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2_ddb_selective_episodes.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
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<title>Historical Sweden Selective Episodes</title>
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<body>
<div style="width:100%;height:200px;background-image:url('library/header/ddb.jpg');background-size:cover;"></div>
<div class="container-fluid main-container"><div class="row"><div class="col-md-4"><a href="index.html"><i class="glyphicon glyphicon-th-list"></i> Back to index</a></div></div></div>
<div class="container-fluid main-container">
<div id="demographic-data-base-sweden-1760-1880-selective-episodes" class="section level1 tab-content">
<h1>Demographic data base (Sweden 1760-1880), selective episodes</h1>
<div id="loading-details" class="section level3 accordion">
<h3>Loading details</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">opts_chunk$<span class="kw">set</span>(<span class="dt">warning=</span><span class="ot">TRUE</span>, <span class="dt">cache=</span>F,<span class="dt">tidy=</span><span class="ot">FALSE</span>,<span class="dt">dev=</span><span class="kw">c</span>(<span class="st">'png'</span>),<span class="dt">fig.width=</span><span class="dv">20</span>,<span class="dt">fig.height=</span><span class="fl">12.5</span>)
<span class="kw">source</span>(<span class="st">"0__helpers.R"</span>)</code></pre></div>
</div>
<div id="analysis-description" class="section level2">
<h2>Analysis description</h2>
<div id="data-subset" class="section level3">
<h3>Data subset</h3>
<p>The <code>ddb.1</code> dataset contains only those participants where paternal age is known, the birthdate is between 1760 and 1880 and the marriage is known (meaning we know when it started and how it ended by spousal death). In known marriages we can assume that missing death dates for the kids mean that they migrated out.</p>
</div>
<div id="model-description" class="section level3">
<h3>Model description</h3>
<p>All of the following models have the following in common:</p>
<div id="estimation" class="section level4">
<h4>Estimation</h4>
<p>We fit all models using <code>brms</code> v. 0.9.1, a Bayesian regression analysis statistical package. <code>brms</code> uses <code>Stan</code>, a statistical programming environment to fit models using Hamiltonian Monte Carlo.</p>
</div>
<div id="covariates" class="section level4">
<h4>Covariates</h4>
<p>As in our main models, we control for birth.cohort (birth years in five equally large bins), for male sex, for age at paternal and maternal loss, for maternal age (bins of 14-20, 20-35 and 35-50), for the number of older siblings (birth order), for the number of younger siblings, and for the number of younger siblings who where alive and dependent during the first 5 years of the child (defined as being alive simultaneously, less than 10 years old, and not dying in the first year).</p>
</div>
<div id="model-stratification" class="section level4">
<h4>Model stratification</h4>
<p>We added random intercepts for each family (father-mother dyad). We then controlled for the average paternal age in the family. Hence, the paternal age effects in the plot are split into those between families and those within families or between siblings. The relevant effect for our hypothesized mechanism, de novo mutations, is paternal age between siblings.</p>
</div>
<div id="episodes-of-selection" class="section level4">
<h4>Episodes of selection</h4>
<p>Here, episodes of selection refers to survival, mating success, reproductive success. To clearly separate the episodes from one another, we make sure that each effect cannot be explained by the episode of selection preceding it. At its simplest, we check for effect on survival to reproduction only among those who lived to their first birthday. In later models, we look for an effect on number of children only among those who married and control their number of spouses.</p>
</div>
<div id="code-for-generating-model-summaries" class="section level4 accordion">
<h4>Code for generating model summaries</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(knitr); <span class="kw">library</span>(dplyr); <span class="kw">library</span>(brms)
<span class="kw">source</span>(<span class="st">'0__helpers.R'</span>)
files =<span class="st"> </span><span class="kw">list.files</span>(<span class="st">"coefs/ddb"</span>,<span class="dt">full.names =</span> T, <span class="dt">pattern =</span> <span class="st">"^e"</span>)
