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<!DOCTYPE html>
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<div id="item-level-analyses" class="section level1 tab-content">
<h1>Item-level analyses</h1>
<p><span style="background:red;width:20px;height:20px;display:inline-block;"></span> Cycling women (not on hormonal birth control)</p>
<p><span style="background:black;width:20px;height:20px;display:inline-block;"></span> Women on hormonal birth control</p>
<div id="load-data" class="section level2">
<h2>Load data</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(knitr)
opts_chunk$<span class="kw">set</span>(<span class="dt">fig.width =</span> <span class="dv">8</span>, <span class="dt">fig.height =</span> <span class="dv">8</span>, <span class="dt">cache =</span> F, <span class="dt">warning =</span> F, <span class="dt">message =</span> F)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(xlsx); <span class="kw">library</span>(formr); <span class="kw">library</span>(data.table); <span class="kw">library</span>(stringr); <span class="kw">library</span>(ggplot2); <span class="kw">library</span>(plyr); <span class="kw">library</span>(car); <span class="kw">library</span>(psych); <span class="kw">library</span>(brms);
<span class="kw">source</span>(<span class="st">"0_helpers.R"</span>)
<span class="kw">load</span>(<span class="st">"full_data.rdata"</span>)
diary$extra_pair_intimacy_sex =<span class="st"> </span>diary$extra_pair_sex +<span class="st"> </span>diary$extra_pair_intimacy
twice =<span class="st"> </span><span class="kw">c</span>(<span class="st">"1"</span> =<span class="st"> </span><span class="dv">1</span>, <span class="st">"2"</span> =<span class="st"> </span><span class="dv">2</span>, <span class="st">"3"</span> =<span class="st"> </span><span class="dv">0</span>)
diary$sexual_intercourse_2 =<span class="st"> </span>twice[diary$sexual_intercourse_2]
diary$sexual_intercourse_5 =<span class="st"> </span>twice[diary$sexual_intercourse_5]
diary$mate_retention_2 =<span class="st"> </span>twice[diary$mate_retention_2]
diary$relationship_satisfaction_1 =<span class="st"> </span><span class="kw">as.integer</span>(diary$relationship_satisfaction_1)
diary$sexual_intercourse_satisfaction =<span class="st"> </span><span class="kw">as.integer</span>(diary$sexual_intercourse_satisfaction)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">items =<span class="st"> </span>xlsx::<span class="kw">read.xlsx</span>(<span class="st">"item_tables/Taeglicher_Fragebogen_1-v3.xls"</span>, <span class="dv">1</span>)
items_engl =<span class="st"> </span>xlsx::<span class="kw">read.xlsx</span>(<span class="st">"item_tables/Daily_items_bearbeitetAM.xlsx"</span>, <span class="dv">1</span>)
outcomes =<span class="st"> </span><span class="kw">list.files</span>(<span class="st">"by_item"</span>,<span class="dt">full.names =</span> T)
fertile_eff_by_item =<span class="st"> </span><span class="kw">list</span>()
for (i in <span class="dv">1</span>:<span class="kw">length</span>(outcomes)) {
<span class="kw">tryCatch</span>({
fit =<span class="st"> </span><span class="kw">readRDS</span>(outcomes[i])
outcome =<span class="st"> </span><span class="kw">all.vars</span>(fit$formula$formula)[<span class="dv">1</span>]
label =<span class="st"> </span>items %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">filter</span>(name ==<span class="st"> </span>outcome) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(label, <span class="kw">starts_with</span>(<span class="st">"choice"</span>))
label_eng =<span class="st"> </span>items_engl %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">filter</span>(Item.name ==<span class="st"> </span>outcome) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(Item) %>%<span class="st"> </span>.[[<span class="dv">1</span>]]
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">##"</span>, outcome, <span class="st">"</span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">cat</span>(<span class="kw">paste0</span>(<span class="st">"### Item text: </span><span class="ch">\n\n</span><span class="st">__"</span>, label$label, <span class="st">"__</span><span class="ch">\n\n\n</span><span class="st">"</span>))
<span class="kw">cat</span>(<span class="kw">paste0</span>(<span class="st">"### Item translation: </span><span class="ch">\n\n</span><span class="st">__"</span>, label_eng, <span class="st">"__</span><span class="ch">\n\n\n</span><span class="st">"</span>))
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">### Choices: {.accordion} </span><span class="ch">\n\n\n</span><span class="st">"</span>)
freqs =<span class="st"> </span>diary %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(<span class="kw">one_of</span>(outcome)) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">table</span>(<span class="dt">exclude =</span> <span class="ot">NULL</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">as.data.frame</span>()
