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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<title> Helpers</title>
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<div class="col-md-4" style="background:black;text-align:center"><a href="index.html" style="color:white"><i class="glyphicon glyphicon-th-list"></i> Back to index</a></div>
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<div id="helper-functions-used-throughout" class="section level1">
<h1>Helper functions used throughout</h1>
<p>documentation on the functions is interspersed through code comments</p>
<div id="set-some-options" class="section level5">
<h5>set some options</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># library = function (...) suppressMessages(base::library(...))</span>
<span class="co"># This should always be the default. Google for reason if not clear.</span>
<span class="kw">options</span>(<span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)
<span class="kw">options</span>(<span class="dt">digits =</span> <span class="dv">4</span>)
<span class="kw">library</span>(rmarkdown); <span class="kw">library</span>(formatR); <span class="kw">library</span>(foreign); <span class="kw">library</span>(Hmisc); <span class="kw">library</span>(car); <span class="kw">library</span>(psych); <span class="kw">library</span>(QuantPsyc); <span class="kw">library</span>(formr); <span class="kw">library</span>(ggplot2); <span class="kw">library</span>(lubridate); <span class="kw">library</span>(stringr); <span class="kw">library</span>(stringi); <span class="kw">library</span>(reshape2); <span class="kw">library</span>(plyr); <span class="kw">library</span>(knitr); <span class="kw">library</span>(data.table); <span class="kw">library</span>(dplyr); <span class="kw">library</span>(dtplyr); <span class="kw">library</span>(broom)
<span class="kw">library</span>(pander)
<span class="co"># I'm tired of data table messing up my rmarkdown. Never want to print this in a live report anyway, I'm using pander for that.</span>
<span class="kw">options</span>(<span class="dt">datatable.print.nrows=</span><span class="dv">0</span>)</code></pre></div>
</div>
<div id="always-use-these-settings" class="section level4">
<h4>always use these settings</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">analysis_theme =<span class="st"> </span><span class="kw">theme_minimal</span>(<span class="dt">base_size =</span> <span class="dv">24</span>)
<span class="kw">theme_set</span>(analysis_theme)</code></pre></div>
<p>Auto-adjust the table column alignment depending on data type.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">alignment =<span class="st"> </span>function (...) <span class="kw">UseMethod</span>(<span class="st">'alignment'</span>)
alignment.default =<span class="st"> </span>function (...) <span class="st">'left'</span>
alignment.integer =<span class="st"> </span>function (...) <span class="st">'right'</span>
alignment.numeric =<span class="st"> </span>function (...) <span class="st">'right'</span>
<span class="kw">panderOptions</span>(<span class="st">"table.split.table"</span>, <span class="ot">Inf</span>)</code></pre></div>
</div>
<div id="two-simple-functions-to-compute-coefficients-of-variation" class="section level4">
<h4>two simple functions to compute coefficients of variation</h4>
<div id="binary-vars" class="section level5">
<h5>binary vars</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cva_bin =<span class="st"> </span>function(x) {
p =<span class="st"> </span><span class="kw">mean</span>(x,<span class="dt">na.rm=</span>T)
<span class="kw">sqrt</span>(p*(<span class="dv">1</span>-p)) /
<span class="st"> </span>p
}</code></pre></div>
</div>
<div id="continuous-vars" class="section level5">
<h5>continuous vars</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cva =<span class="st"> </span>function(x) {
<span class="kw">sd</span>(x, <span class="dt">na.rm =</span> T) /
<span class="st"> </span><span class="kw">mean</span>(x, <span class="dt">na.rm =</span> T)
}</code></pre></div>
</div>
<div id="shorthand-for-centering-predictors" class="section level5">
<h5>shorthand for centering predictors</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">center =<span class="st"> </span>function(x) { <span class="kw">scale</span>(x, <span class="dt">scale =</span> <span class="ot">FALSE</span>) }</code></pre></div>
</div>
<div id="shorthand-for-finding-mode" class="section level5">
<h5>shorthand for finding mode</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Mode <-<span class="st"> </span>function(x) { <span class="co"># from https://stackoverflow.com/questions/2547402/standard-library-function-in-r-for-finding-the-mode</span>
ux <-<span class="st"> </span><span class="kw">unique</span>(x)
ux[<span class="kw">which.max</span>(<span class="kw">tabulate</span>(<span class="kw">match</span>(x, ux)))]
}</code></pre></div>
</div>
<div id="small-helper-function-to-sample-at-level-2-families-not-individuals" class="section level5">
<h5>small helper function to sample at level 2 (families, not individuals)</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sample_level2 =<span class="st"> </span>function(data, level2_var, size, <span class="dt">replace =</span> F, <span class="dt">prob =</span> <span class="ot">NULL</span>) {
if(<span class="kw">is.data.table</span>(data)) {
some =<span class="st"> </span>data[, level2_var, with =<span class="st"> </span>F][[<span class="dv">1</span>]]
} else {
some =<span class="st"> </span>data[ ,level2_var]
}
some_u =<span class="st"> </span><span class="kw">unique</span>(some)
sample_some =<span class="st"> </span><span class="kw">sample</span>(some_u, size, replace, prob)
take =<span class="st"> </span>some %in%<span class="st"> </span>sample_some
data[take,]
}</code></pre></div>
</div>
<div id="pander-tables-think-the-output-from-missingness_patterns-are-markdown" class="section level5">
<h5>pander tables think the output from missingness_patterns are markdown</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pander_escape =<span class="st"> </span>function(x, ...) {
x$Pattern =<span class="st"> </span>stringr::<span class="kw">str_replace_all</span>(x$Pattern,<span class="st">"_"</span>,<span class="st">"</span><span class="ch">\\\\</span><span class="st">_"</span>)
x
}</code></pre></div>
<p>formula.tools got a bit too picky</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">my_as.character.formula =<span class="st"> </span>function(x) <span class="kw">Reduce</span>( paste, <span class="kw">deparse</span>(x) )</code></pre></div>
</div>
<div id="we-use-this-function-to-automatically-get-nice-tables" class="section level5">
<h5>we use this function to automatically get nice tables</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pander_handler =<span class="st"> </span>function(x, ...) {
anyS3method =<span class="st"> </span>function(x) {
classes =<span class="st"> </span><span class="kw">class</span>(x)
<span class="kw">any</span>(<span class="kw">sapply</span>(classes, <span class="dt">FUN =</span> function(classes) { !<span class="kw">is.null</span>(<span class="kw">getS3method</span>(<span class="st">'pander'</span>,classes,<span class="ot">TRUE</span>)) } ))
}
if (<span class="st">"knit_asis"</span> %in%<span class="st"> </span><span class="kw">class</span>(x)) {
x
} else if (<span class="kw">is.data.table</span>(x)) {
<span class="st">""</span>
<span class="co"># don't ever print stupid data tables</span>
} else if (<span class="kw">anyS3method</span>(x)) {
<span class="kw">pander</span>(x, <span class="dt">row.names =</span> F, ...)
} else if (<span class="kw">isS4</span>(x)) {
<span class="kw">show</span>(x)
} else {
<span class="kw">print</span>(x)
}
}</code></pre></div>
</div>
<div id="diagnostic-function-for-me-to-compare-number-of-zeros-in-data-to-a-poisson-process-with-similar-amount-of-overdispersion" class="section level5">
<h5>diagnostic function for me to compare number of zeros in data to a poisson process with similar amount of overdispersion</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">plot_zero_infl =<span class="st"> </span>function(variable) {
rpois.od<-function (n, lambda,<span class="dt">d=</span><span class="dv">1</span>) {
if (d<=<span class="dv">1</span>)
<span class="kw">rpois</span>(n, lambda)
else if (d><span class="dv">1</span>)
<span class="kw">rnbinom</span>(n, <span class="dt">size=</span>(lambda/(d<span class="dv">-1</span>)), <span class="dt">mu=</span>lambda)
}
if( <span class="kw">var</span>(variable,<span class="dt">na.rm=</span>T) ><span class="st"> </span><span class="kw">mean</span>(variable,<span class="dt">na.rm=</span>T)) { lab =<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"Overdispersed (d="</span>,<span class="kw">round</span>(<span class="kw">var</span>(variable,<span class="dt">na.rm=</span>T)/<span class="kw">mean</span>(variable,<span class="dt">na.rm=</span>T),<span class="dv">2</span>),<span class="st">") poisson generated using distribution mean and variance via neg.bin"</span>)
} else { lab =<span class="st"> "Poisson generated with distribution mean"</span>}
<span class="kw">qplot</span>(<span class="kw">rpois.od</span>(<span class="dt">n=</span><span class="kw">NROW</span>(variable),<span class="kw">mean</span>(variable,<span class="dt">na.rm=</span>T), <span class="kw">var</span>(variable,<span class="dt">na.rm=</span>T)/<span class="kw">mean</span>(variable,<span class="dt">na.rm=</span>T)), <span class="dt">binwidth=</span><span class="dv">1</span>, <span class="dt">fill=</span><span class="kw">I</span>(<span class="st">"tomato3"</span>), <span class="dt">alpha=</span><span class="kw">I</span>(<span class="fl">0.5</span>)) +<span class="st"> </span><span class="kw">ggtitle</span>(<span class="kw">paste0</span>(<span class="st">"Slate=Real; Tomato=Generated</span><span class="ch">\n</span><span class="st">"</span>,lab)) +
<span class="st"> </span><span class="kw">geom_histogram</span>(<span class="kw">aes</span>(<span class="dt">x=</span>variable),<span class="dt">fill=</span><span class="st">"darkslategray4"</span>,<span class="dt">alpha=</span><span class="fl">0.5</span>,<span class="dt">binwidth=</span><span class="dv">1</span>) +<span class="st"> </span><span class="kw">xlab</span>(<span class="st">""</span>)
}</code></pre></div>
</div>
</div>
<div id="this-function-is-just-a-shorthand-to-quickly-find-the-objects-i-generated" class="section level4">
<h4>this function is just a shorthand to quickly find the objects I generated</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">lstype =<span class="st"> </span>function(<span class="dt">types=</span><span class="kw">c</span>(<span class="st">'lmerMod'</span>,<span class="st">'glmerMod'</span>,<span class="st">'bglmerMod'</span>,<span class="st">'blmerMod'</span>), <span class="dt">envir =</span> <span class="kw">parent.frame</span>(), ...){
inlist<-<span class="kw">ls</span>(<span class="dt">envir =</span> envir, ...)
