble()
}

# Load raw data - save cleaned dataset as RDS for reuse
data_raw <- load_raw(here("data","dr7.0_all_control_data.csv.gz"))
dir.create("export", F, F)

data <- data_raw %>% 
  clean_raw_data() 
saveRDS(data, "export/data_clean.rds")
```

For analysis we load the RDS created above and other datasets:

```{r load}
data <- readRDS(here("export", "data_clean.rds")) 

procedures <- read_csv(here("data", "procedures.csv"))

```


Checking length of different variables and sample sizes.

# Table 1:  "Strains and Center Sample Sizes"
This table summarises the available numbers of male and female mice from each strain and originating institution.

```{r}
length(unique(data$parameter_name)) # 232 traits
length(unique(data$parameter_group)) # 161 parameter groups
length(unique(data$procedure_name)) # 26 procedure groups
length(unique(data$biological_sample_id)) # 27147 individial mice   

#number of males and females per strain per production center 
kable(cbind(data %>% group_by(production_center, strain_name) %>% count(biological_sample_id, sex) %>% count(sex) %>% print(n = Inf))) %>%
  kable_styling() %>%
  scroll_box(width = "60%", height = "200px")
```

# Meta-analyses
## 1. Population as analysis unit 
(Step C, Figure 3 in main document)

### Loop: Meta-analyses on all traits

* The loop combines the functions mentioned above and fills the data matrix with results from our meta analysis. 
* Error messages indicate traits that either did not reach convergence, or that did not return meaningful results in the meta-analysis, due to absence of variance. Those traits will be removed in later steps, outlined below.

```{r}

(n <- length(unique(data$id)))

# Create dataframe to store results
results_alltraits_grouping <- 
    tibble(id = 1:n, lnCVR=0, lnCVR_lower=0, lnCVR_upper=0, 
           lnCVR_se=0, lnVR=0, lnVR_lower=0, lnVR_upper=0, 
           lnVR_se=0, lnRR=0, lnRR_lower=0, lnRR_upper=0, lnRR_se=0, sampleSize=0, trait=0)

for (t in 1:n) {
  tryCatch(
    {
      results <- data %>% 
        data_subset_parameterid_individual_by_age(t) %>%
        calculate_population_stats() %>%
        create_meta_analysis_effect_sizes()

      # lnCVR,  log repsonse-ratio of the coefficient of variance
      cvr <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, 
                             random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                             control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                            maxit = 1000), verbose = F, data = results)

      # lnVR, comparison of standard deviations
      cv <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR,
                            random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                            control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                           maxit = 1000), verbose = F, data = results)

      # for means, lnRR
      means <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, 
                               random = list(~ 1 | strain_name, ~ 1 | production_center, ~ 1 | err), 
                               control = list(optimizer = "optim", optmethod = "Nelder-Mead", 
                                              maxit = 1000), verbose = F, data = results)
      
      f <- function(x) unlist(x[c("b", "ci.lb", "ci.ub", "se")])

      results_alltraits_grouping[t, 2:14] <- c(f(cvr), f(cv), f(means), means$k)
      results_alltraits_grouping[t, 15] <- unique(results$trait)
    },
    error = function(e) {
      cat("ERROR :", t, conditionMessage(e), "\n")
    }
  )
}
```

In the above function, we use 'tryCatch' and 'conditionMessage' to prevent the loop from aborting when the first error at row 84 is produced.
As convergence in the two listed non-converging cases can't be achieved by sensibly tweaking (other optim etc.), and we only learn about non-convergence in the loop, it is not possible to exclude the traits (N=2) beforehand.
Similarly, there are 8 traits with very low variation, which can not be excluded prior running the loop.

The produced "Warnings" indicate cases where variance components are set to zero during likelihood optimization.


### Merging datasets & removal of non-converged traits

Procedure names, grouping variables and trait names ("parameter_names") are merged back together with the results from the metafor analysis above.
 

```{r}
results_alltraits_grouping2 <- 
  results_alltraits_grouping %>% 
  left_join(by="id",
             data %>% select(id, parameter_group, procedure = procedure_name, procedure_name, parameter_name) %>%   # We filter duplicated id's to get only one unique row per id (and there is one id per parameter_name)
              filter(!duplicated(id))
            ) %>%
  # Below we add 'procedure' (from the previously loaded 'procedures.csv') as a variable
  left_join(by="procedure", 
            procedures %>% distinct()
            )
  

(n <- length(unique(results_alltraits_grouping2$parameter_name))) # 232
```

### Removal of traits 
14 traits from the originally 232 that had been included are removed because they either did not achieve convergence or are nonsensical for analysis of variance (such as traits that show no variation, see list below). 

Not converged: "dp t cells", "mzb (cd21/35 high)"

Not enough variation: "number of caudal vertebrae", "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left","number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a","total number of acquired events in panel b", "whole arena permanence".


```{r}
# We exclude 14 parameter names for which metafor models didn't converge ("dp t cells", "mzb (cd21/35 high)"), and of parameters that don't harbour enough variation
meta_clean <- results_alltraits_grouping2 %>% 
	  filter(!parameter_name %in% c("dp t cells", "mzb (cd21/35 high)", "number of caudal vertebrae", 
	  "number of cervical vertebrae", "number of digits", "number of lumbar vertebrae", "number of pelvic vertebrae", "number of ribs left",                       
        "number of ribs right", "number of signals", "number of thoracic vertebrae", "total number of acquired events in panel a",
        "total number of acquired events in panel b", "whole arena permanence"))

```


**Reveiw**: check against old script -- identical, remove once fixed #
#Felix: not sure

```{r, eval=FALSE}
meta_clean.test <- readRDS(here("export", "meta_clean.test.rds"))  
all.equal(meta_clean, meta_clean.test %>% mutate(id=as.integer(id), parameter_group = as.character(parameter_group), GroupingTerm = as.character(GroupingTerm)))
```
[1] "Rows in x but not y: 162, 161. Rows in y but not x: 162, 161. "
Not sure??

## 2. Meta-analysis: condensing non-independent traits 
(Step F in Figure 3 in main article)

### Dealing with Correlated Parameters, preparation

This dataset contained a number of highly correlated traits, such as different kinds of cell counts (for example hierarchical parameterization within immunological assays). As those data-points are not independent of each other,  we conducted meta analyses on these correlated parameters to collapse the number of levels.

#### Collapsing and merging correlated parameters

Here we double check numbers of trait parameters in the dataset

```{r}

meta1 <- meta_clean 
length(unique(meta1$procedure)) #18
length(unique(meta1$GroupingTerm)) #9
length(unique(meta1$parameter_group)) # 148 levels. To be used as grouping factor for meta-meta analysis / collapsing down based on things that are classified identically in "parameter_group" but have different "parameter_name"
length(unique(meta1$parameter_name)) #218
```

#### Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size) 

## Table: Numbers of correlated and uncorrelated traits
This serves to identify and separate the traits that are correlated from the full dataset that can be processed as is. If the sample size (n) for a given "parameter group" equals 1, the trait is unique and uncorrelated. All instances, where there are 2 or more traits associated with the same  parameter group (90 cases), are selected for a "mini-meta analysis", which removes the issue of correlation.

```{r}
kable(cbind(meta1 %>% count(parameter_group))) %>%
  kable_styling() %>%
  scroll_box(width = "60%", height = "200px")
```

```{r}
meta1_sub <- meta1 %>%
  # add summary of number of parameter names in each parameter group
  group_by(parameter_group) %>%
  mutate(par_group_size = length(unique(parameter_name)), 
         sampleSize = as.numeric(sampleSize)) %>% 
  ungroup() %>% 
  # Create subsets with > 1 count (par_group_size > 1)
  filter(par_group_size > 1) # 90 observations
```

#### Meta-analyses on correlated (sub-)traits, using robumeta` 
Here we pepare the subset of the data (using nest()), and in this first step the model of the meta analysis effect sizes are calculated

```{r}

meta1b <-
  meta1 %>%
  group_by(parameter_group) %>% 
  summarize(par_group_size = length(unique(parameter_name, na.rm = TRUE)))
#this gives a summary of number of parameter names in each parameter group, now it neeeds to get merged it back together


meta1$par_group_size <- meta1b$par_group_size[match(meta1$parameter_group, meta1b$parameter_group)]

# Create subsets with > 1 count (par_group_size > 1) 

meta1_sub <- subset(meta1,par_group_size >1) # 90 observations   
meta1_sub$sampleSize <- as.numeric(meta1_sub$sampleSize)

# nesting
n_count <- meta1_sub %>%
  group_by(parameter_group) %>%
  mutate(raw_N = sum(sampleSize)) %>%
  nest() %>%
  ungroup()

model_count <- n_count %>%
  mutate(
    model_lnRR = map(data, ~ robu(.x$lnRR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnRR_se)^2)),
    model_lnVR = map(data, ~ robu(.x$lnVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnVR_se)^2)),
    model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1, data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8, small = TRUE, var.eff.size = (.x$lnCVR_se)^2))
  )
```

#### Extract and save parameter estimates:
Function to collect the outcomes of the "mini" meta analysis

