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m1_children_linear_noranef.html
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<!DOCTYPE html>
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<title> Children Linear Noranef</title>
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<div id="run-model-on-cluster" class="section level1">
<h1>Run model on cluster</h1>
<p>this script runs a model on our scientific computing cluster</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(dplyr); <span class="kw">library</span>(brms)
<span class="kw">setwd</span>(<span class="st">"/usr/users/rarslan/updated_data/"</span>)
args =<span class="st"> </span><span class="kw">commandArgs</span>()
dataset =<span class="st"> </span>args[<span class="dv">6</span>]
uptobyear =<span class="st"> </span>args[<span class="dv">7</span>]
model_formula =<span class="st"> </span>children ~<span class="st"> </span>paternalage +<span class="st"> </span>birth_cohort +<span class="st"> </span>male +<span class="st"> </span>maternalage.factor +<span class="st"> </span>paternal_loss +<span class="st"> </span>maternal_loss +<span class="st"> </span>older_siblings +<span class="st"> </span>nr.siblings +<span class="st"> </span>last_born
if (dataset ==<span class="st"> "swed"</span>) {
<span class="kw">load</span>(<span class="st">"swed1.rdata"</span>)
model_prior =<span class="st"> </span><span class="kw">c</span>(<span class="kw">set_prior</span>(<span class="st">"normal(0,5)"</span>, <span class="dt">class =</span> <span class="st">"b"</span>),
<span class="kw">set_prior</span>(<span class="st">"student_t(3, 0, 5)"</span>, <span class="dt">class =</span> <span class="st">"sd"</span>))
model_family =<span class="st"> </span><span class="kw">poisson</span>()
} else {
<span class="kw">load</span>(<span class="kw">paste0</span>(dataset, <span class="st">".rdata"</span>))
model_prior =<span class="st"> </span><span class="kw">c</span>(<span class="kw">set_prior</span>(<span class="st">"normal(0,5)"</span>, <span class="dt">class =</span> <span class="st">"b"</span>),
<span class="kw">set_prior</span>(<span class="st">"normal(0,5)"</span>, <span class="dt">class =</span> <span class="st">"b"</span>, <span class="dt">nlpar =</span> <span class="st">"hu"</span>),
<span class="kw">set_prior</span>(<span class="st">"student_t(3, 0, 5)"</span>, <span class="dt">class =</span> <span class="st">"sd"</span>))
model_formula_hu =<span class="st"> </span><span class="kw">update</span>(model_formula, hu ~<span class="st"> </span>. )
model_formula =<span class="st"> </span><span class="kw">bf</span>(model_formula, model_formula_hu)
model_family =<span class="st"> </span><span class="kw">hurdle_poisson</span>()
}
model_data =<span class="st"> </span><span class="kw">get</span>(<span class="kw">paste0</span>(dataset, <span class="st">".1"</span>)) %>%<span class="st"> </span>tbl_df %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">filter</span>(byear <<span class="st"> </span>uptobyear) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(children, birth_cohort, male, maternalage.factor, paternalage, paternal_loss, maternal_loss, older_siblings, nr.siblings, last_born, idParents) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">na.omit</span>()
model =<span class="st"> </span><span class="kw">brm</span>( model_formula,
<span class="dt">prior =</span> model_prior,
<span class="dt">family =</span> model_family, <span class="dt">data =</span> model_data,
<span class="dt">chains =</span> <span class="dv">6</span>, <span class="dt">iter =</span> <span class="dv">800</span>, <span class="dt">warmup =</span> <span class="dv">300</span>, <span class="dt">cores =</span> <span class="dv">6</span>, <span class="dt">ranef =</span> <span class="ot">TRUE</span>)
<span class="kw">summary</span>(model)
<span class="kw">saveRDS</span>(model,<span class="dt">file =</span> <span class="kw">paste0</span>(<span class="st">"coefs/"</span>, dataset, <span class="st">"/m1_children_linear_noranef.rds"</span>))</code></pre></div>
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