<span class="kw">names</span>(freqs) =<span class="st"> </span><span class="kw">c</span>(<span class="st">"choice"</span>, <span class="st">"frequency"</span>)
freqs$choice =<span class="st"> </span><span class="kw">as.character</span>(freqs$choice)
label %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(-label) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">gather</span>(choice, value) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">choice =</span> stringr::<span class="kw">str_sub</span>(choice, <span class="dv">7</span>)) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">right_join</span>(freqs, <span class="dt">by =</span> <span class="st">"choice"</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(choice)) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">percent =</span> <span class="kw">round</span>(frequency/<span class="kw">sum</span>(frequency),<span class="dv">2</span>)) %>%
<span class="st"> </span><span class="kw">pander</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">cat</span>()
<span class="kw">print</span>(
<span class="kw">ggplot</span>(diary, <span class="kw">aes_string</span>(<span class="dt">x =</span> <span class="kw">paste0</span>(<span class="st">"factor("</span>,outcome,<span class="st">")"</span>))) +
<span class="st"> </span><span class="kw">scale_x_discrete</span>(outcome) +<span class="st"> </span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">na.rm =</span> T)
)
coefs =<span class="st"> </span><span class="kw">fixef</span>(fit, <span class="dt">estimate =</span> <span class="kw">c</span>(<span class="st">"mean"</span>, <span class="st">"sd"</span>, <span class="st">"quantile"</span>), <span class="dt">probs =</span> <span class="kw">c</span>(<span class="fl">0.005</span>, <span class="fl">0.025</span>, <span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.8</span>, <span class="fl">0.9</span>, <span class="fl">0.975</span>, <span class="fl">0.995</span>), <span class="dt">old =</span> T)
coefs =<span class="st"> </span>coefs %>%<span class="st"> </span><span class="kw">data.frame</span>() %>%<span class="st"> </span>tibble::<span class="kw">rownames_to_column</span>(<span class="st">"term"</span>) %>%<span class="st"> </span><span class="kw">filter</span>(term %in%<span class="st"> </span><span class="kw">c</span>(<span class="st">"fertile"</span>, <span class="st">"includedhorm_contra:fertile"</span>))
if (<span class="kw">nrow</span>(coefs) ><span class="st"> </span><span class="dv">0</span>) {
coefs$outcome =<span class="st"> </span>outcome
if (<span class="kw">nrow</span>(label) ) {
coefs$label =<span class="st"> </span>label$label
}
fertile_eff_by_item[[i]] =<span class="st"> </span>coefs
}
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">### Model {.tab-content} </span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">#### Model summary </span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">print_summary</span>(fit)
<span class="kw">tryCatch</span>({
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">#### Coefficient plot</span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">print</span>(<span class="kw">stanplot</span>(fit, <span class="dt">pars =</span> <span class="st">"^b_[^I]"</span>) +<span class="st"> </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="dv">0</span>, <span class="dt">linetype =</span> <span class="st">'dashed'</span>))
}, <span class="dt">error =</span> function(e) <span class="kw">warning</span>(e))
<span class="kw">tryCatch</span>({
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">#### Marginal effect plots</span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">marginal_effects_pass</span>(fit)
}, <span class="dt">error =</span> function(e) <span class="kw">warning</span>(e))
<span class="kw">tryCatch</span>({
<span class="kw">cat</span>(<span class="st">"</span><span class="ch">\n\n\n</span><span class="st">#### Diagnostics</span><span class="ch">\n\n\n</span><span class="st">"</span>)
<span class="kw">print</span>(<span class="kw">pp_check</span>(fit))
}, <span class="dt">error =</span> function(e) <span class="kw">warning</span>(e))
}, <span class="dt">error =</span> function(e){<span class="kw">cat_message</span>(e, <span class="st">"danger"</span>)})
}</code></pre></div>
</div>
<div id="attention_1" class="section level2">
<h2>attention_1</h2>
<div id="item-text" class="section level3">
<h3>Item text:</h3>
<p><strong>… habe ich meinem Partner gezeigt, dass er mir wichtig ist.</strong></p>
</div>
<div id="item-translation" class="section level3">
<h3>Item translation:</h3>
<p><strong>41. I showed my partner that he is important to me.</strong></p>
</div>
<div id="choices" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">2950</td>