```{r}
count_fun <- function(mod_sub) {
  return(c(mod_sub$reg_table$b.r, mod_sub$reg_table$CI.L, mod_sub$reg_table$CI.U, mod_sub$reg_table$SE))
} # estimate, lower ci, upper ci, SE
```

Extraction of values created during Meta analysis using robu meta:

```{r}
robusub_RR <- model_count %>%
  transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun)) %>%
  mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnRR) %>%
  purrr::set_names(c("parameter_group", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se"))

robusub_CVR <- model_count %>%
  transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun)) %>%
  mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnCVR) %>%
  purrr::set_names(c("parameter_group", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se"))

robusub_VR <- model_count %>%
  transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun)) %>%
  mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnVR) %>%
  purrr::set_names(c("parameter_group", "lnVR", "lnVR_lower", "lnVR_upper", "lnVR_se"))

robu_all <- full_join(robusub_CVR, robusub_VR) %>% full_join(., robusub_RR)
```
#### Combine data 
Merge the two data sets (the new [robu_all] and the initial [uncorrelated sub-traits with count = 1]) 

```{r}
meta_all <- meta1 %>%
  filter(par_group_size == 1) %>%
  as_tibble()
# str(meta_all)
# str(robu_all)
# which(is.na(match(names(meta_all),names(robu_all))))  # check

#Step1:  Columns are matched by name (in our case, 'parameter_group'), and any missing columns will be filled with NA
combinedmeta <- bind_rows(robu_all, meta_all)
# glimpse(combinedmeta)

# Steps 2&3 (add information about number of traits in a parameter group, procedure, and grouping term) 
metacombo <- combinedmeta
metacombo$counts <- meta1$par_group_size[match(metacombo$parameter_group, meta1$parameter_group)] 
metacombo$procedure2 <- meta1$procedure[match(metacombo$parameter_group, meta1$parameter_group)]
metacombo$GroupingTerm2 <- meta1$GroupingTerm[match(metacombo$parameter_group, meta1$parameter_group)]

```

Clean-up, reorder, and rename 

```{r}
metacombo <- metacombo[c("parameter_group", "counts","procedure2","GroupingTerm2", "lnCVR","lnCVR_lower","lnCVR_upper","lnCVR_se","lnVR","lnVR_lower","lnVR_upper","lnVR_se","lnRR","lnRR_lower","lnRR_upper","lnRR_se")] 

names(metacombo)[names(metacombo)=="procedure2"] <- "procedure" 
names(metacombo)[names(metacombo)=="GroupingTerm2"] <- "GroupingTerm" 

# Quick pre-check before doing plots
metacombo %>%
  group_by(GroupingTerm) %>%
  dplyr::summarize(MeanCVR = mean(lnCVR), MeanVR = mean(lnVR), MeanRR = mean(lnRR))
```

# Table for SHINY APP

We use this corrected (for correlated traits) "results" table, which contains each of the meta-analytic means for all effect sizes of interest, for further analyses.  We further use this table as part of the Shiny App, which is able to provide the percentage differences between males and females for mean, variance and coefficient of variance. 

This is the full result dataset
```{r}
kable(metacombo) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")

# trait_meta_results <- write.csv(metacombo, file = "export/trait_meta_results.csv")
```

## 3. Second-order meta analysis for functional groups
(Section H in Figure 3 in main article)

### Performing meta-analyses (3 for each of the 9 grouping terms: lnCVR, lnVR, lnRR) 
#### Preparation of data
Nesting, calculating the number of parameters within each grouping term, and running the meta-analysis

```{r}
metacombo_final <- metacombo %>%
  group_by(GroupingTerm) %>%
   nest_legacy()   # we're using 'nest_legacy' to keep old syntax/functionality

# **calculate number of parameters per grouping term

metacombo_final <- metacombo_final %>% mutate(para_per_GroupingTerm = map_dbl(data, nrow))

# For all grouping terms
metacombo_final_all <- metacombo %>%
  nest_legacy() #'nest_legacy' to keep old syntax/functionality

# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

overall1 <- metacombo_final %>%

  mutate(
    model_lnCVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnRR = map(data, ~ metafor::rma.uni(
      yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    ))
  )

# **Final fixed effects meta-analyses ACROSS grouping terms, with SE of the estimate

overall_all1 <- metacombo_final_all %>%

  mutate(
    model_lnCVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnVR = map(data, ~ metafor::rma.uni(
      yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    )),
    model_lnRR = map(data, ~ metafor::rma.uni(
      yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
    ))
  )
```

### Re-structuring the data for each grouping term
We here delete unused variables, and select the respective effect sizes. Please note - the referencing of the cells does NOT depend on previous ordering of the data. This would only be affected if the output structure from metafor::rma.uni changes. 

```{r}
Behaviour <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Behaviour") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Immunology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Immunology") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Hematology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hematology") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Hearing <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Hearing") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Physiology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Physiology") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Metabolism <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Metabolism") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Morphology <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Morphology") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Heart <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Heart") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

Eye <- as.data.frame(overall1 %>% filter(., GroupingTerm == "Eye") %>% mutate(
  lnCVR = .[[4]][[1]]$b, lnCVR_lower = .[[4]][[1]]$ci.lb, lnCVR_upper = .[[4]][[1]]$ci.ub, lnCVR_se = .[[4]][[1]]$se,
  lnVR = .[[5]][[1]]$b, lnVR_lower = .[[5]][[1]]$ci.lb, lnVR_upper = .[[5]][[1]]$ci.ub, lnVR_se = .[[5]][[1]]$se,
  lnRR = .[[6]][[1]]$b, lnRR_lower = .[[6]][[1]]$ci.lb, lnRR_upper = .[[6]][[1]]$ci.ub, lnRR_se = .[[6]][[1]]$se
))[, c(1, 7:18)]

All <- as.data.frame(overall_all1 %>% mutate(
  lnCVR = .[[2]][[1]]$b, lnCVR_lower = .[[2]][[1]]$ci.lb, lnCVR_upper = .[[2]][[1]]$ci.ub, lnCVR_se = .[[2]][[1]]$se, lnVR = .[[3]][[1]]$b, lnVR_lower = .[[3]][[1]]$ci.lb, lnVR_upper = .[[3]][[1]]$ci.ub, lnVR_se = .[[3]][[1]]$se,
  lnRR = .[[4]][[1]]$b, lnRR_lower = .[[4]][[1]]$ci.lb, lnRR_upper = .[[4]][[1]]$ci.ub, lnRR_se = .[[4]][[1]]$se
))[, c(5:16)]

All$lnCVR <- as.numeric(All$lnCVR)
All$lnVR <- as.numeric(All$lnVR)
All$lnRR <- as.numeric(All$lnRR)
All <- All %>% mutate(GroupingTerm = "All")

overall2 <- bind_rows(Behaviour, Morphology, Metabolism, Physiology, Immunology, Hematology, Heart, Hearing, Eye, All) #FZ: warnings are ok
```

# Visualisation
## Figure 4 
#### Preparation for plots: Count data, based on First-order meta analysis results
This includes all separate eligible traits.
Re-ordering of grouping terms 

```{r}

meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye"))
meta_clean$GroupingTerm <- factor(meta_clean$GroupingTerm, rev(levels(meta_clean$GroupingTerm)))

# *Preparing data for all traits

meta.plot2.all <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plot2.all.b <- gather(meta.plot2.all, trait, value, c(lnCVR, lnRR)) # lnVR has been removed here and in the steps below, as this is only included in the supplemental figure

meta.plot2.all.b$trait <- factor(meta.plot2.all.b$trait, levels = c("lnCVR", "lnRR")) 
meta.plot2.all.c <- meta.plot2.all.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.all.c$label <- "All traits"

# restructure to create stacked bar plots

meta.plot2.all.d <- as.data.frame(meta.plot2.all.c)
meta.plot2.all.e <- gather(meta.plot2.all.d, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.all.e$samplesize <- with(meta.plot2.all.e, ifelse(sex == "malepercent", malebias, femalebias))

# add summary row ('All') and re-arrange rows into correct order for plotting #FZ added

meta.plot2.all.f <- meta.plot2.all.e %>% group_by(trait, sex) %>% 
	summarise(GroupingTerm = "All", malebias = sum(malebias), femalebias = sum(femalebias), total = malebias + femalebias, 
	label = "All traits", samplesize = sum(samplesize)) %>%
	mutate(percent = ifelse(sex == "femalepercent", femalebias*100/(malebias+femalebias), malebias*100/(malebias+femalebias))) %>%
	bind_rows(meta.plot2.all.e, .) %>%
	mutate(rownumber = row_number()) %>%
	.[c(37, 1:9, 39, 10:18, 38, 19:27, 40, 28:36), ]

meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All")) 
meta.plot2.all.f$GroupingTerm <- factor(meta.plot2.all.f$GroupingTerm, rev(levels(meta.plot2.all.f$GroupingTerm)))

malebias_Fig2_alltraits <-
  ggplot(meta.plot2.all.f) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.all.f, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_Fig2_alltraits     #(panel A in Figure 4 in ms)
```


###  Overall results of second order meta analysis (Figure 4, Panel B)
#### Restructure data for plotting 
Data are restructured, and grouping terms are being re-ordered

```{r}
overall3 <- gather(overall2, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3 %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3 %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3 %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4 <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