ifexistsgetclass =<span class="st"> </span>function(x, envir) {
if(<span class="kw">exists</span>(x, <span class="dt">envir =</span> envir, <span class="dt">inherits =</span> <span class="ot">FALSE</span>)) {
bla =<span class="st"> </span><span class="kw">get</span>(x, <span class="dt">envir =</span> envir, <span class="dt">inherits =</span> <span class="ot">FALSE</span>)
<span class="kw">class</span>(bla)
} else {
<span class="ot">NULL</span>
}
}
classlist<-<span class="st"> </span><span class="kw">sapply</span>(inlist,function(x) <span class="kw">ifexistsgetclass</span>(x, <span class="dt">envir =</span> envir))
if(<span class="kw">length</span>(classlist) ><span class="st"> </span><span class="dv">0</span> ) {
<span class="kw">names</span>(classlist[
<span class="kw">sapply</span>(classlist,function(x){ !!<span class="kw">length</span>(<span class="kw">intersect</span>(x, types) ) }) ## take any who have a class that is in our list
])
} else <span class="kw">character</span>(<span class="dv">0</span>)
}</code></pre></div>
</div>
<div id="a-helper-function-to-get-a-data-frame-with-estimates-and-cis" class="section level4">
<h4>a helper function to get a data frame with estimates and CIs</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fortify_mine =<span class="st"> </span>function(fit,<span class="dt">confint=</span><span class="st">"Wald"</span>, <span class="dt">modelname =</span> <span class="ot">NULL</span>, <span class="dt">mcmclist =</span> <span class="ot">NULL</span>) {
if(<span class="kw">is.null</span>(modelname)) modelname =<span class="st"> </span><span class="kw">deparse</span>(<span class="kw">substitute</span>(fit))
info =<span class="st"> </span><span class="kw">summary</span>(fit)
link =<span class="st"> "identity"</span>
family =<span class="st"> "gaussian"</span>
call =<span class="st"> ""</span>
if(<span class="kw">inherits</span>(fit, <span class="dt">what =</span> <span class="st">"MCMCglmm"</span>)) {
<span class="kw">library</span>(MCMCglmm)
if(<span class="kw">is.null</span>(mcmclist)) {
conf <-<span class="st"> </span><span class="kw">as.data.frame</span>(info$solutions[, <span class="kw">c</span>(<span class="st">'post.mean'</span>,<span class="st">'l-95% CI'</span>,<span class="st">'u-95% CI'</span>)])
<span class="kw">names</span>(conf) =<span class="st"> </span><span class="kw">c</span>(<span class="st">"Estimate"</span>,<span class="st">"Lower"</span>,<span class="st">"Upper"</span>)
} else {
conf =<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">summary</span>(mcmclist, <span class="dt">quantiles =</span> <span class="kw">c</span>(<span class="fl">0.05</span>,<span class="fl">0.5</span>,<span class="fl">0.95</span>))$quantiles)
<span class="kw">names</span>(conf) =<span class="st"> </span><span class="kw">c</span>(<span class="st">"Lower"</span>,<span class="st">"Estimate"</span>,<span class="st">"Upper"</span>)
conf =<span class="st"> </span>conf[,<span class="kw">c</span>(<span class="st">"Estimate"</span>,<span class="st">"Lower"</span>,<span class="st">"Upper"</span>)]
}
call =<span class="st"> </span><span class="kw">my_as.character.formula</span>(fit$Fixed$formula)
conf$Coefficient =<span class="st"> </span><span class="kw">rownames</span>(conf)
N_obs =<span class="st"> </span>fit$Residual$nrl
N_fam =<span class="st"> </span>fit$Random$nrl
from_class =<span class="st"> "MCMCglmm"</span>
Outcome =<span class="st"> </span><span class="kw">as.character</span>(fit$Fixed$formula[<span class="dv">2</span>])
family =<span class="st"> </span><span class="kw">head</span>(<span class="kw">unique</span>(fit$family),<span class="dv">1</span>)
if(family !=<span class="st"> 'gaussian'</span>) link =<span class="st"> "log"</span>
} else if (<span class="kw">inherits</span>(fit, <span class="dt">what=</span><span class="st">"glmmPQL"</span>)) {
summ =<span class="st"> </span><span class="kw">summary</span>(fit)
conf =<span class="st"> </span><span class="kw">data.frame</span>(summ$tTable)
conf$Coefficient =<span class="st"> </span><span class="kw">rownames</span>(conf)
conf$Lower =<span class="st"> </span>conf$Value -<span class="st"> </span><span class="fl">1.96</span> *<span class="st"> </span>conf$Std.Error
conf$Upper =<span class="st"> </span>conf$Value +<span class="st"> </span><span class="fl">1.96</span> *<span class="st"> </span>conf$Std.Error
conf$Estimate =<span class="st"> </span>conf$Value
N_obs =<span class="st"> </span><span class="kw">nrow</span>(summ$groups)
N_fam =<span class="st"> </span><span class="kw">nrow</span>(<span class="kw">unique</span>(summ$groups))
link =<span class="st"> </span>fit$family$link
family =<span class="st"> </span>fit$family$family
from_class =<span class="st"> "glmmPQL"</span>
Outcome =<span class="st"> </span><span class="kw">names</span>(fit$data)[<span class="dv">1</span>]
} else if(<span class="kw">inherits</span>(fit,<span class="dt">what=</span><span class="st">'merMod'</span>) |<span class="st"> </span><span class="kw">inherits</span>(fit,<span class="dt">what=</span><span class="st">'lm'</span>)|<span class="st"> </span><span class="kw">inherits</span>(fit,<span class="dt">what=</span><span class="st">'coxme'</span>))
{
if(confint==<span class="st">"boot"</span>){
<span class="co"># conf <- confint(fit,method= confint, parallel = "multicore", ncpus = 256)</span>
conf <-<span class="st"> </span><span class="kw">confint</span>(fit,<span class="dt">method=</span> confint, <span class="dt">parallel =</span> <span class="st">"multicore"</span>, <span class="dt">ncpus =</span> parallel::<span class="kw">detectCores</span>())
} else
{
conf <-<span class="st"> </span><span class="kw">confint</span>(fit,<span class="dt">method=</span> confint )
}
<span class="co"># citation? https://stat.ethz.ch/pipermail/r-sig-mixed-models/2013q4/021197.html</span>
conf =<span class="st"> </span><span class="kw">na.omit</span>(<span class="kw">as.data.frame</span>(conf))
conf$Coefficient =<span class="st"> </span><span class="kw">rownames</span>(conf)
<span class="kw">names</span>(conf) =<span class="st"> </span><span class="kw">c</span>(<span class="st">'Lower'</span>,<span class="st">'Upper'</span>,<span class="st">'Coefficient'</span>)
if(<span class="kw">inherits</span>(fit,<span class="dt">what=</span><span class="st">'merMod'</span>)) {
<span class="kw">library</span>(lme4)
conf$Estimate =<span class="st"> </span><span class="kw">fixef</span>(fit)
call =<span class="st"> </span><span class="kw">my_as.character.formula</span>(fit@call)
N_obs =<span class="st"> </span><span class="kw">as.vector</span>(info$devcomp$dims[<span class="st">"N"</span>])
N_fam =<span class="st"> </span><span class="kw">as.vector</span>(info$ngrps)
if(<span class="kw">length</span>(N_fam)><span class="dv">1</span>) N_fam =<span class="st"> </span><span class="kw">as.vector</span>(info$ngrps[<span class="st">'idParents'</span>])
from_class =<span class="st"> "merMod"</span>
Outcome =<span class="st"> </span><span class="kw">as.character</span>(fit@call$formula[<span class="dv">2</span>])
if(!<span class="kw">is.null</span>(info$link)) link =<span class="st"> </span>info$link
if(!<span class="kw">is.null</span>(info$family)) family =<span class="st"> </span>info$family
} else if(<span class="kw">inherits</span>(fit, <span class="dt">what=</span><span class="st">"coxme"</span>)) {
conf[,<span class="dv">1</span>] =<span class="st"> </span>fit$coefficients<span class="fl">-1.96</span>*<span class="kw">sqrt</span>(<span class="kw">diag</span>(<span class="kw">vcov</span>(fit)))
conf[,<span class="dv">2</span>] =<span class="st"> </span>fit$coefficients<span class="fl">+1.96</span>*<span class="kw">sqrt</span>(<span class="kw">diag</span>(<span class="kw">vcov</span>(fit)))
conf$Estimate =<span class="st"> </span><span class="kw">coef</span>(fit)
call =<span class="st"> </span><span class="kw">my_as.character.formula</span>(fit$call)
N_obs =<span class="st"> </span><span class="kw">tail</span>(fit$n,<span class="dv">1</span>)
N_fam =<span class="st"> </span><span class="kw">head</span>(fit$n,<span class="dv">1</span>)
from_class =<span class="st"> "coxme"</span>
Outcome =<span class="st"> </span><span class="kw">my_as.character.formula</span>(fit$call)[<span class="dv">2</span>]
link =<span class="st"> "log"</span>
family =<span class="st"> "survival"</span>
} else {
call =<span class="st"> </span><span class="kw">my_as.character.formula</span>(fit$call)
conf$Estimate =<span class="st"> </span><span class="kw">coef</span>(fit)
N_obs =<span class="st"> </span><span class="kw">nrow</span>(fit$model)
N_fam =<span class="st"> </span><span class="kw">nrow</span>(fit$model)
from_class =<span class="st"> "lm"</span>
Outcome =<span class="st"> </span><span class="kw">names</span>(fit$model)[<span class="dv">1</span>]
if(!<span class="kw">is.null</span>(fit$family$link)) link =<span class="st"> </span>fit$family$link
if(!<span class="kw">is.null</span>(fit$family$family)) family =<span class="st"> </span>fit$family$family
}
} else
{
<span class="kw">stop</span>(<span class="st">"Unknown object"</span>)
}
coefs =<span class="st"> </span><span class="kw">within</span>(conf, {
N =<span class="st"> </span><span class="kw">head</span>(N_obs,<span class="dv">1</span>)
Families =<span class="st"> </span><span class="kw">head</span>(N_fam,<span class="dv">1</span>)
Outcome =<span class="st"> </span>Outcome
Model =<span class="st"> </span>modelname
Method =<span class="st"> </span>from_class
Link =<span class="st"> </span>link
Family =<span class="st"> </span>family
Call =<span class="st"> </span>call
})
if(link %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"log"</span>,<span class="st">"logit"</span>)) {
<span class="co"># transform logits into Odds Ratios (ORs) and logs (poisson-link) into Incidence Rate Ratios</span>
coefs[,<span class="kw">c</span>(<span class="st">"Estimate"</span>,<span class="st">"Lower"</span>,<span class="st">"Upper"</span>)] =<span class="st"> </span><span class="kw">exp</span>(coefs[,<span class="kw">c</span>(<span class="st">"Estimate"</span>,<span class="st">"Lower"</span>,<span class="st">"Upper"</span>)])
}
<span class="kw">class</span>(coefs) =<span class="st"> </span><span class="kw">c</span>( <span class="kw">class</span>(coefs), <span class="st">'confint.coefs'</span>)
<span class="kw">class</span>(coefs) =<span class="st"> </span><span class="kw">c</span>( <span class="kw">class</span>(coefs), <span class="kw">paste0</span>(<span class="st">'confint.'</span>,from_class))
coefs
}</code></pre></div>
<div id="plotting-the-output-from-the-above-function" class="section level5">
<h5>plotting the output from the above function</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">plot_fortified_mer =<span class="st"> </span>function(coefs,<span class="dt">ylab =</span> <span class="st">"Effect on outcome"</span>,<span class="dt">suppress_intercept=</span>T,<span class="dt">colour =</span> <span class="st">""</span>)
{
modelname =<span class="st"> </span>coefs$Model[<span class="dv">1</span>]
<span class="co"># coefs = fortify.mine(fit, 'Wald', modelname)</span>
<span class="co"># info = summary(fit)</span>