model_names =<span class="st"> </span><span class="kw">c</span>(<span class="st">"Survival to first year"</span>, <span class="st">"Survival to reproductive age"</span>, <span class="st">"Ever married"</span>, <span class="st">"Number of children"</span>)
model_descriptions =<span class="st"> </span><span class="kw">c</span>(
<span class="st">"Probability of surviving the first year of life"</span>,
<span class="st">"Probability of surviving the first 15 years of life, only including people who survived their first year."</span>,
<span class="st">"Probability of ever marrying, only including people who lived to reproductive age."</span>,
<span class="st">"Number of children the anchor had, only including ever married people and adjusting for number of spouses."</span>)
out =<span class="st"> </span><span class="ot">NULL</span>
for (i in <span class="kw">seq_along</span>(files)) {
model_name =<span class="st"> </span>model_names[i]
model_description =<span class="st"> </span>model_descriptions[i]
model_filename =<span class="st"> </span>files[i]
model =<span class="st"> </span><span class="kw">readRDS</span>(files[i])
<span class="kw">message</span>(model_name)
out <-<span class="st"> </span><span class="kw">c</span>(out, <span class="kw">knit_child</span>(<span class="st">'~/paternal_age_genealogies/model_summary.Rmd'</span>, <span class="dt">quiet=</span>T))
}</code></pre></div>
</div>
</div>
</div>
<div id="survival-to-first-year" class="section level2 tab-content">
<h2>Survival to first year</h2>
<p>Probability of surviving the first year of life</p>
<div id="model-summary" class="section level3 tab-content">
<h3>Model summary</h3>
<div id="full-summary" class="section level4">
<h4>Full summary</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model_summary =<span class="st"> </span><span class="kw">summary</span>(model, <span class="dt">use_cache =</span> <span class="ot">FALSE</span>)
model_summary</code></pre></div>
<pre><code>## Family: bernoulli (cauchit)
## Formula: survive1y ~ paternalage.diff + birth_cohort + male + maternalage.factor + paternalage.mean + paternal_loss + maternal_loss + older_siblings + nr.siblings + last_born + (1 | idParents)
## Data: model_data (Number of observations: 174737)
## Samples: 6 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup samples = 3000
## WAIC: Not computed
##
## Group-Level Effects:
## ~idParents (Number of levels: 48140)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 1.6 0.03 1.54 1.65 854 1.01
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## Intercept 5.01 0.53 4.02 6.08 172
## paternalage.diff -1.45 0.07 -1.59 -1.31 881
## birth_cohort1750M1755 0.34 1.06 -1.45 2.77 400
## birth_cohort1755M1760 -1.11 0.61 -2.38 0.09 215
## birth_cohort1760M1765 -1.57 0.57 -2.72 -0.47 186
## birth_cohort1765M1770 -0.46 0.64 -1.68 0.79 247
## birth_cohort1770M1775 -0.59 0.63 -1.87 0.66 227
## birth_cohort1775M1780 -1.93 0.54 -3.02 -0.91 172
## birth_cohort1780M1785 -2.08 0.54 -3.15 -1.06 163
## birth_cohort1785M1790 -1.65 0.54 -2.75 -0.63 171
## birth_cohort1790M1795 -1.63 0.52 -2.70 -0.66 159
## birth_cohort1795M1800 -1.24 0.53 -2.33 -0.22 166
## birth_cohort1800M1805 -1.16 0.53 -2.24 -0.16 164
## birth_cohort1805M1810 -1.36 0.52 -2.46 -0.36 163
## birth_cohort1810M1815 -1.49 0.52 -2.57 -0.51 161
## birth_cohort1815M1820 -1.48 0.52 -2.55 -0.49 162
## birth_cohort1820M1825 -1.02 0.52 -2.10 -0.03 164
## birth_cohort1825M1830 -0.85 0.52 -1.92 0.13 163
## birth_cohort1830M1835 -1.02 0.52 -2.08 -0.04 162
## birth_cohort1835M1840 -0.90 0.52 -1.98 0.08 162
## birth_cohort1840M1845 -0.58 0.52 -1.66 0.40 159
## birth_cohort1845M1850 -0.44 0.52 -1.50 0.55 161
## birth_cohort1850M1855 -0.57 0.52 -1.65 0.41 162
## birth_cohort1855M1860 -0.42 0.52 -1.48 0.56 161
## birth_cohort1860M1865 -0.55 0.52 -1.64 0.42 160
## birth_cohort1865M1870 -0.61 0.52 -1.69 0.36 159
## birth_cohort1870M1875 -0.73 0.52 -1.81 0.24 162
## birth_cohort1875M1880 -0.61 0.52 -1.68 0.35 160
## male1 -0.37 0.03 -0.43 -0.32 3000
## maternalage.factor1020 -0.01 0.13 -0.26 0.25 3000
## maternalage.factor3559 -0.15 0.04 -0.23 -0.06 2134
## paternalage.mean 0.09 0.03 0.03 0.15 1934
## paternal_loss01 -1.11 0.12 -1.35 -0.87 939
## paternal_loss15 -0.53 0.10 -0.72 -0.32 728
## paternal_loss510 -0.24 0.10 -0.44 -0.05 576
## paternal_loss1015 -0.20 0.09 -0.38 -0.01 656
## paternal_loss1520 -0.13 0.09 -0.31 0.05 657
## paternal_loss2025 -0.02 0.09 -0.19 