# re-order Grouping Terms

overall4$GroupingTerm <- factor(overall4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall4$GroupingTerm <- factor(overall4$GroupingTerm, rev(levels(overall4$GroupingTerm)))
overall4$label <- "All traits"

kable(cbind(overall4, overall4)) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "200px")
```

```{r}
Metameta_Fig3_alltraits <- overall4 %>%

  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.24, 0.25),
    breaks = c(-0.2, -0.1, 0, 0.1, 0.2),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_alltraits
```

### Fig 4
Join the different parts and  #TO DO!! add M / F symbols in Metameta_Fig3_alltraits 
```{r}
#Test
#male <- readPNG(system.file("img", "male"))
#test <- Metameta_Fig3_alltraits 

#library(png)
```


```{r}
Fig4 <- ggarrange(malebias_Fig2_alltraits, Metameta_Fig3_alltraits,  nrow = 2, align = "v", heights = c(1, 1), labels = c("A", "B"))
Fig4

```

### Figure 4: 
Panel A shows the numbers of traits across functional groups that are either male-biased (blue-green) or female-biased (orange-red), as calculated in Step D (figure 3). Panel B shows effect sizes and 95% CI from separate meta-analysis for each functional group (step H in Figure 3). Both panels represent results evaluated across all traits (Phase 3, Figure 3). Traits that are male biased are Male data is shown in blue, whereas female bias data is represented in orange.



## Figure 5
#### Preparing data for traits with CI not overlapping 0
To further investigate sex bias in this dataset, and in particular if the extent of sex bias differs between traits, we investigate the magnitude of male- and female bias in significantly different traits on (both for means and variability)

To do this, we select only traits that have CIs that do not overlap with zero.
### FELIX: "ALL" missing.  This figure is panel A in Fig 5
```{r}

meta.plot2.sig <- meta_clean %>%
  mutate(
    lnCVRsig = ifelse(lnCVR_lower * lnCVR_upper > 0, 1, 0), lnVRsig = ifelse(lnVR_lower * lnVR_upper > 0, 1, 0),
    lnRRsig = ifelse(lnRR_lower * lnRR_upper > 0, 1, 0)
  )

meta.plot2.sig.b <- meta.plot2.sig[, c("lnCVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")] # "lnVR",

meta.plot2.sig.c <- gather(meta.plot2.sig.b, trait, value, lnCVR:lnRR)
meta.plot2.sig.c$sig <- "placeholder"

meta.plot2.sig.c$trait <- factor(meta.plot2.sig.c$trait, levels = c("lnCVR", "lnRR")) # "lnVR",

meta.plot2.sig.c$sig <- ifelse(meta.plot2.sig.c$trait == "lnCVR", meta.plot2.sig.c$lnCVRsig,
  ifelse(meta.plot2.sig.c$trait == "lnVR", meta.plot2.sig.c$lnVRsig, meta.plot2.sig.c$lnRRsig)
)

# choosing sex biased ln-ratios significantly larger than 0
meta.plot2.sig.malebias <- meta.plot2.sig.c %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  filter(sig == 1) %>%
  summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig)

meta.plot2.sig.malebias <- ungroup(meta.plot2.sig.malebias) %>%
  add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros)
  mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total)

meta.plot2.sig.malebias$label <- "CI not overlapping zero"

# restructure to create stacked bar plots

meta.plot2.sig.bothsexes <- as.data.frame(meta.plot2.sig.malebias)
meta.plot2.sig.bothsexes.b <- gather(meta.plot2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.sig.bothsexes.b$samplesize <- with(meta.plot2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig))

# Plot Fig2 all significant results (CI not overlapping zero):
# Several grouing terms are added post-hoc (with no data to display): no significant lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no significant male-biased lnVR for 'Eye'.

malebias_Fig2_sigtraits <-
  ggplot(meta.plot2.sig.bothsexes.b) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.sig.bothsexes.b, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()
```


### Preparation for Plots on significant sex-bias (Second-order meta analysis results

#### Figure 5 B - traits with CI not overlapping 0 
Prepare data 
create column with 1= different from zero, 0= zero included in CI
#### Male-biased (significant) traits

```{r}
meta.male.plot3.sig <- metacombo %>%
  mutate(
    sigCVR = ifelse(lnCVR_lower > 0, 1, 0),
    sigVR = ifelse(lnVR_lower > 0, 1, 0),
    sigRR = ifelse(lnRR_lower > 0, 1, 0)
  )

# Significant subset for lnCVR
metacombo_male.plot3.CVR <- meta.male.plot3.sig %>%
  filter(sigCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.CVR.all <- meta.male.plot3.sig %>%
  filter(sigCVR == 1) %>%
  nest()

# Significant subset for lnVR
metacombo_male.plot3.VR <- meta.male.plot3.sig %>%
  filter(sigVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.VR.all <- meta.male.plot3.sig %>%
  filter(sigVR == 1) %>%
  nest()

# Significant subset for lnRR
metacombo_male.plot3.RR <- meta.male.plot3.sig %>%
  filter(sigRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.RR.all <- meta.male.plot3.sig %>%
  filter(sigRR == 1) %>%
  nest()

# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.male.meta.CVR <- metacombo_male.plot3.CVR %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR <- metacombo_male.plot3.VR %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR <- metacombo_male.plot3.RR %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.male.meta.CVR.all <- metacombo_male.plot3.CVR.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.CVR.all <- plot3.male.meta.CVR.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.VR.all <- metacombo_male.plot3.VR.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.all <- plot3.male.meta.VR.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.RR.all <- metacombo_male.plot3.RR.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.all <- plot3.male.meta.RR.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.male.meta.CVR <- bind_rows(plot3.male.meta.CVR, plot3.male.meta.CVR.all)
plot3.male.meta.VR <- bind_rows(plot3.male.meta.VR, plot3.male.meta.VR.all)
plot3.male.meta.RR <- bind_rows(plot3.male.meta.RR, plot3.male.meta.RR.all)

# **Re-structure data for each grouping term; delete un-used variables

plot3.male.meta.CVR.b <- as.data.frame(plot3.male.meta.CVR %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.b))

plot3.male.meta.CVR.b <- bind_rows(plot3.male.meta.CVR.b, add.row.hearing)
plot3.male.meta.CVR.b <- plot3.male.meta.CVR.b[order(plot3.male.meta.CVR.b$GroupingTerm), ]

plot3.male.meta.VR.b <- as.data.frame(plot3.male.meta.VR %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
plot3.male.meta.VR.b <- plot3.male.meta.VR.b[order(plot3.male.meta.VR.b$GroupingTerm), ]

plot3.male.meta.RR.b <- as.data.frame(plot3.male.meta.RR %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
plot3.male.meta.RR.b <- plot3.male.meta.RR.b[order(plot3.male.meta.RR.b$GroupingTerm), ]

overall.male.plot3 <- full_join(plot3.male.meta.CVR.b, plot3.male.meta.VR.b)
overall.male.plot3 <- full_join(overall.male.plot3, plot3.male.meta.RR.b)

overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm)))

# add missing GroupingTerms for plot
overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Behaviour")
overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Immunology")
overall.male.plot3 <- add_row(overall.male.plot3, GroupingTerm = "Eye")

overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3$GroupingTerm <- factor(overall.male.plot3$GroupingTerm, rev(levels(overall.male.plot3$GroupingTerm)))

# str(overall.male.plot3)
```


Restructure MALE data for plotting 

```{r}
overall3.male.sig <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3.male.sig %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
# lnVR.ci <- overall3.male.sig  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sig %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

overall4.male.sig$label <- "CI not overlapping zero"
```

Plot Fig5b all significant results (CI not overlapping zero) for males

```{r}

Metameta_Fig3_male.sig <- overall4.male.sig %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(0, 0.4),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_male.sig
```

#### Female part, significant traits
Female Fig5B sig

Prepare data for traits with CI not overlapping 0
create column with 1= different from zero, 0= zero included in CI

```{r}

# female-biased traits

meta.female.plot3.sig <- metacombo %>%
  mutate(
    sigCVR = ifelse(lnCVR_upper < 0, 1, 0),
    sigVR = ifelse(lnVR_upper < 0, 1, 0),
    sigRR = ifelse(lnRR_upper < 0, 1, 0)
  )

# Significant subset for lnCVR

metacombo_female.plot3.CVR <- meta.female.plot3.sig %>%
  filter(sigCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_female.plot3.CVR.all <- meta.female.plot3.sig %>%
  filter(sigCVR == 1) %>%
  nest()

# Significant subset for lnVR

metacombo_female.plot3.VR <- meta.female.plot3.sig %>%
  filter(sigVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_female.plot3.VR.all <- meta.female.plot3.sig %>%
  filter(sigVR == 1) %>%
  nest()

# Significant subset for lnRR

metacombo_female.plot3.RR <- meta.female.plot3.sig %>%
  filter(sigRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_female.plot3.RR.all <- meta.female.plot3.sig %>%
  filter(sigRR == 1) %>%
  nest()

# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.female.meta.CVR <- metacombo_female.plot3.CVR %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.VR <- metacombo_female.plot3.VR %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.RR <- metacombo_female.plot3.RR %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.female.meta.CVR.all <- metacombo_female.plot3.CVR.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.CVR.all <- plot3.female.meta.CVR.all %>% mutate(GroupingTerm = "All")

plot3.female.meta.VR.all <- metacombo_female.plot3.VR.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.VR.all <- plot3.female.meta.VR.all %>% mutate(GroupingTerm = "All")

plot3.female.meta.RR.all <- metacombo_female.plot3.RR.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.female.meta.RR.all <- plot3.female.meta.RR.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.female.meta.CVR <- bind_rows(plot3.female.meta.CVR, plot3.female.meta.CVR.all)
plot3.female.meta.VR <- bind_rows(plot3.female.meta.VR, plot3.female.meta.VR.all)
plot3.female.meta.RR <- bind_rows(plot3.female.meta.RR, plot3.female.meta.RR.all)

# **Re-structure data for each grouping term; delete un-used variables

plot3.female.meta.CVR.b <- as.data.frame(plot3.female.meta.CVR %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]

add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.female.meta.CVR.b))

plot3.female.meta.CVR.b <- bind_rows(plot3.female.meta.CVR.b, add.row.hearing)
plot3.female.meta.CVR.b <- plot3.female.meta.CVR.b[order(plot3.female.meta.CVR.b$GroupingTerm), ]

plot3.female.meta.VR.b <- as.data.frame(plot3.female.meta.VR %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]

plot3.female.meta.VR.b <- plot3.female.meta.VR.b[order(plot3.female.meta.VR.b$GroupingTerm), ]

plot3.female.meta.RR.b <- as.data.frame(plot3.female.meta.RR %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]

plot3.female.meta.RR.b <- plot3.female.meta.RR.b[order(plot3.female.meta.RR.b$GroupingTerm), ]

overall.female.plot3 <- full_join(plot3.female.meta.CVR.b, plot3.female.meta.VR.b)
overall.female.plot3 <- full_join(overall.female.plot3, plot3.female.meta.RR.b)

overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.female.plot3$GroupingTerm <- factor(overall.female.plot3$GroupingTerm, rev(levels(overall.female.plot3$GroupingTerm)))
```

Restructure data for plotting

```{r}
overall3.female.sig <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3.female.sig %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
# lnVR.ci <- overall3.female.sig  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.female.sig %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.female.sig <- bind_rows(lnCVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

overall4.female.sig$label <- "CI not overlapping zero"
```

Plotting Fig5B all significant results (CI not overlapping zero, female )

```{r}

Metameta_Fig3_female.sig <- overall4.female.sig %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.4, 0),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_female.sig #(Figure 5B left panel)
```
## JOIN!!! CODE MISSING??



# Supplemental Plots
## Figure S1 
### Including lnVR
### Count data, including lnVR (Fig S1 panel A)

```{r}
# *Prepare data for all traits

meta.plot2.all <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plot2.all.bS1 <- gather(meta.plot2.all, trait, value, c(lnCVR, lnVR, lnRR))

meta.plot2.all.bS1$trait <- factor(meta.plot2.all.bS1$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plot2.all.cS1 <- meta.plot2.all.bS1 %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > 0), femalebias = sum(value <= 0), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.all.cS1$label <- "All traits"

# restructure to create stacked bar plots

meta.plot2.all.dS1 <- as.data.frame(meta.plot2.all.cS1)
meta.plot2.all.eS1 <- gather(meta.plot2.all.dS1, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.all.eS1$samplesize <- with(meta.plot2.all.eS1, ifelse(sex == "malepercent", malebias, femalebias))

malebias_FigS1_alltraits <-
  ggplot(meta.plot2.all.eS1) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.all.eS1, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS1_alltraits     #(panel A in Figure S1)
```

###  Overall results of second order meta analysis, INCLUDING VR
#### Restructure data for plotting 
Restructure MALE data for plotting 

```{r}
overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.male.sigS$label <- "CI not overlapping zero"

# Data are restructured, and grouping terms are being re-ordered

overall3S <- gather(overall2, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3S %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

# re-order Grouping Terms

overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall4S$GroupingTerm <- factor(overall4S$GroupingTerm, rev(levels(overall4S$GroupingTerm)))
overall4S$label <- "All traits"
```

#### Preparation for plot, including lnVR
Preparation: Sub-Plot  for Figure S1: all traits (S1 B)

```{r}
Metameta_FigS1_alltraits <- overall4S %>%

  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.24, 0.25),
    breaks = c(-0.2, -0.1, 0, 0.1, 0.2),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_FigS1_alltraits
```

### Heterogeneity
The analysis for heterogeneity follows the workflow of the above steps for the different meta-analyses. However, in the initial meta-analysis we extract sigma^2 and errors for mouse strains and centers (Institutions). 

```{r}
results.allhetero.grouping <- as.data.frame(cbind(c(1:n), matrix(rep(0, n * 30), ncol = 30)))
names(results.allhetero.grouping) <- c(
  "id", "sigma2_strain.CVR", "sigma2_center.CVR", "sigma2_error.CVR", "s.nlevels.strain.CVR",
  "s.nlevels.center.CVR", "s.nlevels.error.CVR", "sigma2_strain.VR", "sigma2_center.VR", "sigma2_error.VR", "s.nlevels.strain.VR",
  "s.nlevels.center.VR", "s.nlevels.error.VR", "sigma2_strain.RR", "sigma2_center.RR", "sigma2_error.RR", "s.nlevels.strain.RR",
  "s.nlevels.center.RR", "s.nlevels.error.RR", "lnCVR", "lnCVR_lower", "lnCVR_upper", "lnCVR_se", "lnVR", "lnVR_lower", "lnVR_upper",
  "lnVR_se", "lnRR", "lnRR_lower", "lnRR_upper", "lnRR_se"
)
```

LOOP
Parameters to extract from metafor (sigma2's, s.nlevels)

```{r}

for (t in 1:n) {
  tryCatch(
    {
      data_par_age <- data_subset_parameterid_individual_by_age(data, t, age_min = 0, age_center = 100)

      population_stats <- calculate_population_stats(data_par_age)

      results <- create_meta_analysis_effect_sizes(population_stats)

      # lnCVR, logaritm of the ratio of male and female coefficients of variance

      cvr. <- metafor::rma.mv(yi = effect_size_CVR, V = sample_variance_CVR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 2] <- cvr.$sigma2[1]
      results.allhetero.grouping[t, 3] <- cvr.$sigma2[2]
      results.allhetero.grouping[t, 4] <- cvr.$sigma2[3]
      results.allhetero.grouping[t, 5] <- cvr.$s.nlevels[1]
      results.allhetero.grouping[t, 6] <- cvr.$s.nlevels[2]
      results.allhetero.grouping[t, 7] <- cvr.$s.nlevels[3]
      results.allhetero.grouping[t, 20] <- cvr.$b
      results.allhetero.grouping[t, 21] <- cvr.$ci.lb
      results.allhetero.grouping[t, 22] <- cvr.$ci.ub
      results.allhetero.grouping[t, 23] <- cvr.$se

      # lnVR, male to female variability ratio (logarithm of male and female standard deviations)

      vr. <- metafor::rma.mv(yi = effect_size_VR, V = sample_variance_VR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 8] <- vr.$sigma2[1]
      results.allhetero.grouping[t, 9] <- vr.$sigma2[2]
      results.allhetero.grouping[t, 10] <- vr.$sigma2[3]
      results.allhetero.grouping[t, 11] <- vr.$s.nlevels[1]
      results.allhetero.grouping[t, 12] <- vr.$s.nlevels[2]
      results.allhetero.grouping[t, 13] <- vr.$s.nlevels[3]
      results.allhetero.grouping[t, 24] <- vr.$b
      results.allhetero.grouping[t, 25] <- vr.$ci.lb
      results.allhetero.grouping[t, 26] <- vr.$ci.ub
      results.allhetero.grouping[t, 27] <- vr.$se

      # lnRR, response ratio (logarithm of male and female means)

      rr. <- metafor::rma.mv(yi = effect_size_RR, V = sample_variance_RR, random = list(
        ~ 1 | strain_name, ~ 1 | production_center,
        ~ 1 | err
      ), control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), data = results)
      results.allhetero.grouping[t, 14] <- rr.$sigma2[1]
      results.allhetero.grouping[t, 15] <- rr.$sigma2[2]
      results.allhetero.grouping[t, 16] <- rr.$sigma2[3]
      results.allhetero.grouping[t, 17] <- rr.$s.nlevels[1]
      results.allhetero.grouping[t, 18] <- rr.$s.nlevels[2]
      results.allhetero.grouping[t, 19] <- rr.$s.nlevels[3]
      results.allhetero.grouping[t, 28] <- rr.$b
      results.allhetero.grouping[t, 29] <- rr.$ci.lb
      results.allhetero.grouping[t, 30] <- rr.$ci.ub
      results.allhetero.grouping[t, 31] <- rr.$se
    },
    error = function(e) {
      cat("ERROR :", conditionMessage(e), "\n")
    }
  )
}
```

#### Exclude traits, merge datasets

```{r}
results.allhetero.grouping2 <- results.allhetero.grouping[results.allhetero.grouping$s.nlevels.strain.VR != 0, ]
# nrow(results.allhetero.grouping2) #218  SZ 223???
```

Merge data sets containing metafor results with procedure etc. names 