<span class="co"># list_of_coefs[[ modelname ]] <<- list(coefs, info, fit)</span>
plot_title =<span class="st"> </span><span class="kw">str_c</span>(<span class="kw">paste0</span>(<span class="kw">unique</span>(coefs[,<span class="st">"N"</span>]), <span class="dt">collapse=</span><span class="st">"/"</span>),<span class="st">' individuals in '</span>,<span class="kw">paste0</span>(<span class="kw">unique</span>(coefs[,<span class="st">"Families"</span>]), <span class="dt">collapse=</span><span class="st">"/"</span>), <span class="st">' families'</span>)
if(suppress_intercept) {
intercept =<span class="st"> </span>coefs[<span class="kw">which</span>(coefs$Coefficient ==<span class="st"> "(Intercept)"</span>),]
plot_title =<span class="st"> </span><span class="kw">str_c</span>(plot_title,<span class="st">"</span><span class="ch">\n</span><span class="st">Intercept = "</span>,<span class="kw">as.character</span>(<span class="kw">round</span>(intercept[<span class="st">'Estimate'</span>],<span class="dv">2</span>)), <span class="st">", ["</span>,<span class="kw">as.character</span>(<span class="kw">round</span>(intercept[<span class="st">"Lower"</span>],<span class="dv">2</span>)),<span class="st">";"</span>,<span class="kw">as.character</span>(<span class="kw">round</span>(intercept[<span class="st">"Upper"</span>],<span class="dv">2</span>)),<span class="st">"]"</span>)
coefs =<span class="st"> </span>coefs[<span class="kw">which</span>(coefs$Coefficient !=<span class="st"> "(Intercept)"</span>),]
}
if(coefs$Link[<span class="dv">1</span>] %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"logit"</span>,<span class="st">"log"</span>)) {
null.point =<span class="st"> </span><span class="dv">1</span>; scaleY =<span class="st"> </span><span class="kw">scale_y_log10</span>(ylab)
} else {
null.point =<span class="st"> </span><span class="dv">0</span>; scaleY =<span class="st"> </span><span class="kw">scale_y_continuous</span>(ylab)
}
coefs$Coefficient =<span class="st"> </span>car::<span class="kw">Recode</span>(coefs$Coefficient,<span class="st">"'paternalage.diff'='Paternal age';'paternalage.mean'='Paternal age</span><span class="ch">\\</span><span class="st">nfamily average'"</span>)
coefs$Coefficient =<span class="st"> </span><span class="kw">str_replace</span>(coefs$Coefficient, <span class="st">"paternalage.factor"</span>, <span class="st">"Paternal age "</span>)
order =<span class="st"> </span><span class="kw">sort</span>(<span class="kw">unique</span>(coefs$Coefficient))
main =<span class="st"> </span><span class="kw">c</span>(<span class="st">'Paternal age</span><span class="ch">\n</span><span class="st">family average'</span>,<span class="st">'Paternal age'</span>)
order =<span class="st"> </span><span class="kw">c</span>(<span class="kw">setdiff</span>(order, main), <span class="kw">intersect</span>(main, order))
coefs$Coefficient =<span class="st"> </span><span class="kw">factor</span>(coefs$Coefficient,<span class="dt">levels =</span> order)
if(colour !=<span class="st"> ""</span>) {
aesthetics =<span class="st"> </span><span class="kw">aes_string</span>(<span class="dt">x =</span> <span class="st">"Coefficient"</span>, <span class="dt">y =</span> <span class="st">"Estimate"</span>, <span class="dt">ymin =</span> <span class="st">"Lower"</span>, <span class="dt">ymax =</span> <span class="st">"Upper"</span>, <span class="dt">colour =</span> colour)
} else {
aesthetics =<span class="st"> </span><span class="kw">aes_string</span>(<span class="dt">x =</span> <span class="st">"Coefficient"</span>, <span class="dt">y =</span> <span class="st">"Estimate"</span>, <span class="dt">ymin =</span> <span class="st">"Lower"</span>, <span class="dt">ymax =</span> <span class="st">"Upper"</span>)
}
<span class="kw">ggplot</span>(coefs, aesthetics) +
<span class="st"> </span><span class="kw">geom_pointrange</span>(<span class="dt">position=</span><span class="kw">position_dodge</span>(<span class="dt">width=</span><span class="fl">0.8</span>), <span class="dt">size =</span> <span class="dv">1</span>) +
<span class="st"> </span><span class="kw">coord_flip</span>() +
<span class="st"> </span><span class="kw">ggtitle</span>(plot_title) +
<span class="st"> </span>scaleY +
<span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label=</span><span class="kw">round</span>(Estimate,<span class="dv">2</span>)), <span class="dt">vjust =</span> <span class="dv">2</span>, <span class="dt">position=</span><span class="kw">position_dodge</span>(<span class="dt">width=</span><span class="fl">0.8</span>)) +
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="st">"Predictors"</span>) +
<span class="st"> </span><span class="kw">geom_hline</span>(<span class="dt">yintercept =</span> null.point, <span class="dt">linetype=</span><span class="st">'dotted'</span>) +
<span class="st"> </span>analysis_theme +<span class="st"> </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),<span class="dt">panel.grid.major.y =</span><span class="kw">element_blank</span>(),<span class="dt">panel.grid.major.x =</span> <span class="kw">element_line</span>(<span class="dt">colour=</span><span class="st">"#eeeeee"</span>))
}</code></pre></div>
</div>
</div>
<div id="grand-mean-center-paternal-age-or-anything-and-center-it-within-groups" class="section level4">
<h4>grand mean center paternal age (or anything) and center it within groups</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">recenter.pat =<span class="st"> </span>function(data, <span class="dt">what =</span> <span class="st">'paternalage'</span>, <span class="dt">among_who =</span> <span class="st">'idParents'</span>, <span class="dt">breaks =</span> <span class="st">'c(0 ,25, 30, 35, 40, 45, 50, 55, 90)'</span>) {
makecenter =<span class="st"> </span><span class="kw">parse</span>(<span class="dt">text=</span> <span class="kw">str_c</span>(what,<span class="st">'_c := meanCenter('</span>,what,<span class="st">')'</span>) )
makemean =<span class="st"> </span><span class="kw">parse</span>(<span class="dt">text=</span> <span class="kw">str_c</span>(what,<span class="st">'.mean := ave('</span>,what,<span class="st">','</span>,among_who,<span class="st">', FUN= function(x) { mean(x,na.rm=T) })'</span>))
makediff =<span class="st"> </span><span class="kw">parse</span>(<span class="dt">text=</span> <span class="kw">str_c</span>(what,<span class="st">'.diff := '</span>,what,<span class="st">' -'</span>,what,<span class="st">'.mean'</span>))
make_factors =<span class="st"> </span><span class="kw">parse</span>(<span class="dt">text=</span> <span class="kw">str_c</span>(what,<span class="st">'.factor := cut((10*'</span>,what,<span class="st">'),breaks = '</span>,breaks,<span class="st">', include.lowest = T, right = T)'</span>))
data[, <span class="kw">eval</span>(makecenter)]
data[, <span class="kw">eval</span>(makemean)]
data[, <span class="kw">eval</span>(makediff)]
data[, <span class="kw">eval</span>(make_factors)]
data
}</code></pre></div>
</div>
<div id="count-the-number-of-siblings-who-compete-for-foodattention-in-first-five-years-of-life-and-are-younger-than-10" class="section level4">
<h4>count the number of siblings who compete for food/attention in first five years of life (and are younger than 10)</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dependent_sibs_f5y =<span class="st"> </span>function(survive1y, byear, dyear) {
sibs =<span class="st"> </span><span class="kw">length</span>(byear)
other_dependent_sibs_f5y =<span class="st"> </span><span class="kw">integer</span>(<span class="dt">length=</span>sibs)
for(i in <span class="dv">1</span>:sibs) {
other_births =<span class="st"> </span>byear[-i]
other_deaths =<span class="st"> </span>dyear[-i]
other_made1y =<span class="st"> </span>survive1y[-i]
my_sibs =<span class="st"> </span>sibs -<span class="st"> </span><span class="dv">1</span> -<span class="st"> </span><span class="co"># minus self</span>
<span class="st"> </span><span class="kw">sum</span>(
other_births ><span class="st"> </span>(byear[i] +<span class="st"> </span><span class="dv">5</span>) |<span class="st"> </span><span class="co"># born more than 5y later</span>
<span class="st"> </span>(other_births +<span class="st"> </span><span class="dv">5</span>) <<span class="st"> </span>byear[i] |<span class="st"> </span><span class="co"># finished infancy before birth</span>
<span class="st"> </span>other_deaths <=<span class="st"> </span>byear[i] |<span class="st"> </span><span class="co"># died before birth</span>
<span class="st"> </span>other_made1y ==<span class="st"> </span><span class="dv">0</span>, <span class="co"># if they died right away, don't count</span>
<span class="dt">na.rm=</span>T) <span class="co"># if dyear missing assume they lived</span>
other_dependent_sibs_f5y[i] =<span class="st"> </span>my_sibs
}
other_dependent_sibs_f5y
}</code></pre></div>
<div id="only-younger-sibs" class="section level5">
<h5>only younger sibs</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">younger_sibs_alive_and_dependent =<span class="st"> </span>function(survive5y, byear, dyear) {
made5y =<span class="st"> </span><span class="kw">sum</span>(survive5y==<span class="dv">1</span> |<span class="st"> </span><span class="kw">is.na</span>(survive5y)) <span class="co"># number of siblings who turned at least 5</span>
sibs =<span class="st"> </span><span class="kw">length</span>(survive5y)
younger_sibs_alive_and_dependent_in_first_5y =<span class="st"> </span><span class="kw">integer</span>(<span class="dt">length=</span>sibs)
for(i in <span class="dv">1</span>:sibs) {
younger_sibs =<span class="st"> </span>byear >=<span class="st"> </span>byear[i] <span class="co"># not using < because of twins</span>
younger_sibs[i] =<span class="st"> </span>F <span class="co"># minus self</span>
my_sibs =<span class="st"> </span><span class="kw">sum</span>(younger_sibs, <span class="dt">na.rm =</span> T) <span class="co"># minus self</span>
if(my_sibs ><span class="st"> </span><span class="dv">0</span>) {
sib_births =<span class="st"> </span>byear[ <span class="kw">which</span>(younger_sibs) ]
sib_deaths =<span class="st"> </span>dyear[ <span class="kw">which</span>(younger_sibs) ]
my_sibs =<span class="st"> </span>my_sibs -
<span class="st"> </span><span class="kw">sum</span>(
sib_births ><span class="st"> </span>(byear[i] +<span class="st"> </span><span class="dv">5</span>) |<span class="st"> </span><span class="co"># born more than 5y later</span>
<span class="st"> </span>(sib_deaths <=<span class="st"> </span>sib_births +<span class="st"> </span><span class="dv">1</span>) <span class="co"># died within less than a year</span>
,<span class="dt">na.rm=</span>T)
younger_sibs_alive_and_dependent_in_first_5y[i] =<span class="st"> </span>my_sibs
}
}
younger_sibs_alive_and_dependent_in_first_5y
}</code></pre></div>
</div>
<div id="only-older-sibs" class="section level5">
<h5>only older sibs</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">older_sibs_alive_and_dependent =<span class="st"> </span>function(survive5y, byear, dyear) {
made5y =<span class="st"> </span><span class="kw">sum</span>(survive5y,<span class="dt">na.rm =</span> T) <span class="co"># number of siblings who turned at least 5</span>