0.16 663
## paternal_loss2530 -0.06 0.09 -0.22 0.11 696
## paternal_loss3035 -0.08 0.09 -0.25 0.09 732
## paternal_loss3540 0.05 0.09 -0.12 0.22 975
## paternal_loss4045 0.15 0.10 -0.04 0.34 1169
## paternal_lossunclear 0.04 0.08 -0.11 0.21 568
## maternal_loss01 -3.78 0.11 -4.01 -3.57 749
## maternal_loss15 -1.19 0.10 -1.39 -1.00 668
## maternal_loss510 -0.83 0.09 -1.01 -0.65 715
## maternal_loss1015 -0.50 0.09 -0.68 -0.32 729
## maternal_loss1520 -0.38 0.10 -0.57 -0.19 776
## maternal_loss2025 -0.44 0.09 -0.61 -0.27 788
## maternal_loss2530 -0.37 0.09 -0.55 -0.20 754
## maternal_loss3035 -0.31 0.08 -0.48 -0.15 810
## maternal_loss3540 -0.10 0.08 -0.26 0.06 858
## maternal_loss4045 -0.09 0.09 -0.27 0.08 1122
## maternal_lossunclear -0.08 0.08 -0.24 0.08 578
## older_siblings1 0.56 0.05 0.46 0.65 1369
## older_siblings2 0.83 0.06 0.71 0.94 1070
## older_siblings3 1.05 0.07 0.92 1.19 900
## older_siblings4 1.39 0.08 1.23 1.55 861
## older_siblings5P 2.01 0.11 1.80 2.22 783
## nr.siblings -0.25 0.01 -0.27 -0.23 882
## last_born1 -0.14 0.04 -0.22 -0.07 3000
## Rhat
## Intercept 1.03
## paternalage.diff 1.01
## birth_cohort1750M1755 1.01
## birth_cohort1755M1760 1.02
## birth_cohort1760M1765 1.03
## birth_cohort1765M1770 1.02
## birth_cohort1770M1775 1.02
## birth_cohort1775M1780 1.03
## birth_cohort1780M1785 1.03
## birth_cohort1785M1790 1.03
## birth_cohort1790M1795 1.03
## birth_cohort1795M1800 1.03
## birth_cohort1800M1805 1.03
## birth_cohort1805M1810 1.03
## birth_cohort1810M1815 1.03
## birth_cohort1815M1820 1.03
## birth_cohort1820M1825 1.03
## birth_cohort1825M1830 1.03
## birth_cohort1830M1835 1.03
## birth_cohort1835M1840 1.03
## birth_cohort1840M1845 1.03
## birth_cohort1845M1850 1.03
## birth_cohort1850M1855 1.03
## birth_cohort1855M1860 1.03
## birth_cohort1860M1865 1.03
## birth_cohort1865M1870 1.03
## birth_cohort1870M1875 1.03
## birth_cohort1875M1880 1.03
## male1 1.00
## maternalage.factor1020 1.00
## maternalage.factor3559 1.00
## paternalage.mean 1.00
## paternal_loss01 1.01
## paternal_loss15 1.01
## paternal_loss510 1.01
## paternal_loss1015 1.01
## paternal_loss1520 1.01
## paternal_loss2025 1.01
## paternal_loss2530 1.01
## paternal_loss3035 1.01
## paternal_loss3540 1.00
## paternal_loss4045 1.00
## paternal_lossunclear 1.01
## maternal_loss01 1.01
## maternal_loss15 1.01
## maternal_loss510 1.00
## maternal_loss1015 1.00
## maternal_loss1520 1.00
## maternal_loss2025 1.01
## maternal_loss2530 1.01
## maternal_loss3035 1.01
## maternal_loss3540 1.00
## maternal_loss4045 1.00
## maternal_lossunclear 1.01
## older_siblings1 1.00
## older_siblings2 1.01
## older_siblings3 1.01
## older_siblings4 1.01
## older_siblings5P 1.01
## nr.siblings 1.01
## last_born1 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).</code></pre>
</div>
<div id="table-of-fixed-effects" class="section level4">
<h4>Table of fixed effects</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fixed_eff =<span class="st"> </span><span class="kw">data.frame</span>(model_summary$fixed, <span class="dt">check.names =</span> F)
fixed_eff$Est.Error =<span class="st"> </span>fixed_eff$Eff.Sample =<span class="st"> </span>fixed_eff$Rhat =<span class="st"> </span><span class="ot">NULL</span>
fixed_eff$OR =<span class="st"> </span><span class="kw">exp</span>(fixed_eff$Estimate)
fixed_eff$OR_low =<span class="st"> </span><span class="kw">exp</span>(fixed_eff$<span class="st">`</span><span class="dt">l-95% CI</span><span class="st">`</span>)
fixed_eff$OR_high =<span class="st"> </span><span class="kw">exp</span>(fixed_eff$<span class="st">`</span><span class="dt">u-95% CI</span><span class="st">`</span>)
pander::<span class="kw">pander</span>(fixed_eff)</code></pre></div>
<table>
<colgroup>
<col width="32%" />
<col width="12%" />
<col width="12%" />
<col width="12%" />
<col width="9%" />
<col width="10%" />
<col width="10%" />
</colgroup>
<thead>
<tr class="header">
<th align="center"> </th>
<th align="center">Estimate</th>
<th align="center">l-95% CI</th>
<th align="center">u-95% CI</th>
<th align="center">OR</th>
<th align="center">OR_low</th>
<th align="center">OR_high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><strong>Intercept</strong></td>
<td align="center">5.012</td>
<td align="center">4.024</td>
<td align="center">6.081</td>
<td align="center">150.3</td>
<td align="center">55.95</td>
<td align="center">437.6</td>
</tr>
<tr class="even">
<td align="center"><strong>paternalage.diff</strong></td>