```{r}
# procedures <- read.csv(here("export", "procedures.csv"))

results.allhetero.grouping2$parameter_group <- data$parameter_group[match(results.allhetero.grouping2$id, data$id)]
results.allhetero.grouping2$procedure <- data$procedure_name[match(results.allhetero.grouping2$id, data$id)]

results.allhetero.grouping2$GroupingTerm <- procedures$GroupingTerm[match(results.allhetero.grouping2$procedure, procedures$procedure)]
results.allhetero.grouping2$parameter_name <- data$parameter_name[match(results.allhetero.grouping2$id, data$id)]
```

#### Correlated parameters
##FELIX : check? numbers don't add up??
```{r}
metahetero1 <- results.allhetero.grouping2
# length(unique(metahetero1$procedure)) #18  SZ 19
# length(unique(metahetero1$GroupingTerm)) #9 Sz ok
# length(unique(metahetero1$parameter_group)) # 149 SZ 152
# length(unique(metahetero1$parameter_name)) #218  SZ 223

# Count of number of parameter names (correlated sub-traits) in each parameter group (par_group_size)

metahetero1b <-
  metahetero1 %>%
  group_by(parameter_group) %>%
  mutate(par_group_size = n_distinct(parameter_name))

metahetero1$par_group_size <- metahetero1b$par_group_size[match(metahetero1$parameter_group, metahetero1b$parameter_group)]

# Create subsets with > 1 count (par_group_size > 1)

metahetero1_sub <- subset(metahetero1, par_group_size > 1) # 90 observations
# str(metahetero1_sub)
# metahetero1_sub$sampleSize <- as.numeric(metahetero1_sub$sampleSize) #from previous analysis? don't think is used: : delete in final version

# Nest data

n_count. <- metahetero1_sub %>%
  group_by(parameter_group) %>%
  # mutate(raw_N = sum(sampleSize)) %>%  #don't think is necessary: delete in final version
  nest()

# meta-analysis preparation

model_count. <- n_count. %>%
  mutate(
    model_lnRR = map(data, ~ robu(.x$lnRR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnRR_se)^2
    )),
    model_lnVR = map(data, ~ robu(.x$lnVR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnVR_se)^2
    )),
    model_lnCVR = map(data, ~ robu(.x$lnCVR ~ 1,
      data = .x, studynum = .x$id, modelweights = c("CORR"), rho = 0.8,
      small = TRUE, var.eff.size = (.x$lnCVR_se)^2
    ))
  )


# Robumeta object details:
# str(model_count.$model_lnCVR[[1]])

## *Perform meta-analyses on correlated sub-traits, using robumeta
 # Susi / FELIX: what's this below?
# Shinichi: We think we want to use these for further analyses:
# residual variance: as.numeric(robu_fit$mod_info$term1)     (same as 'mod_info$tau.sq')
# sample size: robu_fit$N

## **Extract and save parameter estimates

# Felix: doesn't work , error message:
#!!!!!!!!!!! ERROR!!!!!!!!!!!!!!!!!!!!
#Error: Column `parameter_group` can't be modified because it's a grouping variable

count_fun. <- function(mod_sub) {
  return(c(as.numeric(mod_sub$mod_info$term1), mod_sub$N))
}

robusub_RR. <- model_count. %>%
  transmute(parameter_group, estimatelnRR = map(model_lnRR, count_fun.)) %>%
  mutate(r = map(estimatelnRR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnRR) %>%
  purrr::set_names(c("parameter_group", "var.RR", "N.RR"))

robusub_CVR. <- model_count. %>%
  transmute(parameter_group, estimatelnCVR = map(model_lnCVR, count_fun.)) %>%
  mutate(r = map(estimatelnCVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnCVR) %>%
  purrr::set_names(c("parameter_group", "var.CVR", "N.CVR"))

robusub_VR. <- model_count. %>%
  transmute(parameter_group, estimatelnVR = map(model_lnVR, count_fun.)) %>%
  mutate(r = map(estimatelnVR, ~ data.frame(t(.)))) %>%
  unnest(r) %>%
  select(-estimatelnVR) %>%
  purrr::set_names(c("parameter_group", "var.VR", "N.VR"))

robu_all. <- full_join(robusub_CVR., robusub_VR.) %>% full_join(., robusub_RR.)
```

Merge the two data sets (the new [robu_all.] and the initial [uncorrelated sub-traits with count = 1])

In this step, we 	
1) merge the N from robumeta and the  N from metafor (s.nlevels.error) together into the same columns (N.RR, N.VR, N.CVR)
2) calculate the total variance for metafor models as the sum of random effect variances and the residual error, then add in the same columns together with the residual variances from robumeta

```{r}
metahetero_all <- metahetero1 %>%
  filter(par_group_size == 1) %>%
  as_tibble()
metahetero_all$N.RR <- metahetero_all$s.nlevels.error.RR
metahetero_all$N.CVR <- metahetero_all$s.nlevels.error.CVR
metahetero_all$N.VR <- metahetero_all$s.nlevels.error.VR
metahetero_all$var.RR <- log(sqrt(metahetero_all$sigma2_strain.RR + metahetero_all$sigma2_center.RR + metahetero_all$sigma2_error.RR))
metahetero_all$var.VR <- log(sqrt(metahetero_all$sigma2_strain.VR + metahetero_all$sigma2_center.VR + metahetero_all$sigma2_error.VR))
metahetero_all$var.CVR <- log(sqrt(metahetero_all$sigma2_strain.CVR + metahetero_all$sigma2_center.CVR + metahetero_all$sigma2_error.CVR))
# str(metahetero_all)
# str(robu_all.)

metahetero_all <- metahetero_all %>% mutate(
  var.RR = if_else(var.RR == -Inf, -7, var.RR),
  var.VR = if_else(var.VR == -Inf, -5, var.VR),
  var.CVR = if_else(var.CVR == -Inf, -6, var.CVR)
)

# **Combine data
## Step1
combinedmetahetero <- bind_rows(robu_all., metahetero_all)
# glimpse(combinedmetahetero)

# Steps 2&3

metacombohetero <- combinedmetahetero
metacombohetero$counts <- metahetero1$par_group_size[match(metacombohetero$parameter_group, metahetero1$parameter_group)]
metacombohetero$procedure2 <- metahetero1$procedure[match(metacombohetero$parameter_group, metahetero1$parameter_group)]
metacombohetero$GroupingTerm2 <- metahetero1$GroupingTerm[match(metacombohetero$parameter_group, metahetero1$parameter_group)]

# **Clean-up and rename

metacombohetero <- metacombohetero[, c(1:7, 43:45)]
names(metacombohetero)[9] <- "procedure"
names(metacombohetero)[10] <- "GroupingTerm"
```

#### Meta-analysis of heterogeneity

```{r}
## Perform meta-meta-analysis (3 for each of the 9 grouping terms: var.CVR, var.VR, var.RR)

metacombohetero_final <- metacombohetero %>%
  group_by(GroupingTerm) %>%
  nest()

# Final fixed effects meta-analyses within grouping terms, with SE of the estimate

# metacombohetero$var.CVR

heterog1 <- metacombohetero_final %>%

  mutate(
    model_heteroCVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.CVR, sei = sqrt(1 / 2 * (.x$N.CVR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroVR = map(data, ~ metafor::rma.uni(
      yi = .x$var.VR, sei = sqrt(1 / 2 * (.x$N.VR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    )),
    model_heteroRR = map(data, ~ metafor::rma.uni(
      yi = .x$var.RR, sei = sqrt(1 / 2 * (.x$N.RR - 1)),
      control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 10000, stepadj = 0.5), verbose = F
    ))
  )


# Re-structure data for each grouping term; extract heterogenenity/variance terms; delete un-used variables
## FELIX: ADD "ALL"
Behaviour. <- heterog1 %>%
  filter(., GroupingTerm == "Behaviour") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Immunology. <- heterog1 %>%
  filter(., GroupingTerm == "Immunology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)


Hematology. <- heterog1 %>%
  filter(., GroupingTerm == "Hematology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)


Hearing. <- heterog1 %>%
  filter(., GroupingTerm == "Hearing") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Physiology. <- heterog1 %>%
  filter(., GroupingTerm == "Physiology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Metabolism. <- heterog1 %>%
  filter(., GroupingTerm == "Metabolism") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Morphology. <- heterog1 %>%
  filter(., GroupingTerm == "Morphology") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Heart. <- heterog1 %>%
  filter(., GroupingTerm == "Heart") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

Eye. <- heterog1 %>%
  filter(., GroupingTerm == "Eye") %>%
  select(., -data) %>%
  mutate(
    heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se,
    heteroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
    heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
  ) %>%
  select(., GroupingTerm, heteroCVR:heteroRR_se)

 #FELIX: check.I have added / modifie this can't run to check
All. <- as.data.frame(heterog1 %>% 
      mutate(
   heteroCVR = .[[2]][[1]]$b, heteroCVR_lower = .[[2]][[1]]$ci.lb, heteroCVR_upper = .[[2]][[1]]$ci.ub, heteroCVR_se = .[[2]][[1]]$se, heeroVR = .[[3]][[1]]$b, heteroVR_lower = .[[3]][[1]]$ci.lb, heteroVR_upper = .[[3]][[1]]$ci.ub, heteroVR_se = .[[3]][[1]]$se,
  heteroRR = .[[4]][[1]]$b, heteroRR_lower = .[[4]][[1]]$ci.lb, heteroRR_upper = .[[4]][[1]]$ci.ub, heteroRR_se = .[[4]][[1]]$se
))[, c(5:16)]