sibs =<span class="st"> </span><span class="kw">length</span>(survive5y)
older_sibs_alive_and_dependent_in_first_5y =<span class="st"> </span><span class="kw">integer</span>(<span class="dt">length=</span>sibs)
for(i in <span class="dv">1</span>:sibs) {
older_sibs =<span class="st"> </span>byear <=<span class="st"> </span>byear[i] <span class="co"># not using < because of twins</span>
older_sibs[i] =<span class="st"> </span>F <span class="co"># minus self</span>
my_sibs =<span class="st"> </span><span class="kw">sum</span>(older_sibs,<span class="dt">na.rm =</span> T) <span class="co"># minus self</span>
if(my_sibs ><span class="st"> </span><span class="dv">0</span>) {
sib_births =<span class="st"> </span>byear[ <span class="kw">which</span>(older_sibs) ]
sib_deaths =<span class="st"> </span>dyear[ <span class="kw">which</span>(older_sibs) ]
my_sibs =<span class="st"> </span>my_sibs -
<span class="st"> </span><span class="kw">sum</span>(
sib_births <<span class="st"> </span>(byear[i] -<span class="st"> </span><span class="dv">5</span>) |<span class="st"> </span><span class="co"># others born more than 5y earlier than me # 10 seconds of 17</span>
<span class="st"> </span>(sib_deaths <=<span class="st"> </span>sib_births +<span class="st"> </span><span class="dv">1</span>) |<span class="st"> </span><span class="co"># died within less than a year # 7 seconds of 17</span>
<span class="st"> </span>(sib_deaths <=<span class="st"> </span>byear[i]) <span class="co"># died before my birth</span>
,<span class="dt">na.rm=</span>T)
older_sibs_alive_and_dependent_in_first_5y[i] =<span class="st"> </span>my_sibs
}
}
older_sibs_alive_and_dependent_in_first_5y
}</code></pre></div>
</div>
</div>
<div id="verify-whether-a-binned-linear-variable-has-a-linear-effect" class="section level4">
<h4>verify whether a binned linear variable has a linear effect</h4>
<p>this function takes a coefficient df which contains a cut-up/binned factor predictor and makes a plot to verify the linearity of the predictor, optionally accompanied by an mcmclist and plots the OR for the paternal age factor</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mod_coefs_factor =<span class="st"> </span>function(fixefs, <span class="dt">factor_base =</span> <span class="st">"paternalage.factor"</span>)
{
if(!<span class="kw">inherits</span>(fixefs, <span class="dt">what =</span> <span class="st">"confint.coefs"</span>)) <span class="kw">stop</span>(<span class="st">"Need a confint.coefs object."</span>)
fixefs =<span class="st"> </span><span class="kw">as.data.table</span>(fixefs)
fixefs =<span class="st"> </span>fixefs[ Coefficient %contains%<span class="st"> </span>factor_base, ]
if(<span class="kw">nrow</span>(fixefs) ><span class="st"> </span><span class="dv">0</span>) {
fixefs[, Trait :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient, <span class="st">"(.+):"</span>)[,<span class="dv">2</span>] ]
escaped_fb =<span class="st"> </span>Hmisc::<span class="kw">escapeRegex</span>(<span class="kw">as.character</span>(factor_base))
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient,<span class="kw">paste0</span>(escaped_fb, <span class="st">"(.+)"</span>))[,<span class="dv">2</span>] ]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient, <span class="st">"</span><span class="ch">\\</span><span class="st">(([0-9.,]+)</span><span class="ch">\\</span><span class="st">]"</span>)[,<span class="dv">2</span>]]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_replace</span>(Coefficient, <span class="st">"</span><span class="ch">\\</span><span class="st">."</span>,<span class="dt">replacement=</span><span class="st">""</span>)]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_replace</span>(Coefficient, <span class="st">","</span>,<span class="dt">replacement=</span><span class="st">"-"</span>)]
reference =<span class="st"> </span><span class="kw">ifelse</span>( fixefs$Link[<span class="dv">1</span>] %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"log"</span>,<span class="st">"logit"</span>), <span class="dv">1</span>, <span class="dv">0</span>)
fixefs =<span class="st"> </span><span class="kw">rbind</span>(fixefs, fixefs[<span class="kw">nrow</span>(fixefs), ])
fixefs[<span class="kw">nrow</span>(fixefs), Lower :<span class="er">=</span><span class="st"> </span>reference]
fixefs[<span class="kw">nrow</span>(fixefs), Upper :<span class="er">=</span><span class="st"> </span>reference]
fixefs[<span class="kw">nrow</span>(fixefs), Estimate :<span class="er">=</span><span class="st"> </span>reference]
fixefs[<span class="kw">nrow</span>(fixefs), Coefficient :<span class="er">=</span><span class="st"> "14-25"</span>]
fixefs
}
}</code></pre></div>
<p>plot the factor ORs to see if it’s linear</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">plot_factor_linearity =<span class="st"> </span>function(fixefs, <span class="dt">factor_base =</span> <span class="st">"paternalage.factor"</span>)
{
if(!<span class="kw">inherits</span>(fixefs, <span class="dt">what =</span> <span class="st">"confint.coefs"</span>)) <span class="kw">stop</span>(<span class="st">"Need a confint.coefs object."</span>)
fixefs =<span class="st"> </span><span class="kw">as.data.table</span>(fixefs)
fixefs =<span class="st"> </span>fixefs[ Coefficient %contains%<span class="st"> </span>factor_base, ]
if(<span class="kw">nrow</span>(fixefs) ><span class="st"> </span><span class="dv">0</span>) {
fixefs[, Trait :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient, <span class="st">"(.+):"</span>)[,<span class="dv">2</span>] ]
escaped_fb =<span class="st"> </span>Hmisc::<span class="kw">escapeRegex</span>(<span class="kw">as.character</span>(factor_base))
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient,<span class="kw">paste0</span>(escaped_fb, <span class="st">"(.+)"</span>))[,<span class="dv">2</span>] ]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_match</span>(Coefficient, <span class="st">"</span><span class="ch">\\</span><span class="st">(([0-9.,]+)</span><span class="ch">\\</span><span class="st">]"</span>)[,<span class="dv">2</span>]]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_replace</span>(Coefficient, <span class="st">"</span><span class="ch">\\</span><span class="st">."</span>,<span class="dt">replacement=</span><span class="st">""</span>)]
fixefs[ , Coefficient :<span class="er">=</span><span class="st"> </span><span class="kw">str_replace</span>(Coefficient, <span class="st">","</span>,<span class="dt">replacement=</span><span class="st">"-"</span>)]
reference =<span class="st"> </span><span class="kw">ifelse</span>( fixefs$Link[<span class="dv">1</span>] %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"log"</span>,<span class="st">"logit"</span>), <span class="dv">1</span>, <span class="dv">0</span>)
just_coefs =<span class="st"> </span><span class="kw">rbind</span>(<span class="kw">data.table</span>( <span class="dt">Lower =</span> reference, <span class="dt">Upper =</span> reference, <span class="dt">Estimate =</span> reference, <span class="dt">Coefficient =</span> <span class="st">"14-25"</span>, <span class="dt">Trait =</span> <span class="kw">unique</span>(fixefs$Trait)[<span class="dv">1</span>]), fixefs[,<span class="kw">list</span>(Lower, Upper, Estimate, Coefficient, Trait)], <span class="dt">use.names =</span> T)
if(<span class="kw">length</span>(<span class="kw">unique</span>(fixefs$Trait))><span class="dv">1</span>) just_coefs =<span class="st"> </span><span class="kw">rbind</span>(<span class="kw">data.table</span>(<span class="dt">Lower =</span> reference, <span class="dt">Upper =</span> reference, <span class="dt">Estimate =</span> reference, <span class="dt">Coefficient =</span> <span class="st">"14-25"</span>, <span class="dt">Trait =</span> <span class="kw">unique</span>(fixefs$Trait)[<span class="dv">2</span>]), just_coefs)
just_coefs[<span class="kw">is.na</span>(Trait), Trait :<span class="er">=</span><span class="st"> ""</span>]
<span class="kw">qplot</span>(<span class="dt">data=</span>just_coefs, <span class="dt">x =</span> Coefficient, <span class="dt">y =</span> Estimate, <span class="dt">geom =</span> <span class="kw">c</span>(<span class="st">"pointrange"</span>,<span class="st">"line"</span>),<span class="dt">ymin =</span> Lower, <span class="dt">ymax =</span> Upper, <span class="dt">group =</span><span class="dv">1</span> ) +<span class="st"> </span><span class="kw">ggtitle</span>(fixefs$Model[<span class="dv">1</span>]) +<span class="st"> </span>analysis_theme +<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Paternal age"</span>) +<span class="st"> </span><span class="kw">scale_y_log10</span>(<span class="kw">paste</span>(<span class="st">"Effect on"</span>,fixefs$Outcome[<span class="dv">1</span>]),<span class="dt">breaks=</span><span class="kw">c</span>(<span class="fl">0.25</span>,<span class="fl">0.5</span>,<span class="fl">0.75</span>,<span class="dv">1</span>)) +<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="kw">aes</span>(<span class="dt">weight =</span> <span class="kw">ifelse</span>(Upper-Lower !=<span class="st"> </span><span class="dv">0</span>, <span class="dv">1</span>/(Upper-Lower),<span class="dv">1</span>)) ,<span class="dt">method =</span> <span class="st">"lm"</span>, <span class="dt">se =</span> F, <span class="dt">lty =</span> <span class="st">"dashed"</span>) +<span class="st"> </span><span class="kw">facet_wrap</span>(~<span class="st"> </span>Trait)
<span class="co"># qplot(data=just_coefs, x = Coefficient, y = Estimate, geom = c("pointrange","line"),ymin = Lower, ymax = Upper, group =1 ) + ggtitle(fixefs$Model[1]) + analysis_theme + xlab("Paternal age") + ylab(paste("Effect on",fixefs$Outcome[1])) + geom_smooth(method = "lm", se = F, lty = "dashed") + facet_wrap(~ Trait)</span>
}
}
make_newdata =<span class="st"> </span>function(pred, olddata) {
newdata =<span class="st"> </span>pred
newdata$idParents =<span class="st"> </span><span class="kw">rep</span>(<span class="dv">1</span>:<span class="dv">2</span>, <span class="dt">length.out=</span><span class="kw">nrow</span>(newdata))
left =<span class="st"> </span><span class="kw">setdiff</span>(<span class="kw">names</span>(olddata), <span class="kw">names</span>(newdata))
for(i in <span class="kw">seq_along</span>(left)) {
barename =<span class="st"> </span><span class="kw">str_match</span>(left[i],<span class="st">"(.+</span><span class="ch">\\</span><span class="st">()?([^()]+)</span><span class="ch">\\</span><span class="st">)?"</span>)[,<span class="dv">3</span>] <span class="co"># bust var out of function term</span>
if(<span class="kw">is.numeric</span>(olddata[, left[i]]) &<span class="st"> </span><span class="kw">length</span>(<span class="kw">unique</span>(<span class="kw">na.omit</span>(olddata[, left[i]]))) ==<span class="st"> </span><span class="dv">2</span>) {
newdata[, barename] =<span class="st"> </span><span class="kw">mean</span>(olddata[, left[i]],<span class="dt">na.rm=</span>T)
} else if(<span class="kw">is.logical</span>((olddata[, left[i]]))) {