<td align="center">-1.452</td>
<td align="center">-1.593</td>
<td align="center">-1.313</td>
<td align="center">0.2342</td>
<td align="center">0.2032</td>
<td align="center">0.2691</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1750M1755</strong></td>
<td align="center">0.3385</td>
<td align="center">-1.45</td>
<td align="center">2.773</td>
<td align="center">1.403</td>
<td align="center">0.2346</td>
<td align="center">16</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1755M1760</strong></td>
<td align="center">-1.115</td>
<td align="center">-2.376</td>
<td align="center">0.09069</td>
<td align="center">0.3281</td>
<td align="center">0.09288</td>
<td align="center">1.095</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1760M1765</strong></td>
<td align="center">-1.571</td>
<td align="center">-2.721</td>
<td align="center">-0.4697</td>
<td align="center">0.2078</td>
<td align="center">0.06581</td>
<td align="center">0.6252</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1765M1770</strong></td>
<td align="center">-0.4566</td>
<td align="center">-1.683</td>
<td align="center">0.7889</td>
<td align="center">0.6334</td>
<td align="center">0.1858</td>
<td align="center">2.201</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1770M1775</strong></td>
<td align="center">-0.5902</td>
<td align="center">-1.867</td>
<td align="center">0.6628</td>
<td align="center">0.5542</td>
<td align="center">0.1546</td>
<td align="center">1.94</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1775M1780</strong></td>
<td align="center">-1.925</td>
<td align="center">-3.024</td>
<td align="center">-0.9072</td>
<td align="center">0.1458</td>
<td align="center">0.04859</td>
<td align="center">0.4036</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1780M1785</strong></td>
<td align="center">-2.076</td>
<td align="center">-3.154</td>
<td align="center">-1.063</td>
<td align="center">0.1254</td>
<td align="center">0.04267</td>
<td align="center">0.3455</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1785M1790</strong></td>
<td align="center">-1.654</td>
<td align="center">-2.745</td>
<td align="center">-0.6317</td>
<td align="center">0.1912</td>
<td align="center">0.06424</td>
<td align="center">0.5317</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1790M1795</strong></td>
<td align="center">-1.628</td>
<td align="center">-2.695</td>
<td align="center">-0.657</td>
<td align="center">0.1963</td>
<td align="center">0.06754</td>
<td align="center">0.5184</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1795M1800</strong></td>
<td align="center">-1.236</td>
<td align="center">-2.334</td>
<td align="center">-0.2185</td>
<td align="center">0.2906</td>
<td align="center">0.09694</td>
<td align="center">0.8037</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1800M1805</strong></td>
<td align="center">-1.16</td>
<td align="center">-2.243</td>
<td align="center">-0.1563</td>
<td align="center">0.3135</td>
<td align="center">0.1061</td>
<td align="center">0.8553</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1805M1810</strong></td>
<td align="center">-1.356</td>
<td align="center">-2.461</td>
<td align="center">-0.3553</td>
<td align="center">0.2576</td>
<td align="center">0.08534</td>
<td align="center">0.701</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1810M1815</strong></td>
<td align="center">-1.494</td>
<td align="center">-2.572</td>
<td align="center">-0.5147</td>
<td align="center">0.2245</td>
<td align="center">0.07641</td>
<td align="center">0.5977</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1815M1820</strong></td>
<td align="center">-1.482</td>
<td align="center">-2.548</td>
<td align="center">-0.491</td>
<td align="center">0.2273</td>
<td align="center">0.07827</td>
<td align="center">0.612</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1820M1825</strong></td>
<td align="center">-1.018</td>
<td align="center">-2.099</td>
<td align="center">-0.02741</td>
<td align="center">0.3612</td>
<td align="center">0.1226</td>
<td align="center">0.973</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1825M1830</strong></td>
<td align="center">-0.8499</td>
<td align="center">-1.919</td>
<td align="center">0.1321</td>
<td align="center">0.4275</td>
<td align="center">0.1467</td>
<td align="center">1.141</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1830M1835</strong></td>
<td align="center">-1.018</td>
<td align="center">-2.082</td>
<td align="center">-0.0366</td>