All.$heteroCVR <- as.numeric(All.$heteroCVR)
All.$heteroVR <- as.numeric(All.$heteroVR)
All.$heteroRR <- as.numeric(All.$lheteroRR)
All. <- All. %>% mutate(GroupingTerm = "All")

heterog2 <- bind_rows(Behaviour., Morphology., Metabolism., Physiology., Immunology., Hematology., Heart., Hearing., Eye., All.)
# str(heterog2)
```

#### Heterogeneity PLOT
Restructure data for plotting 

```{r}
heterog3 <- gather(heterog2, parameter, value, c(heteroCVR, heteroVR, heteroRR), factor_key = TRUE)

heteroCVR.ci <- heterog3 %>%
  filter(parameter == "heteroCVR") %>%
  mutate(ci.low = heteroCVR_lower, ci.high = heteroCVR_upper)
heteroVR.ci <- heterog3 %>%
  filter(parameter == "heteroVR") %>%
  mutate(ci.low = heteroVR_lower, ci.high = heteroVR_upper)
heteroRR.ci <- heterog3 %>%
  filter(parameter == "heteroRR") %>%
  mutate(ci.low = heteroRR_lower, ci.high = heteroRR_upper)

heterog4 <- bind_rows(heteroCVR.ci, heteroVR.ci, heteroRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

# **Re-order grouping terms

heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye"))
heterog4$GroupingTerm <- factor(heterog4$GroupingTerm, rev(levels(heterog4$GroupingTerm)))
heterog4$label <- "All traits"
# write.csv(heterog4, "heterog4.csv")
```

#### Plot S1 C (Second-order meta analysis on heterogeneity)

```{r}
heterog5 <- heterog4
heterog5$mean <- as.numeric(exp(heterog5$value))
heterog5$ci.l <- as.numeric(exp(heterog5$ci.low))
heterog5$ci.h <- as.numeric(exp(heterog5$ci.high))

heterog6 <- heterog5

HeteroS1 <-
  heterog6 %>%
  filter(
    parameter == "heteroCVR" | parameter == "heteroRR"
  ) %>%  ## FELIX : do we need this "filter" - we want VR as well??
  ggplot(aes(y = GroupingTerm, x = mean)) +
  geom_errorbarh(aes(
    xmin = ci.l,
    xmax = ci.h
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "black",
    color = "black", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.1, 1.4),
    # breaks = c(0, 0.1, 0.2),
    name = "sigma^2"
  ) +
  # geom_vline(xintercept=0,
  # color='black',
  # linetype='dashed')+
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# HeteroS1

```


#### Combined Figure S1: overall Count data, Meta anlysis results, Heterogeneity)

```{r}
FigS1 <- ggarrange(malebias_FigS1_alltraits + xlab("percentage sex bias"), Metameta_FigS1_alltraits, HeteroS1, nrow = 3, align = "v", heights = c(1, 1, 1), labels = c("A", "B", "C"))
FigS1
# ggsave("FigS1_OverallResults.pdf", plot = Fig4, width = 6, height = 5)
```

## Figure S2

Plot FigS2 all significant results (CI not overlapping zero) for males
### FELIX:  "ALL" missing. 
```{r}
meta.plot2.sig.bS <- meta.plot2.sig[, c("lnCVR", "lnVR", "lnRR", "lnCVRsig", "lnVRsig", "lnRRsig", "GroupingTerm")]

meta.plot2.sig.cS <- gather(meta.plot2.sig.bS, trait, value, lnCVR:lnRR)
meta.plot2.sig.cS$sig <- "placeholder"

meta.plot2.sig.cS$trait <- factor(meta.plot2.sig.cS$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plot2.sig.cS$sig <- ifelse(meta.plot2.sig.cS$trait == "lnCVR", meta.plot2.sig.cS$lnCVRsig,
  ifelse(meta.plot2.sig.cS$trait == "lnVR", meta.plot2.sig.cS$lnVRsig, meta.plot2.sig.cS$lnRRsig)
)

# choosing sex biased ln-ratios significantly larger than 0
meta.plotS2.sig.malebias <- meta.plot2.sig.cS %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  filter(sig == 1) %>%
  summarise(male_sig = sum(value > 0), female_sig = sum(value < 0), total = male_sig + female_sig)

meta.plotS2.sig.malebias <- ungroup(meta.plotS2.sig.malebias) %>%
  add_row(trait = "lnCVR", GroupingTerm = "Hearing", male_sig = 0, female_sig = 0, .before = 4) %>% # add "Hearing" for lnCVR (not filtered as only zeros)
  mutate(malepercent = male_sig * 100 / total, femalepercent = female_sig * 100 / total)

meta.plotS2.sig.malebias$label <- "CI not overlapping zero"

# restructure to create stacked bar plots

meta.plotS2.sig.bothsexes <- as.data.frame(meta.plotS2.sig.malebias)
meta.plotS2.sig.bothsexes.b <- gather(meta.plotS2.sig.bothsexes, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plotS2.sig.bothsexes.b$samplesize <- with(meta.plotS2.sig.bothsexes.b, ifelse(sex == "malepercent", male_sig, female_sig))

# *Plot Fig2 all significant results (CI not overlapping zero):
#     no sig. lnCVR for 'Hearing' in either sex; no sig. male-biased lnCVR for 'Immunology' and 'Eye, and no sig. male-biased lnVR for 'Eye'


malebias_FigS2_sigtraits <-
  ggplot(meta.plotS2.sig.bothsexes.b) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plotS2.sig.bothsexes.b, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS2_sigtraits # this is Figure S2 A
```
### Prepare data for traits with effect size ratios > 10% larger in males, supplemental Figure S2
### FELIX:  "ALL" missing. 
This Figure extends Figure 4, as it includes results not only for lnCVR and lnRR but also lnCVR. In addition, we compare two different assessments of sex-bias, significance (CI not overlapping zero) and sex differences in male / female ratios > 10%

### Over 10% male bias, count data (first- order metanalysis) 
```{r}
meta.plot2.over10 <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm) 

meta.plot2.over10.b <- gather(meta.plot2.over10, trait, value, c(lnCVR, lnVR, lnRR)) 

meta.plot2.over10.b$trait <- factor(meta.plot2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR")) 

meta.plot2.over10.c <- meta.plot2.over10.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plot2.over10.c$label <- "Sex difference in m/f ratios > 10%"

# restructure to create stacked bar plots

meta.plot2.over10.c <- as.data.frame(meta.plot2.over10.c)
meta.plot2.over10.d <- gather(meta.plot2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plot2.over10.d$samplesize <- with(meta.plot2.over10.d, ifelse(sex == "malepercent", malebias, femalebias))

# *Plot Fig2 Sex difference in m/f ratio > 10%
malebias_Fig2_over10 <-
  ggplot(meta.plot2.over10.d) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_Fig2_over10  (supplemental Figure S2)
```

#### Fig S2, second-order meta-analysis, male traits
#### Female Figure, significant traits
Female FigS2 B sig

Prepare data for traits with CI not overlapping 0
create column with 1= different from zero, 0= zero included in CI


Restructure data for plotting

```{r}
overall3.female.sigS <- gather(overall.female.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)

lnCVR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.female.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.female.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.female.sigS$label <- "CI not overlapping zero"

##

Metameta_FigS2_female.sig <- overall4.female.sigS %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.4, 0),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_female.sig
```

Prepare data for traits with m/f difference > 10%

Create column with 1= larger, 0= difference not larger than 10% between male/female ratios
```{r}
meta.male.plot3.perc <- metacombo %>%
  mutate(
    percCVR = ifelse(lnCVR > log(11 / 10), 1, 0),
    percVR = ifelse(lnVR > log(11 / 10), 1, 0),
    percRR = ifelse(lnRR > log(11 / 10), 1, 0)
  )

# Significant subset for lnCVR
metacombo_male.plot3.CVR.perc <- meta.male.plot3.perc %>%
  filter(percCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.CVR.perc.all <- meta.male.plot3.perc %>%
  filter(percCVR == 1) %>%
  nest()

# Significant subset for lnVR
metacombo_male.plot3.VR.perc <- meta.male.plot3.perc %>%
  filter(percVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.VR.perc.all <- meta.male.plot3.perc %>%
  filter(percVR == 1) %>%
  nest()

# Significant subset for lnRR
metacombo_male.plot3.RR.perc <- meta.male.plot3.perc %>%
  filter(percRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_male.plot3.RR.perc.all <- meta.male.plot3.perc %>%
  filter(percRR == 1) %>%
  nest()