newdata[, barename] =<span class="st"> </span><span class="ot">FALSE</span>
} else if(<span class="kw">is.numeric</span>(olddata[, left[i]])) {
newdata[, barename] =<span class="st"> </span><span class="kw">median</span>(olddata[, left[i]],<span class="dt">na.rm=</span>T)
} else if(<span class="kw">is.factor</span>(olddata[, left[i] ])) {
fact =<span class="st"> </span><span class="kw">unique</span>(olddata[,left[i] ]) ## choose reference for factors
newdata[, barename] =<span class="st"> </span>fact[fact==<span class="kw">levels</span>(fact)[<span class="dv">1</span>]]
} else {
newdata[, barename] =<span class="st"> </span><span class="kw">Mode</span>(olddata[,left[i] ])
}
}
newdata =<span class="st"> </span>newdata[<span class="kw">order</span>(pred[,<span class="dv">1</span>]),]
<span class="kw">rownames</span>(newdata) =<span class="st"> </span><span class="dv">1</span>:<span class="kw">nrow</span>(newdata)
newdata
}</code></pre></div>
<div id="compare-two-models-coefficients" class="section level5">
<h5>compare two model’s coefficients</h5>
<p>(input are the result of fortify_mine)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">compare_coefs =<span class="st"> </span>function(x, y, <span class="dt">colour =</span> <span class="st">"Model"</span>)
{
if(<span class="kw">is</span>(x, <span class="st">'confint.coefs'</span>) &<span class="st"> </span><span class="kw">is</span>(y, <span class="st">'confint.coefs'</span>)) {
if(<span class="kw">unique</span>(x$Model)==<span class="kw">unique</span>(y$Model)) {
x$Model =<span class="st"> </span><span class="kw">deparse</span>(<span class="kw">substitute</span>(x))
y$Model =<span class="st"> </span><span class="kw">deparse</span>(<span class="kw">substitute</span>(y))
}
x$CI =<span class="st"> </span><span class="kw">str_c</span>( <span class="kw">sprintf</span>(<span class="st">"%1.2f"</span>, x$Lower), <span class="st">";"</span>, <span class="kw">sprintf</span>(<span class="st">"%1.2f"</span>, x$Upper) )
y$CI =<span class="st"> </span><span class="kw">str_c</span>( <span class="kw">sprintf</span>(<span class="st">"%1.2f"</span>, y$Lower), <span class="st">";"</span>, <span class="kw">sprintf</span>(<span class="st">"%1.2f"</span>, y$Upper) )
coefs =<span class="st"> </span><span class="kw">merge</span>(x[, <span class="kw">c</span>(<span class="st">'Coefficient'</span>,<span class="st">'Estimate'</span>,<span class="st">'CI'</span>)], y[, <span class="kw">c</span>(<span class="st">'Coefficient'</span>,<span class="st">'Estimate'</span>,<span class="st">'CI'</span>)], <span class="dt">by =</span> <span class="st">"Coefficient"</span>)
coefs =<span class="st"> </span>coefs[, <span class="kw">c</span>(<span class="st">"Coefficient"</span>, <span class="kw">setdiff</span>(<span class="kw">sort</span>(<span class="kw">names</span>(coefs)),<span class="st">"Coefficient"</span>))]
<span class="kw">print</span>(<span class="kw">plot_fortified_mer</span>(<span class="kw">rbind</span>(x,y), <span class="dt">colour =</span> colour))
<span class="kw">kable</span>(coefs,<span class="dt">digits =</span> <span class="dv">2</span>, <span class="dt">row.names=</span><span class="ot">FALSE</span>)
} else {
<span class="kw">stop</span>(<span class="st">"Need confint.coefs"</span>)
}
}</code></pre></div>
<p>shorthand for comparing models with few/many controls</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">compare_controlled_to_uncontrolled =<span class="st"> </span>function(ucon, con) {
ucon$Controls =<span class="st"> "few"</span>
con$Controls =<span class="st"> "many"</span>
<span class="kw">compare_coefs</span>(ucon, con, <span class="dt">colour =</span> <span class="st">"Controls"</span>)
}</code></pre></div>
<p>shorthand for sample comparison</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">compare_krmh_to_rpqa =<span class="st"> </span>function(krmh_coefs, rpqa_coefs) {
krmh_coefs$Population =<span class="st"> "Krummhörn"</span>
rpqa_coefs$Population =<span class="st"> "Québec"</span>
rpqa_coefs=rpqa_coefs[!rpqa_coefs$Coefficient %contains%<span class="st"> "birth.cohort"</span>, ]
krmh_coefs=krmh_coefs[!krmh_coefs$Coefficient %contains%<span class="st"> "birth.cohort"</span>, ]
<span class="kw">compare_coefs</span>(krmh_coefs, rpqa_coefs, <span class="dt">colour =</span> <span class="st">"Population"</span>)
}</code></pre></div>
<p>putting in a named list to name the origin of the coefficients</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">merge_coefs =<span class="st"> </span>function(...) {
dots <-<span class="st"> </span><span class="kw">list</span>(...)
if(<span class="kw">length</span>(dots) ==<span class="st"> </span><span class="dv">0</span>) <span class="kw">return</span>(<span class="ot">NULL</span>)
for(i in <span class="dv">1</span>:<span class="kw">length</span>(dots)) {
dots[[i]]$Population =<span class="st"> </span><span class="kw">names</span>(dots)[i]
}
coefs =<span class="st"> </span><span class="kw">rbindlist</span>(dots, <span class="dt">fill =</span> T)
coefs =<span class="st"> </span><span class="kw">data.table</span>(coefs)
<span class="kw">class</span>(coefs) =<span class="st"> </span><span class="kw">c</span>( <span class="kw">class</span>(coefs), <span class="st">'confint.coefs'</span>)
coefs
}</code></pre></div>
<p>another comparison plot</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">factor_plot =<span class="st"> </span>function(coefs) {
coefs$Population =<span class="st"> </span><span class="kw">factor</span>(coefs$Population, <span class="kw">c</span>(<span class="st">"Québec"</span>,<span class="st">"Modern Sweden"</span>,<span class="st">"Historical Sweden"</span>,<span class="st">"Krummhörn"</span>))
<span class="kw">ggplot</span>(coefs,
<span class="kw">aes</span>(<span class="dt">colour =</span> Population, <span class="dt">x =</span> Coefficient, <span class="dt">y =</span> Estimate, <span class="dt">ymin =</span> Lower, <span class="dt">ymax =</span> Upper,<span class="dt">weight =</span> <span class="kw">ifelse</span>(Upper-Lower !=<span class="st"> </span><span class="dv">0</span>, <span class="dv">1</span>/(Upper-Lower),<span class="dv">1</span>), <span class="dt">group =</span> Population)) +
<span class="st"> </span><span class="kw">geom_pointrange</span>(<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.4</span>), <span class="dt">size =</span> <span class="dv">1</span>) +
<span class="st"> </span><span class="co"># geom_line(position = position_dodge(width = 0.4))+</span>
<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.4</span>),<span class="dt">method =</span> <span class="st">"lm"</span>, <span class="dt">se =</span> F, <span class="dt">linetype =</span> <span class="st">"dashed"</span>, <span class="dt">size =</span> <span class="fl">0.2</span>) +
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Paternal age"</span>) +
<span class="st"> </span><span class="kw">scale_fill_brewer</span>(<span class="dt">palette =</span> <span class="st">"Set2"</span>) +
<span class="st"> </span><span class="kw">scale_colour_brewer</span>(<span class="dt">palette =</span> <span class="st">"Set2"</span>) +
<span class="st"> </span><span class="kw">scale_y_log10</span>(<span class="dt">breaks =</span> <span class="kw">c</span>(<span class="fl">0.4</span>,<span class="fl">0.6</span>,<span class="fl">0.8</span>,<span class="dv">1</span>,<span class="fl">1.2</span>))+
<span class="st"> </span><span class="kw">geom_hline</span>(<span class="dt">y =</span> <span class="dv">1</span>, <span class="dt">colour =</span> <span class="st">"black"</span>, <span class="dt">linetype =</span> <span class="st">"dotted"</span>) +
<span class="st"> </span>analysis_theme
}</code></pre></div>
<p>another comparison plot</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">factor_boot_plot =<span class="st"> </span>function(coefs) {
coefs$Population =<span class="st"> </span><span class="kw">factor</span>(coefs$Population, <span class="kw">c</span>(<span class="st">"Québec"</span>,<span class="st">"Modern Sweden"</span>,<span class="st">"Historical Sweden"</span>,<span class="st">"Krummhörn"</span>))
coefs$Population =<span class="st"> </span><span class="kw">factor</span>(coefs$Population, <span class="kw">c</span>(<span class="st">"Québec"</span>,<span class="st">"Modern Sweden"</span>,<span class="st">"Historical Sweden"</span>,<span class="st">"Krummhörn"</span>))
<span class="kw">ggplot</span>(coefs, <span class="kw">aes</span>(<span class="dt">x =</span> paternalage.factor, <span class="dt">y =</span> value, <span class="dt">fill =</span> Population)) +
<span class="st"> </span><span class="kw">geom_violin</span>(<span class="dt">colour =</span> <span class="st">"transparent"</span>, <span class="dt">alpha =</span> <span class="fl">0.3</span>, <span class="dt">position =</span> <span class="kw">position_identity</span>(), <span class="dt">scale =</span> <span class="st">"width"</span>) +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">colour =</span> Population),<span class="dt">stat =</span> <span class="st">"summary"</span>, <span class="dt">fun.data =</span> <span class="st">"mean_sdl"</span>, <span class="dt">position =</span> <span class="kw">position_identity</span>())+
<span class="st"> </span><span class="kw">geom_smooth</span>(<span class="kw">aes</span>(<span class="dt">colour =</span> Population, <span class="dt">weight =</span> <span class="dv">1</span>/<span class="kw">sd</span>(value, <span class="dt">na.rm =</span> T),<span class="dt">group =</span> Population),<span class="dt">method =</span> <span class="st">"lm"</span>, <span class="dt">se =</span> F, <span class="dt">lty =</span> <span class="st">"dashed"</span>, <span class="dt">position =</span> <span class="kw">position_identity</span>()) +
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Children of older fathers have lower evolutionary fitness</span><span class="ch">\n</span><span class="st">further controls"</span>) +
<span class="st"> </span><span class="kw">scale_fill_brewer</span>(<span class="dt">palette =</span> <span class="st">"Set2"</span>) +
<span class="st"> </span><span class="kw">scale_colour_brewer</span>(<span class="dt">palette =</span> <span class="st">"Set2"</span>) +
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Paternal age"</span>) +
<span class="st"> </span>analysis_theme +
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>),
<span class="dt">legend.justification =</span> <span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>))
}</code></pre></div>
</div>
</div>
<div id="count-eggs" class="section level2">
<h2>count eggs</h2>
<p>produce a (weighted) count of offspring and merge it back on the individual</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">count_and_merge =<span class="st"> </span>function(df, what, wt_var) {
if(!<span class="kw">is.data.table</span>(df)) df =<span class="st"> </span><span class="kw">as.data.table</span>(df)
<span class="kw">setnames</span>(df, wt_var, <span class="st">".tmp_wt"</span>)
counted.dad =<span class="st"> </span>df %>%
<span class="st"> </span>dplyr::<span class="kw">group_by</span>(idPere) %>%