<td align="center">0.3615</td>
<td align="center">0.1247</td>
<td align="center">0.9641</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1835M1840</strong></td>
<td align="center">-0.8999</td>
<td align="center">-1.979</td>
<td align="center">0.08428</td>
<td align="center">0.4066</td>
<td align="center">0.1382</td>
<td align="center">1.088</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1840M1845</strong></td>
<td align="center">-0.5797</td>
<td align="center">-1.657</td>
<td align="center">0.4018</td>
<td align="center">0.56</td>
<td align="center">0.1907</td>
<td align="center">1.495</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1845M1850</strong></td>
<td align="center">-0.4405</td>
<td align="center">-1.503</td>
<td align="center">0.5465</td>
<td align="center">0.6437</td>
<td align="center">0.2226</td>
<td align="center">1.727</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1850M1855</strong></td>
<td align="center">-0.5714</td>
<td align="center">-1.649</td>
<td align="center">0.4091</td>
<td align="center">0.5647</td>
<td align="center">0.1923</td>
<td align="center">1.506</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1855M1860</strong></td>
<td align="center">-0.4164</td>
<td align="center">-1.484</td>
<td align="center">0.5618</td>
<td align="center">0.6594</td>
<td align="center">0.2268</td>
<td align="center">1.754</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1860M1865</strong></td>
<td align="center">-0.5544</td>
<td align="center">-1.638</td>
<td align="center">0.418</td>
<td align="center">0.5744</td>
<td align="center">0.1944</td>
<td align="center">1.519</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1865M1870</strong></td>
<td align="center">-0.6146</td>
<td align="center">-1.686</td>
<td align="center">0.3577</td>
<td align="center">0.5408</td>
<td align="center">0.1853</td>
<td align="center">1.43</td>
</tr>
<tr class="odd">
<td align="center"><strong>birth_cohort1870M1875</strong></td>
<td align="center">-0.733</td>
<td align="center">-1.812</td>
<td align="center">0.2366</td>
<td align="center">0.4804</td>
<td align="center">0.1633</td>
<td align="center">1.267</td>
</tr>
<tr class="even">
<td align="center"><strong>birth_cohort1875M1880</strong></td>
<td align="center">-0.6123</td>
<td align="center">-1.676</td>
<td align="center">0.355</td>
<td align="center">0.5421</td>
<td align="center">0.1872</td>
<td align="center">1.426</td>
</tr>
<tr class="odd">
<td align="center"><strong>male1</strong></td>
<td align="center">-0.3736</td>
<td align="center">-0.4318</td>
<td align="center">-0.3172</td>
<td align="center">0.6882</td>
<td align="center">0.6493</td>
<td align="center">0.7282</td>
</tr>
<tr class="even">
<td align="center"><strong>maternalage.factor1020</strong></td>
<td align="center">-0.00716</td>
<td align="center">-0.2577</td>
<td align="center">0.2543</td>
<td align="center">0.9929</td>
<td align="center">0.7728</td>
<td align="center">1.29</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternalage.factor3559</strong></td>
<td align="center">-0.1452</td>
<td align="center">-0.2285</td>
<td align="center">-0.06023</td>
<td align="center">0.8649</td>
<td align="center">0.7957</td>
<td align="center">0.9415</td>
</tr>
<tr class="even">
<td align="center"><strong>paternalage.mean</strong></td>
<td align="center">0.09031</td>
<td align="center">0.03194</td>
<td align="center">0.1506</td>
<td align="center">1.095</td>
<td align="center">1.032</td>
<td align="center">1.163</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_loss01</strong></td>
<td align="center">-1.107</td>
<td align="center">-1.346</td>
<td align="center">-0.8703</td>
<td align="center">0.3306</td>
<td align="center">0.2604</td>
<td align="center">0.4188</td>
</tr>
<tr class="even">
<td align="center"><strong>paternal_loss15</strong></td>
<td align="center">-0.5293</td>
<td align="center">-0.7244</td>
<td align="center">-0.3221</td>
<td align="center">0.589</td>
<td align="center">0.4846</td>
<td align="center">0.7246</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_loss510</strong></td>
<td align="center">-0.2426</td>
<td align="center">-0.438</td>
<td align="center">-0.05015</td>
<td align="center">0.7846</td>
<td align="center">0.6453</td>
<td align="center">0.9511</td>
</tr>
<tr class="even">
<td align="center"><strong>paternal_loss1015</strong></td>
<td align="center">-0.1975</td>
<td align="center">-0.3798</td>
<td align="center">-0.01207</td>