# **Final fixed effects meta-analyses within grouping terms and across grouping terms, with SE of the estimate

plot3.male.meta.CVR.perc <- metacombo_male.plot3.CVR.perc %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.perc <- metacombo_male.plot3.VR.perc %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.perc <- metacombo_male.plot3.RR.perc %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.male.meta.CVR.perc.all <- metacombo_male.plot3.CVR.perc.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.CVR.perc.all <- plot3.male.meta.CVR.perc.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.VR.perc.all <- metacombo_male.plot3.VR.perc.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.VR.perc.all <- plot3.male.meta.VR.perc.all %>% mutate(GroupingTerm = "All")

plot3.male.meta.RR.perc.all <- metacombo_male.plot3.RR.perc.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.male.meta.RR.perc.all <- plot3.male.meta.RR.perc.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.male.meta.CVR.perc <- bind_rows(plot3.male.meta.CVR.perc, plot3.male.meta.CVR.perc.all)
plot3.male.meta.VR.perc <- bind_rows(plot3.male.meta.VR.perc, plot3.male.meta.VR.perc.all)
plot3.male.meta.RR.perc <- bind_rows(plot3.male.meta.RR.perc, plot3.male.meta.RR.perc.all)


# **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"

plot3.male.meta.CVR.perc.b <- as.data.frame(plot3.male.meta.CVR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.CVR.perc.b))
plot3.male.meta.CVR.perc.b <- rbind(plot3.male.meta.CVR.perc.b, add.row.hearing)
plot3.male.meta.CVR.perc.b <- plot3.male.meta.CVR.perc.b[order(plot3.male.meta.CVR.perc.b$GroupingTerm), ]

plot3.male.meta.VR.perc.b <- as.data.frame(plot3.male.meta.VR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.male.meta.VR.perc.b))
plot3.male.meta.VR.perc.b <- rbind(plot3.male.meta.VR.perc.b, add.row.hearing)
plot3.male.meta.VR.perc.b <- plot3.male.meta.VR.perc.b[order(plot3.male.meta.VR.perc.b$GroupingTerm), ]

plot3.male.meta.RR.perc.b <- as.data.frame(plot3.male.meta.RR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>%
  setNames(names(plot3.male.meta.RR.perc.b))
plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.hearing)

add.row.eye <- as.data.frame(t(c("Eye", NA, NA, NA, NA))) %>%
  setNames(names(plot3.male.meta.RR.perc.b))
plot3.male.meta.RR.perc.b <- rbind(plot3.male.meta.RR.perc.b, add.row.eye)

plot3.male.meta.RR.perc.b <- plot3.male.meta.RR.perc.b[order(plot3.male.meta.RR.perc.b$GroupingTerm), ]

plot3.male.meta.CVR.Vr.perc <- full_join(plot3.male.meta.CVR.perc.b, plot3.male.meta.VR.perc.b)
overall.male.plot3.perc <- full_join(plot3.male.meta.CVR.Vr.perc, plot3.male.meta.RR.perc.b)


overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.male.plot3.perc$GroupingTerm <- factor(overall.male.plot3.perc$GroupingTerm, rev(levels(overall.male.plot3.perc$GroupingTerm)))
```

Restructure data for plotting : Male biased, 10% difference

```{r}
overall3.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.perc  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.perc <- bind_rows(lnCVR.ci,lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) 

overall4.male.perc$label <- "Sex difference in m/f ratios > 10%"

overall4.male.perc$value <- as.numeric(overall4.male.perc$value)
overall4.male.perc$ci.low <- as.numeric(overall4.male.perc$ci.low)
overall4.male.perc$ci.high <- as.numeric(overall4.male.perc$ci.high)
```

Plot Fig S2 all >10% difference (male bias)

```{r}

Metameta_Fig3_male.perc <- overall4.male.perc %>% # filter(., GroupingTerm != "Hearing") %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(
    shape = parameter,
    fill = parameter
  ),
  color = "mediumaquamarine", size = 2.2,
  show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.2, 0.62),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_male.perc (Figure S2 right panel)
```

#### Female Fig S2 >10%

```{r}

meta.plot3.perc <- metacombo %>%
  mutate(
    percCVR = ifelse(lnCVR < log(9 / 10), 1, 0),
    percVR = ifelse(lnVR < log(9 / 10), 1, 0),
    percRR = ifelse(lnRR < log(9 / 10), 1, 0)
  )

# Significant subset for lnCVR
metacombo_plot3.CVR.perc <- meta.plot3.perc %>%
  filter(percCVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.CVR.perc.all <- meta.plot3.perc %>%
  filter(percCVR == 1) %>%
  nest()

# Significant subset for lnVR
metacombo_plot3.VR.perc <- meta.plot3.perc %>%
  filter(percVR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.VR.perc.all <- meta.plot3.perc %>%
  filter(percVR == 1) %>%
  nest()

# Significant subset for lnRR
metacombo_plot3.RR.perc <- meta.plot3.perc %>%
  filter(percRR == 1) %>%
  group_by(GroupingTerm) %>%
  nest()

metacombo_plot3.RR.perc.all <- meta.plot3.perc %>%
  filter(percRR == 1) %>%
  nest()


# **Final fixed effects meta-analyses within grouping terms, with SE of the estimate

plot3.meta.CVR.perc <- metacombo_plot3.CVR.perc %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.VR.perc <- metacombo_plot3.VR.perc %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.RR.perc <- metacombo_plot3.RR.perc %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

# Across all grouping terms #

plot3.meta.CVR.perc.all <- metacombo_plot3.CVR.perc.all %>%
  mutate(model_lnCVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnCVR, sei = (.x$lnCVR_upper - .x$lnCVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.CVR.perc.all <- plot3.meta.CVR.perc.all %>% mutate(GroupingTerm = "All")

plot3.meta.VR.perc.all <- metacombo_plot3.VR.perc.all %>%
  mutate(model_lnVR = map(data, ~ metafor::rma.uni(
    yi = .x$lnVR, sei = (.x$lnVR_upper - .x$lnVR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.VR.perc.all <- plot3.meta.VR.perc.all %>% mutate(GroupingTerm = "All")

plot3.meta.RR.perc.all <- metacombo_plot3.RR.perc.all %>%
  mutate(model_lnRR = map(data, ~ metafor::rma.uni(
    yi = .x$lnRR, sei = (.x$lnRR_upper - .x$lnRR_lower) / (2 * 1.96),
    control = list(optimizer = "optim", optmethod = "Nelder-Mead", maxit = 1000), verbose = F
  )))

plot3.meta.RR.perc.all <- plot3.meta.RR.perc.all %>% mutate(GroupingTerm = "All")

# Combine with separate grouping term results

plot3.meta.CVR.perc <- bind_rows(plot3.meta.CVR.perc, plot3.meta.CVR.perc.all)
plot3.meta.VR.perc <- bind_rows(plot3.meta.VR.perc, plot3.meta.VR.perc.all)
plot3.meta.RR.perc <- bind_rows(plot3.meta.RR.perc, plot3.meta.RR.perc.all)


# **Re-structure data for each grouping term; delete un-used variables: "Hearing missing for all 3 parameters"

plot3.meta.CVR.perc.b <- as.data.frame(plot3.meta.CVR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnCVR = map_dbl(model_lnCVR, pluck(2)), lnCVR_lower = map_dbl(model_lnCVR, pluck(6)),
    lnCVR_upper = map_dbl(model_lnCVR, pluck(7)), lnCVR_se = map_dbl(model_lnCVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.CVR.perc.b))
plot3.meta.CVR.perc.b <- rbind(plot3.meta.CVR.perc.b, add.row.hearing)
plot3.meta.CVR.perc.b <- plot3.meta.CVR.perc.b[order(plot3.meta.CVR.perc.b$GroupingTerm), ]

plot3.meta.VR.perc.b <- as.data.frame(plot3.meta.VR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnVR = map_dbl(model_lnVR, pluck(2)), lnVR_lower = map_dbl(model_lnVR, pluck(6)),
    lnVR_upper = map_dbl(model_lnVR, pluck(7)), lnVR_se = map_dbl(model_lnVR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.VR.perc.b))
plot3.meta.VR.perc.b <- rbind(plot3.meta.VR.perc.b, add.row.hearing)
plot3.meta.VR.perc.b <- plot3.meta.VR.perc.b[order(plot3.meta.VR.perc.b$GroupingTerm), ]

plot3.meta.RR.perc.b <- as.data.frame(plot3.meta.RR.perc %>% group_by(GroupingTerm) %>%
  mutate(
    lnRR = map_dbl(model_lnRR, pluck(2)), lnRR_lower = map_dbl(model_lnRR, pluck(6)),
    lnRR_upper = map_dbl(model_lnRR, pluck(7)), lnRR_se = map_dbl(model_lnRR, pluck(3))
  ))[, c(1, 4:7)]
add.row.hearing <- as.data.frame(t(c("Hearing", NA, NA, NA, NA))) %>% setNames(names(plot3.meta.RR.perc.b))
plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hearing)
add.row.hematology <- as.data.frame(t(c("Hematology", NA, NA, NA, NA))) %>%
  setNames(names(plot3.meta.RR.perc.b))
plot3.meta.RR.perc.b <- rbind(plot3.meta.RR.perc.b, add.row.hematology)


plot3.meta.RR.perc.b <- plot3.meta.RR.perc.b[order(plot3.meta.RR.perc.b$GroupingTerm), ]

plot3.meta.CVR.perc.c <- full_join(plot3.meta.CVR.perc.b, plot3.meta.VR.perc.b)
overall.plot3.perc <- full_join(plot3.meta.CVR.perc.c, plot3.meta.RR.perc.b)


overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, levels = c("Behaviour", "Morphology", "Metabolism", "Physiology", "Immunology", "Hematology", "Heart", "Hearing", "Eye", "All"))
overall.plot3.perc$GroupingTerm <- factor(overall.plot3.perc$GroupingTerm, rev(levels(overall.plot3.perc$GroupingTerm)))
```

Restructure data for plotting
Female bias, 10 percent difference