<span class="st"> </span>dplyr::<span class="kw">summarise</span>(<span class="dt">.tmp =</span> <span class="kw">sum</span>(.tmp_wt, <span class="dt">na.rm =</span> T)) %>%
<span class="st"> </span>dplyr::<span class="kw">rename</span>(<span class="dt">idIndividu =</span> idPere)
counted.mom =<span class="st"> </span>df %>%<span class="st"> </span>dplyr::<span class="kw">group_by</span>(idMere) %>%
<span class="st"> </span>dplyr::<span class="kw">summarise</span>(<span class="dt">.tmp =</span> <span class="kw">sum</span>(.tmp_wt, <span class="dt">na.rm =</span> T)) %>%
<span class="st"> </span>dplyr::<span class="kw">rename</span>(<span class="dt">idIndividu =</span> idMere)
counted =<span class="st"> </span><span class="kw">rbind</span>(counted.dad,counted.mom)
df =<span class="st"> </span><span class="kw">data.table</span>(<span class="kw">merge</span>(df,counted,<span class="dt">by=</span><span class="st">'idIndividu'</span>,<span class="dt">all.x=</span>T))
df[<span class="kw">is.na</span>(.tmp), .tmp :<span class="er">=</span><span class="st"> </span><span class="dv">0</span>]
<span class="kw">setnames</span>(df, <span class="dt">old =</span> <span class="kw">c</span>(<span class="st">".tmp"</span>,<span class="st">".tmp_wt"</span>), <span class="dt">new =</span> <span class="kw">c</span>(what,wt_var))
df
}</code></pre></div>
<div id="translate-file-name-into-human-readable-title" class="section level4">
<h4>translate file name into human readable title</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">human_readable_filename =<span class="st"> </span>function(file) {
file %>%
<span class="st"> </span>basename %>%
<span class="st"> </span>tools::<span class="kw">file_path_sans_ext</span>() %>%
<span class="st"> </span><span class="kw">str_sub</span>(<span class="dv">3</span>) %>%
<span class="st"> </span><span class="kw">str_replace_all</span>(<span class="st">"_+"</span>,<span class="st">" "</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"peer"</span> ,<span class="st">"Peerage"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"ddb"</span> ,<span class="st">"historical Sweden"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"swed"</span> ,<span class="st">"modern Sweden"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"rpqa"</span> ,<span class="st">"Québec"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"krmh"</span> ,<span class="st">"Krummhörn"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(<span class="st">"massage"</span> ,<span class="st">"Data Wrangling"</span>) %>%
<span class="st"> </span><span class="kw">stri_trans_totitle</span>()
}</code></pre></div>
</div>
<div id="human-readable-paternal-age-factor" class="section level4">
<h4>human readable paternal age factor</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">human_readable_factor =<span class="st"> </span>function(factor) {
tfactor =<span class="st"> </span><span class="kw">as.character</span>(factor)
tfactor =<span class="st"> </span><span class="kw">str_match</span>(tfactor,<span class="st">"</span><span class="ch">\\</span><span class="st">(?([0-9.,]+)</span><span class="ch">\\</span><span class="st">]"</span>)[,<span class="dv">2</span>]
if(<span class="kw">any</span>(<span class="kw">is.na</span>(tfactor))) {
<span class="kw">return</span>(factor)
}
tfactor %>%
<span class="st"> </span><span class="kw">str_replace</span>(.,<span class="st">"</span><span class="ch">\\</span><span class="st">."</span>,<span class="dt">replacement=</span><span class="st">""</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(.,<span class="st">","</span>,<span class="dt">replacement=</span><span class="st">"-"</span>) %>%
<span class="st"> </span><span class="kw">str_replace</span>(.,<span class="st">"0-25"</span>,<span class="dt">replacement=</span><span class="st">"10-25"</span>)
}
describeBin =<span class="st"> </span>function(df) {
df2 =<span class="st"> </span><span class="kw">colwise</span>(function(x){ if(<span class="kw">is.factor</span>(x)){ <span class="kw">ifelse</span>(x==<span class="st">"TRUE"</span>,<span class="dv">1</span>,<span class="dv">0</span>)} else{ x} })(df)
df2 %>%
<span class="st"> </span><span class="kw">data.frame</span>() %>%
<span class="st"> </span><span class="kw">melt</span>(.,<span class="dt">id.vars=</span><span class="kw">c</span>()) %>%
<span class="st"> </span><span class="kw">group_by</span>(variable) %>%
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">n =</span> <span class="kw">sum</span>(!<span class="kw">is.na</span>(value)),
<span class="dt">mean =</span> <span class="kw">mean</span>(value, <span class="dt">na.rm=</span>T),
<span class="dt">sd =</span> <span class="kw">mean</span>(value, <span class="dt">na.rm=</span>T) *<span class="st"> </span>(<span class="dv">1</span>-<span class="kw">mean</span>(value, <span class="dt">na.rm=</span>T))) %>%
<span class="st"> </span><span class="kw">data.frame</span>() -><span class="st"> </span>descriptives
<span class="kw">rownames</span>(descriptives) =<span class="st"> </span>descriptives$variable
descriptives$variable =<span class="st"> </span><span class="ot">NULL</span>
<span class="kw">round</span>(descriptives,<span class="dv">2</span>)
}
aggDemoTrends =<span class="st"> </span>function(df) {
df[, <span class="kw">list</span>(byear, paternalage, maternalage, maternalage_at_1st_sib, maternalage_at_last_sib,paternalage_at_1st_sib,paternalage_at_last_sib)] %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">maternalage_at_1st_sib =</span> <span class="kw">ifelse</span>(maternalage_at_1st_sib==maternalage, maternalage_at_1st_sib, <span class="ot">NA</span>), <span class="co"># don't count sibs twice</span>
<span class="dt">paternalage_at_1st_sib =</span> <span class="kw">ifelse</span>(paternalage_at_1st_sib==paternalage, paternalage_at_1st_sib, <span class="ot">NA</span>),
<span class="dt">maternalage_at_last_sib =</span> <span class="kw">ifelse</span>(maternalage_at_last_sib==maternalage, maternalage_at_last_sib, <span class="ot">NA</span>),
<span class="dt">paternalage_at_last_sib =</span> <span class="kw">ifelse</span>(paternalage_at_last_sib==paternalage, paternalage_at_last_sib, <span class="ot">NA</span>)) %>%
<span class="st"> </span><span class="kw">melt</span>(<span class="dt">id.vars=</span><span class="kw">c</span>(<span class="st">"byear"</span>)) %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">Parent =</span> <span class="kw">ifelse</span>(variable %begins_with%<span class="st"> "paternal"</span>,<span class="st">"Father"</span>,<span class="st">"Mother"</span>),
<span class="dt">variable =</span> <span class="kw">ifelse</span>(variable %contains%<span class="st"> "1st"</span>, <span class="st">"first"</span>, <span class="kw">ifelse</span>(variable %contains%<span class="st"> "last"</span>, <span class="st">"last"</span>, <span class="st">"all"</span>))
) %>%
<span class="st"> </span><span class="kw">group_by</span>(byear, Parent, variable) %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">value =</span> value*<span class="dv">10</span>) %>%
<span class="st"> </span><span class="kw">dcast</span>(byear +<span class="st"> </span>Parent ~<span class="st"> </span>variable, <span class="dt">fun.aggregate =</span> function(x){<span class="kw">mean</span>(x,<span class="dt">na.rm=</span>T)}) %>%
<span class="st"> </span><span class="kw">rename</span>(<span class="dt">Year =</span> byear) %>%
<span class="st"> </span><span class="kw">data.table</span>()
}
<span class="co">#</span>
<span class="co"># colour_values = c(</span>
<span class="co"># "Krummhörn" = "#542437",</span>
<span class="co"># "Québec" = "#53777A",</span>
<span class="co"># "Modern Sweden" = "#D95B43",</span>
<span class="co"># "Historical Sweden" = "#C02942"</span>
<span class="co"># )</span>
<span class="co"># colour_values = c(</span>
<span class="co"># "Krummhörn" = "#FF9C5B",</span>
<span class="co"># "Québec" = "#3B8183",</span>
<span class="co"># "Modern Sweden" = "#ED303C",</span>
<span class="co"># "Historical Sweden" = "#F5634A"</span>
<span class="co"># )</span>
<span class="co"># colour_values = c(</span>
<span class="co"># "Krummhörn" = "#5E8C6A",</span>
<span class="co"># "Québec" = "#8C2318",</span>
<span class="co"># "Modern Sweden" = "#88A65E",</span>
<span class="co"># "Historical Sweden" = "#BFB35A"</span>
<span class="co"># )</span>
<span class="co"># colour_values = c(</span>
<span class="co"># "Krummhörn" = "#89E0B9",</span>
<span class="co"># "Québec" = "#068981",</span>
<span class="co"># "Modern Sweden" = "#535659",</span>
<span class="co"># "Historical Sweden" = "#F4F9CB"</span>
<span class="co"># )</span>
<span class="co"># source: http://www.colourlovers.com/palette/3735149/Some_Elegance</span>
colour_values =<span class="st"> </span><span class="kw">c</span>(
<span class="st">"Krummhörn"</span> =<span class="st"> "#E2A674"</span>,
<span class="st">"Québec"</span> =<span class="st"> "#9F3B2B"</span>,
<span class="st">"20th-century Sweden"</span> =<span class="st"> "#8A846C"</span>,
<span class="st">"Historical Sweden"</span> =<span class="st"> "#21523B"</span>
)
pop_colour =<span class="st"> </span><span class="kw">scale_colour_manual</span>(<span class="dt">values =</span> colour_values)
pop_fill =<span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> colour_values)</code></pre></div>
</div>
</div>
<div id="bin-years" class="section level2">
<h2>Bin years</h2>
<p>simple way to get bins by year</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">year_bins =<span class="st"> </span>function(x, <span class="dt">bin =</span> <span class="dv">5</span>) {
<span class="kw">ifelse</span>(<span class="kw">is.na</span>(x), <span class="ot">NA_character_</span>,
<span class="kw">paste0</span>(plyr::<span class="kw">round_any</span>(x, bin, floor),<span class="st">"-"</span>,plyr::<span class="kw">round_any</span>(x, bin, floor) +<span class="st"> </span>bin)
)
}</code></pre></div>
</div>
<div id="compute-effect-of-10-years-of-paternal-age" class="section level2">
<h2>Compute effect of 10 years of paternal age</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">paternal_age_10y_effect =<span class="st"> </span>function(model, <span class="dt">predictor_name =</span> <span class="st">"paternalage"</span>) {
nam =<span class="st"> </span><span class="kw">rownames</span>(<span class="kw">fixef</span>(model))
pred_name =<span class="st"> </span>nam[nam %contains%<span class="st"> </span>predictor_name &<span class="st"> </span>!<span class="st"> </span>nam %contains%<span class="st"> </span><span class="kw">paste0</span>(predictor_name, <span class="st">".mean"</span>)]
if (<span class="kw">length</span>(pred_name) ><span class="st"> </span><span class="dv">0</span> ) {
pred_name =<span class="st"> </span>pred_name[<span class="dv">1</span>]
predictor =<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">paternalage =</span> <span class="kw">c</span>(<span class="fl">2.5</span>, <span class="fl">3.5</span>))
probs =<span class="st"> </span><span class="kw">c</span>(<span class="fl">0.025</span>, <span class="fl">0.1</span>, <span class="fl">0.5</span>, <span class="fl">0.9</span>, <span class="fl">0.975</span>)