<td align="center">0.8208</td>
<td align="center">0.684</td>
<td align="center">0.988</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_loss1520</strong></td>
<td align="center">-0.1326</td>
<td align="center">-0.3074</td>
<td align="center">0.049</td>
<td align="center">0.8758</td>
<td align="center">0.7353</td>
<td align="center">1.05</td>
</tr>
<tr class="even">
<td align="center"><strong>paternal_loss2025</strong></td>
<td align="center">-0.01738</td>
<td align="center">-0.1928</td>
<td align="center">0.1601</td>
<td align="center">0.9828</td>
<td align="center">0.8247</td>
<td align="center">1.174</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_loss2530</strong></td>
<td align="center">-0.05797</td>
<td align="center">-0.2218</td>
<td align="center">0.1138</td>
<td align="center">0.9437</td>
<td align="center">0.801</td>
<td align="center">1.121</td>
</tr>
<tr class="even">
<td align="center"><strong>paternal_loss3035</strong></td>
<td align="center">-0.0827</td>
<td align="center">-0.2491</td>
<td align="center">0.09208</td>
<td align="center">0.9206</td>
<td align="center">0.7795</td>
<td align="center">1.096</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_loss3540</strong></td>
<td align="center">0.05394</td>
<td align="center">-0.1183</td>
<td align="center">0.2202</td>
<td align="center">1.055</td>
<td align="center">0.8884</td>
<td align="center">1.246</td>
</tr>
<tr class="even">
<td align="center"><strong>paternal_loss4045</strong></td>
<td align="center">0.1493</td>
<td align="center">-0.03969</td>
<td align="center">0.3392</td>
<td align="center">1.161</td>
<td align="center">0.9611</td>
<td align="center">1.404</td>
</tr>
<tr class="odd">
<td align="center"><strong>paternal_lossunclear</strong></td>
<td align="center">0.04332</td>
<td align="center">-0.1125</td>
<td align="center">0.2068</td>
<td align="center">1.044</td>
<td align="center">0.8936</td>
<td align="center">1.23</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_loss01</strong></td>
<td align="center">-3.784</td>
<td align="center">-4.006</td>
<td align="center">-3.574</td>
<td align="center">0.02273</td>
<td align="center">0.0182</td>
<td align="center">0.02805</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternal_loss15</strong></td>
<td align="center">-1.195</td>
<td align="center">-1.393</td>
<td align="center">-1.001</td>
<td align="center">0.3028</td>
<td align="center">0.2483</td>
<td align="center">0.3675</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_loss510</strong></td>
<td align="center">-0.8331</td>
<td align="center">-1.013</td>
<td align="center">-0.6489</td>
<td align="center">0.4347</td>
<td align="center">0.3632</td>
<td align="center">0.5226</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternal_loss1015</strong></td>
<td align="center">-0.5033</td>
<td align="center">-0.6847</td>
<td align="center">-0.3179</td>
<td align="center">0.6045</td>
<td align="center">0.5042</td>
<td align="center">0.7277</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_loss1520</strong></td>
<td align="center">-0.3769</td>
<td align="center">-0.5701</td>
<td align="center">-0.1896</td>
<td align="center">0.686</td>
<td align="center">0.5655</td>
<td align="center">0.8273</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternal_loss2025</strong></td>
<td align="center">-0.4443</td>
<td align="center">-0.6119</td>
<td align="center">-0.2681</td>
<td align="center">0.6412</td>
<td align="center">0.5423</td>
<td align="center">0.7648</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_loss2530</strong></td>
<td align="center">-0.374</td>
<td align="center">-0.5463</td>
<td align="center">-0.2025</td>
<td align="center">0.688</td>
<td align="center">0.5791</td>
<td align="center">0.8167</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternal_loss3035</strong></td>
<td align="center">-0.3145</td>
<td align="center">-0.4835</td>
<td align="center">-0.1464</td>
<td align="center">0.7301</td>
<td align="center">0.6166</td>
<td align="center">0.8638</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_loss3540</strong></td>
<td align="center">-0.1005</td>
<td align="center">-0.2646</td>
<td align="center">0.0618</td>
<td align="center">0.9044</td>
<td align="center">0.7675</td>
<td align="center">1.064</td>
</tr>
<tr class="odd">
<td align="center"><strong>maternal_loss4045</strong></td>
<td align="center">-0.09448</td>
<td align="center">-0.2659</td>
<td align="center">0.08091</td>