```{r}
overall3.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) 

lnCVR.ci <- overall3.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.perc  %>% filter(parameter == "lnVR") %>% mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.perc$label <- "Sex difference in m/f ratios > 10%"

overall4.perc$value <- as.numeric(overall4.perc$value)
overall4.perc$ci.low <- as.numeric(overall4.perc$ci.low)
overall4.perc$ci.high <- as.numeric(overall4.perc$ci.high)
```

Plot FigS2 all >10% difference (female)

```{r}
Metameta_Fig3_female.perc <- overall4.perc %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +

  # scale_shape_manual(values =

  scale_x_continuous(
    limits = c(-0.53, 0.2),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3_female.perc (Figure 5D left panel)
```

#### Plot Fig S2:   plots combined
```{r}
library(ggpubr)
FigS2b <- ggarrange(Metameta_Fig3_female.sig, Metameta_Fig3_male.sig,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

FigS2d <- ggarrange(Metameta_Fig3_female.perc, Metameta_Fig3_male.perc,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

# end combination Figure 5
FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_Fig2_over10, FigS2b, FigS2d, ncol = 1, nrow = 4, heights = c(2.3, 2, 2.1, 2), labels = c("A", " ", "B", " "))
FigS2
```

## NOT SURE WHAT THIS BELOW IS??

## Figure S2: sex-bias, including VR

Prepare data for traits with effect size ratios > 10% larger in males

```{r}
meta.plotS2.over10 <- meta_clean %>%
  select(lnCVR, lnVR, lnRR, GroupingTerm) %>%
  arrange(GroupingTerm)

meta.plotS2.over10.b <- gather(meta.plotS2.over10, trait, value, c(lnCVR, lnVR, lnRR))

meta.plotS2.over10.b$trait <- factor(meta.plotS2.over10.b$trait, levels = c("lnCVR", "lnVR", "lnRR"))

meta.plotS2.over10.c <- meta.plotS2.over10.b %>%
  group_by_at(vars(trait, GroupingTerm)) %>%
  summarise(
    malebias = sum(value > log(11 / 10)), femalebias = sum(value < log(9 / 10)), total = malebias + femalebias,
    malepercent = malebias * 100 / total, femalepercent = femalebias * 100 / total
  )

meta.plotS2.over10.c$label <- "Sex difference in m/f ratios > 10%"

# restructure to create stacked bar plots

meta.plotS2.over10.c <- as.data.frame(meta.plotS2.over10.c)
meta.plotS2.over10.d <- gather(meta.plotS2.over10.c, key = sex, value = percent, malepercent:femalepercent, factor_key = TRUE)

# create new sample size variable

meta.plotS2.over10.d$samplesize <- with(meta.plotS2.over10.d, ifelse(sex == "malepercent", malebias, femalebias))

# *Plot FigS2 Sex difference in m/f ratio > 10%
malebias_FigS2_over10 <-
  ggplot(meta.plotS2.over10.d) +
  aes(x = GroupingTerm, y = percent, fill = sex) +
  geom_col() +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") +
  geom_text(
    data = subset(meta.plot2.over10.d, samplesize != 0), aes(label = samplesize), position = position_stack(vjust = .5),
    color = "white", size = 3.5
  ) +
  facet_grid(
    cols = vars(trait), rows = vars(label), labeller = label_wrap_gen(width = 18),
    scales = "free", space = "free"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme_bw(base_size = 18) +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.position = "none",
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  ) +
  coord_flip()

# malebias_FigS2_over10  #(Panel B in Fig S2 in ms)
```



#Metameta_FigS2_male.sig (Figure 5B right panel)

Restructure MALE data for plotting 

```{r}
overall3.male.sigS <- gather(overall.male.plot3, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE)


lnCVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3.male.sigS %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4.male.sigS <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4.male.sigS$label <- "CI not overlapping zero"
```

Plot FigS2 all significant results (CI not overlapping zero, male )

```{r}
Metameta_FigS2_male.sig <- overall4.male.sigS %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "mediumaquamarine", color = "mediumaquamarine", size = 2.2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(0, 0.4),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_text(size = 12),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_male.sig
```

### 10 % Perc sex difference, male bias
Restructure data for plotting : Male biased, 10% difference

```{r}
overall3S.perc <- gather(overall.male.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3S.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S.perc %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S.male.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high) # lnVR.ci,

overall4S.male.perc$label <- "Sex difference in m/f ratios > 10%"

overall4S.male.perc$value <- as.numeric(overall4S.male.perc$value)
overall4S.male.perc$ci.low <- as.numeric(overall4S.male.perc$ci.low)
overall4S.male.perc$ci.high <- as.numeric(overall4S.male.perc$ci.high)
```

Plot FigS2  all >10% difference (male bias)

```{r}
Metameta_FigS2_male.perc <- overall4S.male.perc %>% # filter(., GroupingTerm != "Hearing") %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(
    shape = parameter,
    fill = parameter
  ),
  color = "mediumaquamarine", size = 2.2,
  show.legend = FALSE
  ) +
  scale_x_continuous(
    limits = c(-0.2, 0.62),
    breaks = c(0, 0.3),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), rows = vars(label),
    labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_FigS2_male.perc (Figure 5D right panel)
```

Restructure data for plotting: 
Female bias, 10 percent difference, including VR

```{r}
overall3S.perc <- gather(overall.plot3.perc, parameter, value, c(lnCVR, lnVR, lnRR), factor_key = TRUE) # lnVR,

lnCVR.ci <- overall3S.perc %>%
  filter(parameter == "lnCVR") %>%
  mutate(ci.low = lnCVR_lower, ci.high = lnCVR_upper)
lnVR.ci <- overall3S.perc %>%
  filter(parameter == "lnVR") %>%
  mutate(ci.low = lnVR_lower, ci.high = lnVR_upper)
lnRR.ci <- overall3S.perc %>%
  filter(parameter == "lnRR") %>%
  mutate(ci.low = lnRR_lower, ci.high = lnRR_upper)

overall4S.perc <- bind_rows(lnCVR.ci, lnVR.ci, lnRR.ci) %>% select(GroupingTerm, parameter, value, ci.low, ci.high)

overall4S.perc$label <- "Sex difference in m/f ratios > 10%"

overall4S.perc$value <- as.numeric(overall4S.perc$value)
overall4S.perc$ci.low <- as.numeric(overall4S.perc$ci.low)
overall4S.perc$ci.high <- as.numeric(overall4S.perc$ci.high)
```

Plot Fig5D all >10% difference (female)

```{r}
Metameta_Fig3S_female.perc <- overall4S.perc %>%
  ggplot(aes(y = GroupingTerm, x = value)) +
  geom_errorbarh(aes(
    xmin = ci.low,
    xmax = ci.high
  ),
  height = 0.1, show.legend = FALSE
  ) +
  geom_point(aes(shape = parameter),
    fill = "salmon1", color = "salmon1", size = 2.2,
    show.legend = FALSE
  ) +

  # scale_shape_manual(values =

  scale_x_continuous(
    limits = c(-0.53, 0.2),
    breaks = c(-0.3, 0),
    name = "Effect size"
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linetype = "dashed"
  ) +
  facet_grid(
    cols = vars(parameter), # rows = vars(label),
    # labeller = label_wrap_gen(width = 23),
    scales = "free",
    space = "free"
  ) +
  theme_bw() +
  theme(
    strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15, r = 15, b = 15, l = 15)),
    strip.text.x = element_blank(),
    strip.background = element_rect(colour = NULL, linetype = "blank", fill = "gray90"),
    text = element_text(size = 14),
    panel.spacing = unit(0.5, "lines"),
    panel.border = element_blank(),
    axis.line = element_line(),
    panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
    panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
    panel.grid.minor.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    legend.title = element_blank(),
    axis.title.x = element_text(hjust = 0.5, size = 14),
    axis.title.y = element_blank()
  )

# Metameta_Fig3S_female.perc (Figure 5D left panel)
```

Figure S2 

```{r}
FigS2c <- ggarrange(Metameta_FigS2_female.sig, Metameta_FigS2_male.sig,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

FigS2d <- ggarrange(Metameta_Fig3S_female.perc, Metameta_FigS2_male.perc,
  ncol = 2, nrow = 1, widths = c(1, 1.20), heights = c(1, 1)
)

# end combination Figure 5

FigS2 <- ggarrange(malebias_FigS2_sigtraits, malebias_FigS2_over10, FigS2c, FigS2d, ncol = 1, nrow = 4, heights = c(2.2, 2, 2.2, 2), labels = c("A", " ", "B", " "))
FigS2
```

## Acknowledgements
tbd

## R Session Information

```{r}
sessionInfo()
```

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