if (pred_name %in%<span class="st"> </span><span class="kw">names</span>(model$data)) {
<span class="kw">names</span>(predictor) =<span class="st"> </span>pred_name[<span class="dv">1</span>]
}
<span class="co"># brms:::prepare_conditions</span>
newd =<span class="st"> </span><span class="kw">make_newdata</span>(predictor, <span class="dt">olddata =</span> model$data)
newd$response =<span class="st"> </span>newd$trait =<span class="st"> </span><span class="ot">NULL</span>
fixefs =<span class="st"> </span><span class="kw">round</span>(<span class="kw">exp</span>(<span class="kw">fixef</span>(model, <span class="dt">estimate =</span> <span class="st">"quantile"</span>, <span class="dt">probs =</span> probs)[pred_name,]), <span class="dv">2</span>)
prediction =<span class="st"> </span><span class="kw">fitted</span>(model, <span class="dt">newdata =</span> newd, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">summary =</span> F)
estlow =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(prediction[,<span class="dv">1</span>], <span class="dt">probs =</span> probs),<span class="dv">2</span>)
esthigh =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(prediction[,<span class="dv">2</span>], <span class="dt">probs =</span> probs),<span class="dv">2</span>)
if (model$family$family ==<span class="st"> "bernoulli"</span>) {
pct_change =<span class="st"> </span>prediction[,<span class="dv">2</span>] -<span class="st"> </span>prediction[,<span class="dv">1</span>]
pct_decrease =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(pct_change, <span class="dt">probs =</span> probs) *<span class="st"> </span><span class="dv">100</span>,<span class="dv">2</span>)
} else {
pct_change =<span class="st"> </span>prediction[,<span class="dv">2</span>] /<span class="st"> </span>prediction[,<span class="dv">1</span>] -<span class="st"> </span><span class="dv">1</span>
pct_decrease =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(pct_change, <span class="dt">probs =</span> probs) *<span class="st"> </span><span class="dv">100</span>, <span class="dv">2</span>)
}
estimates =<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">effect =</span> <span class="kw">c</span>(<span class="st">"estimate father 25y"</span>, <span class="st">"estimate father 35y"</span>,<span class="st">"percentage change"</span>, <span class="st">"OR/IRR"</span>),
<span class="dt">median_estimate =</span> <span class="kw">c</span>(<span class="kw">as.character</span>(estlow[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(esthigh[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(pct_decrease[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(fixefs[<span class="st">'50%'</span>])),
<span class="dt">ci_95 =</span> <span class="kw">c</span>(
<span class="kw">paste0</span>(<span class="st">'['</span>,estlow[<span class="st">'2.5%'</span>],<span class="st">';'</span>,estlow[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,esthigh[<span class="st">'2.5%'</span>],<span class="st">';'</span>,esthigh[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,pct_decrease[<span class="st">'2.5%'</span>],<span class="st">';'</span>,pct_decrease[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs[<span class="st">'2.5%'</span>],<span class="st">';'</span>,fixefs[<span class="st">'97.5%'</span>],<span class="st">']'</span>)
),
<span class="dt">ci_80 =</span> <span class="kw">c</span>(
<span class="kw">paste0</span>(<span class="st">'['</span>,estlow[<span class="st">'10%'</span>],<span class="st">';'</span>,estlow[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,esthigh[<span class="st">'10%'</span>],<span class="st">';'</span>,esthigh[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,pct_decrease[<span class="st">'10%'</span>],<span class="st">';'</span>,pct_decrease[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs[<span class="st">'10%'</span>],<span class="st">';'</span>,fixefs[<span class="st">'90%'</span>],<span class="st">']'</span>)
))
if (model$family$family ==<span class="st"> "hurdle_poisson"</span>) {
fixefs2 =<span class="st"> </span><span class="kw">round</span>(<span class="kw">exp</span>(<span class="kw">fixef</span>(model, <span class="dt">estimate =</span> <span class="st">"quantile"</span>, <span class="dt">probs =</span> probs)[<span class="kw">paste0</span>(<span class="st">"hu_"</span>,pred_name),]), <span class="dv">2</span>)
estimates2 =<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">effect =</span> <span class="kw">c</span>(<span class="st">"OR hurdle"</span>),
<span class="dt">median_estimate =</span> <span class="kw">as.character</span>(fixefs2[<span class="st">'50%'</span>]),
<span class="dt">ci_95 =</span> <span class="kw">c</span>(<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs2[<span class="st">'2.5%'</span>],<span class="st">';'</span>,fixefs2[<span class="st">'97.5%'</span>],<span class="st">']'</span>)),
<span class="dt">ci_80 =</span> <span class="kw">c</span>(<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs2[<span class="st">'10%'</span>],<span class="st">';'</span>,fixefs2[<span class="st">'90%'</span>],<span class="st">']'</span>)))
estimates =<span class="st"> </span><span class="kw">bind_rows</span>(estimates, estimates2)
}
estimates
} else {
<span class="st">" "</span>
}
}</code></pre></div>
</div>
<div id="compute-effect-of-30-years-of-paternal-age" class="section level2">
<h2>Compute effect of 30 years of paternal age</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">effect_of_avg_nr_of_mutations =<span class="st"> </span>function(model, <span class="dt">predictor_name =</span> <span class="st">"paternalage"</span>) {
nam =<span class="st"> </span><span class="kw">rownames</span>(<span class="kw">fixef</span>(model))
pred_name =<span class="st"> </span>nam[nam %contains%<span class="st"> </span>predictor_name &<span class="st"> </span>!<span class="st"> </span>nam %contains%<span class="st"> </span><span class="kw">paste0</span>(predictor_name, <span class="st">".mean"</span>)]
if (<span class="kw">length</span>(pred_name) ><span class="st"> </span><span class="dv">0</span> ) {
pred_name =<span class="st"> </span>pred_name[<span class="dv">1</span>]
predictor =<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">paternalage =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">3</span>))
probs =<span class="st"> </span><span class="kw">c</span>(<span class="fl">0.025</span>, <span class="fl">0.1</span>, <span class="fl">0.5</span>, <span class="fl">0.9</span>, <span class="fl">0.975</span>)
if (pred_name %in%<span class="st"> </span><span class="kw">names</span>(model$data)) {
<span class="kw">names</span>(predictor) =<span class="st"> </span>pred_name[<span class="dv">1</span>]
}
<span class="co"># brms:::prepare_conditions</span>
newd =<span class="st"> </span><span class="kw">make_newdata</span>(predictor, <span class="dt">olddata =</span> model$data)
newd$response =<span class="st"> </span>newd$trait =<span class="st"> </span><span class="ot">NULL</span>
fixefs =<span class="st"> </span><span class="kw">round</span>(<span class="kw">exp</span>(<span class="kw">fixef</span>(model, <span class="dt">estimate =</span> <span class="st">"quantile"</span>, <span class="dt">probs =</span> probs)[pred_name,]), <span class="dv">2</span>)
prediction =<span class="st"> </span><span class="kw">fitted</span>(model, <span class="dt">newdata =</span> newd, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">summary =</span> F)
estlow =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(prediction[,<span class="dv">1</span>], <span class="dt">probs =</span> probs),<span class="dv">2</span>)
esthigh =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(prediction[,<span class="dv">2</span>], <span class="dt">probs =</span> probs),<span class="dv">2</span>)
if (model$family$family ==<span class="st"> "bernoulli"</span>) {
pct_change =<span class="st"> </span>(prediction[,<span class="dv">2</span>] -<span class="st"> </span>prediction[,<span class="dv">1</span>])
pct_decrease =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(pct_change, <span class="dt">probs =</span> probs) *<span class="st"> </span><span class="dv">100</span>,<span class="dv">2</span>)
} else {
pct_change =<span class="st"> </span>(prediction[,<span class="dv">2</span>] /<span class="st"> </span>prediction[,<span class="dv">1</span>] -<span class="st"> </span><span class="dv">1</span>)
pct_decrease =<span class="st"> </span><span class="kw">round</span>(<span class="kw">quantile</span>(pct_change, <span class="dt">probs =</span> probs) *<span class="st"> </span><span class="dv">100</span>, <span class="dv">2</span>)
}
estimates =<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">effect =</span> <span class="kw">c</span>(<span class="st">"estimate at 0 mutations"</span>, <span class="st">"estimate at 60 mutations"</span>,<span class="st">"percentage change"</span>, <span class="st">"OR/IRR"</span>),
<span class="dt">median_estimate =</span> <span class="kw">c</span>(<span class="kw">as.character</span>(estlow[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(esthigh[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(pct_decrease[<span class="st">'50%'</span>]),
<span class="kw">as.character</span>(fixefs[<span class="st">'50%'</span>])),
<span class="dt">ci_95 =</span> <span class="kw">c</span>(
<span class="kw">paste0</span>(<span class="st">'['</span>,estlow[<span class="st">'2.5%'</span>],<span class="st">';'</span>,estlow[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,esthigh[<span class="st">'2.5%'</span>],<span class="st">';'</span>,esthigh[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,pct_decrease[<span class="st">'2.5%'</span>],<span class="st">';'</span>,pct_decrease[<span class="st">'97.5%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs[<span class="st">'2.5%'</span>],<span class="st">';'</span>,fixefs[<span class="st">'97.5%'</span>],<span class="st">']'</span>)
),
<span class="dt">ci_80 =</span> <span class="kw">c</span>(
<span class="kw">paste0</span>(<span class="st">'['</span>,estlow[<span class="st">'10%'</span>],<span class="st">';'</span>,estlow[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,esthigh[<span class="st">'10%'</span>],<span class="st">';'</span>,esthigh[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,pct_decrease[<span class="st">'10%'</span>],<span class="st">';'</span>,pct_decrease[<span class="st">'90%'</span>],<span class="st">']'</span>),
<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs[<span class="st">'10%'</span>],<span class="st">';'</span>,fixefs[<span class="st">'90%'</span>],<span class="st">']'</span>)
))
if (model$family$family ==<span class="st"> "hurdle_poisson"</span>) {
fixefs2 =<span class="st"> </span><span class="kw">round</span>(<span class="kw">exp</span>(<span class="kw">fixef</span>(model, <span class="dt">estimate =</span> <span class="st">"quantile"</span>, <span class="dt">probs =</span> probs)[<span class="kw">paste0</span>(<span class="st">"hu_"</span>,pred_name),]), <span class="dv">2</span>)