<td align="center">0.9098</td>
<td align="center">0.7665</td>
<td align="center">1.084</td>
</tr>
<tr class="even">
<td align="center"><strong>maternal_lossunclear</strong></td>
<td align="center">-0.07751</td>
<td align="center">-0.2365</td>
<td align="center">0.07593</td>
<td align="center">0.9254</td>
<td align="center">0.7894</td>
<td align="center">1.079</td>
</tr>
<tr class="odd">
<td align="center"><strong>older_siblings1</strong></td>
<td align="center">0.5608</td>
<td align="center">0.4637</td>
<td align="center">0.6548</td>
<td align="center">1.752</td>
<td align="center">1.59</td>
<td align="center">1.925</td>
</tr>
<tr class="even">
<td align="center"><strong>older_siblings2</strong></td>
<td align="center">0.8285</td>
<td align="center">0.713</td>
<td align="center">0.942</td>
<td align="center">2.29</td>
<td align="center">2.04</td>
<td align="center">2.565</td>
</tr>
<tr class="odd">
<td align="center"><strong>older_siblings3</strong></td>
<td align="center">1.051</td>
<td align="center">0.9163</td>
<td align="center">1.187</td>
<td align="center">2.86</td>
<td align="center">2.5</td>
<td align="center">3.276</td>
</tr>
<tr class="even">
<td align="center"><strong>older_siblings4</strong></td>
<td align="center">1.39</td>
<td align="center">1.227</td>
<td align="center">1.552</td>
<td align="center">4.017</td>
<td align="center">3.411</td>
<td align="center">4.72</td>
</tr>
<tr class="odd">
<td align="center"><strong>older_siblings5P</strong></td>
<td align="center">2.006</td>
<td align="center">1.797</td>
<td align="center">2.225</td>
<td align="center">7.435</td>
<td align="center">6.034</td>
<td align="center">9.252</td>
</tr>
<tr class="even">
<td align="center"><strong>nr.siblings</strong></td>
<td align="center">-0.2514</td>
<td align="center">-0.2703</td>
<td align="center">-0.2323</td>
<td align="center">0.7777</td>
<td align="center">0.7632</td>
<td align="center">0.7927</td>
</tr>
<tr class="odd">
<td align="center"><strong>last_born1</strong></td>
<td align="center">-0.1432</td>
<td align="center">-0.2158</td>
<td align="center">-0.07256</td>
<td align="center">0.8665</td>
<td align="center">0.8059</td>
<td align="center">0.93</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="paternal-age-effect" class="section level3">
<h3>Paternal age effect</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pander::<span class="kw">pander</span>(<span class="kw">paternal_age_10y_effect</span>(model))</code></pre></div>
<table>
<colgroup>
<col width="39%" />
<col width="23%" />
<col width="18%" />
<col width="18%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">effect</th>
<th align="center">median_estimate</th>
<th align="center">ci_95</th>
<th align="center">ci_80</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">estimate father -5y</td>
<td align="center">0.93</td>
<td align="center">[0.91;0.94]</td>
<td align="center">[0.92;0.94]</td>
</tr>
<tr class="even">
<td align="center">estimate father +5y</td>
<td align="center">0.9</td>
<td align="center">[0.86;0.92]</td>
<td align="center">[0.87;0.92]</td>
</tr>
<tr class="odd">
<td align="center">percentage change</td>
<td align="center">-3</td>
<td align="center">[-6;-2]</td>
<td align="center">[-5;-2]</td>
</tr>
<tr class="even">
<td align="center">absolute change for reference individual</td>
<td align="center">-0.03</td>
<td align="center">[-0.06;-0.02]</td>
<td align="center">[-0.05;-0.02]</td>
</tr>
</tbody>
</table>
</div>
<div id="marginal-effect-plots" class="section level3">
<h3>Marginal effect plots</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot.brmsMarginalEffects_shades</span>(
<span class="dt">x =</span> <span class="kw">marginal_effects</span>(model, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">probs =</span> <span class="kw">c</span>(<span class="fl">0.025</span>,<span class="fl">0.975</span>)),
<span class="dt">y =</span> <span class="kw">marginal_effects</span>(model, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">probs =</span> <span class="kw">c</span>(<span class="fl">0.1</span>,<span class="fl">0.9</span>)),
<span class="dt">ask =</span> <span class="ot">FALSE</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-1.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-2.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-3.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-4.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-5.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-6.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-7.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-8.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-9.png" /><!-- --><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-8-10.png" /><!-- --></p>
</div>
<div id="coefficient-plot" class="section level3">
<h3>Coefficient plot</h3>
<p>Coefficient estimates (95% and 80% credibility).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mcmc_intervals</span>(<span class="kw">as.matrix</span>(model$fit), <span class="dt">regex_pars =</span> <span class="st">"b_[^I]"</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-9-1.png" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mcmc_areas</span>(<span class="kw">as.matrix</span>(model$fit), <span class="dt">regex_pars =</span> <span class="st">"b"</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-9-2.png" /><!-- --></p>
</div>
<div id="diagnostics" class="section level3 tab-content">
<h3>Diagnostics</h3>
<p>These plots were made to diagnose misfit and nonconvergence.</p>
<div id="posterior-predictive-checks" class="section level4">
<h4>Posterior predictive checks</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">brms::<span class="kw">pp_check</span>(model, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">type =</span> <span class="st">"dens_overlay"</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-10-1.png" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">brms::<span class="kw">pp_check</span>(model, <span class="dt">re_formula =</span> <span class="ot">NA</span>, <span class="dt">type =</span> <span class="st">"hist"</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-10-2.png" /><!-- --></p>
</div>
<div id="rhat" class="section level4">
<h4>Rhat</h4>
<p>Did the 6 chains converge?</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">stanplot</span>(model, <span class="dt">pars =</span> <span class="st">"^b_[^I]"</span>, <span class="dt">type =</span> <span class="st">'rhat'</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-11-1.png" /><!-- --></p>
</div>
<div id="effective-sample-size-by-average-sample-size" class="section level4">
<h4>Effective sample size by average sample size</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">stanplot</span>(model, <span class="dt">pars =</span> <span class="st">"^b"</span>, <span class="dt">type =</span> <span class="st">'ess'</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-12-1.png" /><!-- --></p>
</div>
<div id="monte-carlo-se" class="section level4">
<h4>Monte Carlo SE</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">stanplot</span>(model, <span class="dt">pars =</span> <span class="st">"^b"</span>, <span class="dt">type =</span> <span class="st">'mcse'</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-13-1.png" /><!-- --></p>
</div>
<div id="trace-plots" class="section level4">
<h4>Trace plots</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">if(<span class="kw">any</span>( <span class="kw">summary</span>(model)$fixed[,<span class="st">"Rhat"</span>] ><span class="st"> </span><span class="fl">1.1</span>)) { <span class="co"># only do traceplots if not converged</span>
<span class="kw">plot</span>(model, <span class="dt">N =</span> <span class="dv">3</span>, <span class="dt">ask =</span> <span class="ot">FALSE</span>)
}</code></pre></div>
</div>
<div id="further-plots" class="section level4">
<h4>Further plots</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">stanplot</span>(model, <span class="dt">pars =</span> <span class="st">"^b"</span>, <span class="dt">type =</span> <span class="st">'diag'</span>)</code></pre></div>
<p><img src="2_ddb_selective_episodes_files/figure-html/unnamed-chunk-15-1.png" /><!-- --></p>
</div>
<div id="file-name" class="section level4">
<h4>File name</h4>
<p>coefs/ddb/e1_survive1y.rds</p>
</div>
</div>
</div>
<div id="survival-to-reproductive-age" class="section level2 tab-content">
<h2>Survival to reproductive age</h2>
<p>Probability of surviving the first 15 years of life, only including people who survived their first year.</p>
<div id="model-summary-1" class="section level3 tab-content">
<h3>Model summary</h3>
<div id="full-summary-1" class="section level4">
<h4>Full summary</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model_summary =<span class="st"> </span><span class="kw">summary</span>(model, <span class="dt">use_cache =</span> <span class="ot">FALSE</span>)