estimates2 =<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">effect =</span> <span class="kw">c</span>(<span class="st">"OR hurdle"</span>),
<span class="dt">median_estimate =</span> <span class="kw">as.character</span>(fixefs2[<span class="st">'50%'</span>]),
<span class="dt">ci_95 =</span> <span class="kw">c</span>(<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs2[<span class="st">'2.5%'</span>],<span class="st">';'</span>,fixefs2[<span class="st">'97.5%'</span>],<span class="st">']'</span>)),
<span class="dt">ci_80 =</span> <span class="kw">c</span>(<span class="kw">paste0</span>(<span class="st">'['</span>,fixefs2[<span class="st">'10%'</span>],<span class="st">';'</span>,fixefs2[<span class="st">'90%'</span>],<span class="st">']'</span>)))
estimates =<span class="st"> </span><span class="kw">bind_rows</span>(estimates, estimates2)
}
estimates
} else {
<span class="st">" "</span>
}
}</code></pre></div>
</div>
<div id="the-rendering-function-for-rmds-and-md" class="section level2">
<h2>the rendering function for Rmds and md</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"> render_web =<span class="st"> </span>function(file, <span class="dt">auto_tab =</span> T, <span class="dt">include_top =</span> <span class="st">"library/general_header.html"</span>, <span class="dt">output_dir =</span> <span class="st">"../paternal_age_fitness/"</span>) {
title =<span class="st"> </span><span class="kw">human_readable_filename</span>(file)
pops =<span class="st"> </span><span class="kw">c</span>(<span class="st">"krmh"</span>,<span class="st">"ddb"</span>,<span class="st">"rpqa"</span>,<span class="st">"swed"</span>)
pops =<span class="st"> </span>pops[file %contains%<span class="st"> </span>pops]
if(<span class="kw">length</span>(pops) ==<span class="st"> </span><span class="dv">1</span>) include_top =<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"library/"</span>,pops,<span class="st">"_header.html"</span>)
pandoc_args =<span class="st"> </span><span class="kw">list</span>(<span class="kw">paste0</span>(<span class="st">"--metadata=title:</span><span class="ch">\"</span><span class="st">"</span>,title,<span class="st">"</span><span class="ch">\"</span><span class="st">"</span>))
if(auto_tab) pandoc_args[[<span class="dv">2</span>]] =<span class="st"> "--metadata=autotab:1"</span>
output_format =<span class="st"> </span><span class="kw">html_document</span>(
<span class="dt">self_contained =</span> <span class="ot">FALSE</span>,
<span class="dt">template =</span> <span class="st">"../paternal_age_fitness/library/website_theme.html"</span>,
<span class="dt">lib_dir =</span> <span class="kw">paste0</span>(output_dir, <span class="st">"library/"</span>),
<span class="dt">mathjax =</span> <span class="ot">NULL</span>,
<span class="dt">keep_md =</span> <span class="ot">TRUE</span>,
<span class="dt">includes =</span> <span class="kw">includes</span>(<span class="dt">before_body =</span> include_top),
<span class="dt">pandoc_args =</span> pandoc_args,
<span class="dt">theme =</span> <span class="st">"paper"</span>
)
output_format$knitr$opts_chunk$error =<span class="st"> </span><span class="ot">TRUE</span>
<span class="kw">render</span>(<span class="dt">input =</span> file,
<span class="dt">output_format =</span> output_format,
<span class="dt">envir =</span> <span class="kw">new.env</span>(),
<span class="dt">output_dir =</span> output_dir
)
}</code></pre></div>
</div>
<div id="plot-two-credibility-levels-for-brms-marginal-effect-plots" class="section level2">
<h2>Plot two credibility levels for brms marginal effect plots</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">plot.brmsMarginalEffects_shades <-<span class="st"> </span>function(x, y, <span class="dt">ncol =</span> <span class="ot">NULL</span>, <span class="dt">points =</span> <span class="ot">FALSE</span>,
<span class="dt">rug =</span> <span class="ot">FALSE</span>, <span class="dt">theme =</span> ggplot2::<span class="kw">theme</span>(),
<span class="dt">ask =</span> <span class="ot">TRUE</span>, <span class="dt">do_plot =</span> <span class="ot">TRUE</span>, ...) {
for(i in <span class="kw">seq_along</span>(x)) {
old_attr =<span class="st"> </span><span class="kw">attributes</span>(x[[i]])
x[[i]] =<span class="st"> </span>x[[i]] %>%<span class="st"> </span>dplyr::<span class="kw">bind_cols</span>(y[[i]] %>%<span class="st"> </span>dplyr::<span class="kw">select</span>(lowerCI, upperCI) %>%<span class="st"> </span>dplyr::<span class="kw">rename</span>(<span class="dt">lowerCI80 =</span> lowerCI, <span class="dt">upperCI80 =</span> upperCI))
new_col_names =<span class="st"> </span><span class="kw">attributes</span>(x[[i]])$names
<span class="kw">attributes</span>(x[[i]]) =<span class="st"> </span>old_attr
<span class="kw">attributes</span>(x[[i]])$names =<span class="st"> </span>new_col_names
}
if(<span class="st">"paternalage"</span> %in%<span class="st"> </span><span class="kw">names</span>(x)) {
order_x =<span class="st"> </span><span class="kw">names</span>(x)
order_x =<span class="st"> </span><span class="kw">c</span>(<span class="st">"paternalage"</span>, order_x[order_x !=<span class="st"> "paternalage"</span>])
x =<span class="st"> </span>x[order_x]
}
<span class="co"># Compute marginal effects plots using ggplot2</span>
<span class="co"># Returns:</span>
<span class="co"># A list of ggplot objects</span>
if (do_plot) {
default_ask <-<span class="st"> </span><span class="kw">devAskNewPage</span>()
<span class="kw">on.exit</span>(<span class="kw">devAskNewPage</span>(default_ask))
<span class="kw">devAskNewPage</span>(<span class="dt">ask =</span> <span class="ot">FALSE</span>)
}
plots <-<span class="st"> </span><span class="kw">setNames</span>(<span class="kw">vector</span>(<span class="st">"list"</span>, <span class="kw">length</span>(x)), <span class="kw">names</span>(x))
for (i in <span class="kw">seq_along</span>(x)) {
response <-<span class="st"> </span><span class="kw">attributes</span>(x[[i]])$response
effects <-<span class="st"> </span><span class="kw">attributes</span>(x[[i]])$effects
plots[[i]] <-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> x[[i]]) +
<span class="st"> </span><span class="kw">aes_string</span>(<span class="dt">x =</span> effects, <span class="dt">y =</span> <span class="st">"Estimate"</span>, <span class="dt">ymin =</span> <span class="st">"lowerCI"</span>,
<span class="dt">ymax =</span> <span class="st">"upperCI"</span>) +<span class="st"> </span><span class="kw">ylab</span>(response) +<span class="st"> </span>theme
nCond <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">unique</span>(x[[i]]$MargCond))
if (nCond ><span class="st"> </span><span class="dv">1</span>) {
<span class="co"># one plot per row of marginal_data</span>
if (<span class="kw">is.null</span>(ncol)) ncol <-<span class="st"> </span><span class="kw">max</span>(<span class="kw">floor</span>(<span class="kw">sqrt</span>(nCond)), <span class="dv">3</span>)
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">facet_wrap</span>(<span class="st">"MargCond"</span>, <span class="dt">ncol =</span> ncol)
}
if (<span class="kw">length</span>(effects) ==<span class="st"> </span><span class="dv">2</span>) {
<span class="co"># differentiate by colour in case of interaction effects</span>
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">aes_string</span>(<span class="dt">colour =</span> effects[<span class="dv">2</span>], <span class="dt">fill =</span> effects[<span class="dv">2</span>])
}
if (<span class="kw">is.numeric</span>(x[[i]][, effects[<span class="dv">1</span>]])) {
<span class="co"># smooth plots for numeric predictors</span>
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">geom_ribbon</span>(<span class="dt">alpha =</span> <span class="fl">0.2</span>, <span class="dt">color =</span> <span class="ot">NA</span>) +
<span class="st"> </span><span class="kw">geom_ribbon</span>(<span class="kw">aes_string</span>(<span class="dt">ymin =</span> <span class="st">"lowerCI80"</span>, <span class="dt">ymax =</span> <span class="st">"upperCI80"</span>), <span class="dt">alpha =</span> <span class="fl">0.2</span>, <span class="dt">color =</span> <span class="ot">NA</span>) +
<span class="st"> </span><span class="kw">geom_line</span>()
if (rug) {
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">geom_rug</span>(<span class="kw">aes_string</span>(<span class="dt">x =</span> effects[<span class="dv">1</span>]), <span class="dt">sides =</span> <span class="st">"b"</span>,
<span class="dt">data =</span> <span class="kw">attr</span>(x[[i]], <span class="st">"points"</span>), <span class="dt">inherit.aes =</span> <span class="ot">FALSE</span>)
}
} else {
<span class="co"># points and errorbars for factors</span>
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">geom_linerange</span>(<span class="kw">aes_string</span>(<span class="dt">ymin =</span> <span class="st">"lowerCI80"</span>, <span class="dt">ymax =</span> <span class="st">"upperCI80"</span>), <span class="dt">size =</span> <span class="fl">1.7</span>,
<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.4</span>)) +
<span class="st"> </span><span class="kw">geom_linerange</span>(<span class="kw">aes_string</span>(<span class="dt">ymin =</span> <span class="st">"lowerCI"</span>, <span class="dt">ymax =</span> <span class="st">"upperCI"</span>), <span class="dt">size =</span> <span class="fl">0.7</span>,
<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.4</span>)) +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="dt">width =</span> <span class="fl">0.4</span>),
<span class="dt">size =</span> <span class="dv">4</span> /<span class="st"> </span>nCond^<span class="fl">0.25</span>)
}
if (points) {
<span class="co"># show the data as points in the plot</span>
plots[[i]] <-<span class="st"> </span>plots[[i]] +
<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes_string</span>(<span class="dt">x =</span> effects[<span class="dv">1</span>], <span class="dt">y =</span> <span class="st">".RESP"</span>), <span class="dt">shape =</span> <span class="dv">1</span>,
<span class="dt">size =</span> <span class="dv">4</span> /<span class="st"> </span>nCond^<span class="fl">0.25</span>, <span class="dt">data =</span> <span class="kw">attr</span>(x[[i]], <span class="st">"points"</span>),
<span class="dt">inherit.aes =</span> <span class="ot">FALSE</span>)
}
if (do_plot) {
<span class="kw">plot</span>(plots[[i]])
if (i ==<span class="st"> </span><span class="dv">1</span>) <span class="kw">devAskNewPage</span>(<span class="dt">ask =</span> ask)
}
}
<span class="kw">invisible</span>(plots)
}</code></pre></div>
</div>
<div id="get-the-path-to-the-model-file-fitted-on-the-cluster" class="section level2">
<h2>Get the path to the model-file fitted on the cluster</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">get_coefficient_path =<span class="st"> </span>function(file, population) {
<span class="kw">paste0</span>(<span class="st">"coefs/"</span>, population, <span class="st">"/"</span>, file, <span class="st">".rds"</span>)
} </code></pre></div>
</div>
<div id="echo-knit-child-documents-as-asis_output" class="section level2">