<td align="center">0.1</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">1413</td>
<td align="center">0.05</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">3069</td>
<td align="center">0.1</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">7681</td>
<td align="center">0.26</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">7269</td>
<td align="center">0.24</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">7492</td>
<td align="center">0.25</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-1.png" width="768" /></p>
</div>
<div id="model" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: attention_1 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26544)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 74193.79; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.69 0.05 1.59 1.79 563 1.01
sd(fertile) 1.53 0.12 1.29 1.76 564 1.00
sd(menstruationpre) 0.71 0.06 0.59 0.83 614 1.00
sd(menstruationyes) 0.73 0.06 0.61 0.85 618 1.00
cor(Intercept,fertile) -0.24 0.07 -0.36 -0.10 1476 1.00
cor(Intercept,menstruationpre) -0.12 0.08 -0.26 0.04 1357 1.00
cor(fertile,menstruationpre) 0.34 0.10 0.13 0.52 443 1.00
cor(Intercept,menstruationyes) -0.17 0.07 -0.31 -0.03 1441 1.00
cor(fertile,menstruationyes) 0.39 0.09 0.19 0.56 550 1.00
cor(menstruationpre,menstruationyes) 0.54 0.09 0.35 0.70 518 1.01
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept[1] -2.93 0.13 -3.19 -2.67 624 1.01
Intercept[2] -2.35 0.13 -2.60 -2.10 622 1.01
Intercept[3] -1.46 0.13 -1.71 -1.21 621 1.01
Intercept[4] 0.15 0.13 -0.10 0.40 620 1.01
Intercept[5] 1.71 0.13 1.46 1.96 622 1.01
includedhorm_contra 0.28 0.12 0.04 0.51 420 1.01
menstruationpre 0.05 0.07 -0.09 0.19 1224 1.00
menstruationyes 0.05 0.07 -0.08 0.19 1190 1.00
fertile 0.17 0.15 -0.12 0.46 1293 1.00
fertile_mean -0.16 0.55 -1.24 0.90 1076 1.00
includedhorm_contra:menstruationpre -0.10 0.09 -0.28 0.08 1195 1.00
includedhorm_contra:menstruationyes -0.02 0.09 -0.20 0.16 1164 1.00
includedhorm_contra:fertile -0.40 0.19 -0.78 -0.03 1184 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="coefficient-plot" class="section level4">
<h4>Coefficient plot</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-2.png" width="768" /></p>
</div>
<div id="marginal-effect-plots" class="section level4">
<h4>Marginal effect plots</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-3.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-4.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-5.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-6.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-7.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-8.png" width="768" /></p>
</div>
<div id="diagnostics" class="section level4">
<h4>Diagnostics</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-9.png" width="768" /></p>
</div>
</div>
</div>
<div id="attention_2" class="section level2">
<h2>attention_2</h2>
<div id="item-text-1" class="section level3">
<h3>Item text:</h3>
<p><strong>… habe ich meinem Partner gezeigt, dass ich mich von ihm sexuell angezogen fühle.</strong></p>
</div>
<div id="item-translation-1" class="section level3">
<h3>Item translation:</h3>
<p><strong>42. I showed my partner that I was sexually attracted to him.</strong></p>
</div>
<div id="choices-1" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">7785</td>
<td align="center">0.26</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">3194</td>
<td align="center">0.11</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">4982</td>
<td align="center">0.17</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">5502</td>
<td align="center">0.18</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">4166</td>
<td align="center">0.14</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">4245</td>
<td align="center">0.14</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-10.png" width="768" /></p>
</div>
<div id="model-1" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary-1" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: attention_2 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26544)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 83451.19; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.47 0.05 1.38 1.56 1188 1.00
sd(fertile) 1.66 0.11 1.44 1.89 774 1.00
sd(menstruationpre) 0.63 0.06 0.51 0.76 645 1.01
sd(menstruationyes) 0.65 0.06 0.54 0.77 731 1.01
cor(Intercept,fertile) -0.27 0.06 -0.40 -0.14 1911 1.00
cor(Intercept,menstruationpre) -0.21 0.08 -0.37 -0.05 2237 1.00
cor(fertile,menstruationpre) 0.37 0.10 0.17 0.55 718 1.01
cor(Intercept,menstruationyes) -0.22 0.08 -0.37 -0.07 2266 1.00
cor(fertile,menstruationyes) 0.25 0.10 0.06 0.43 559 1.01
cor(menstruationpre,menstruationyes) 0.31 0.12 0.06 0.52 480 1.01
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept[1] -1.30 0.12 -1.53 -1.06 773 1.00
Intercept[2] -0.62 0.12 -0.85 -0.39 769 1.00
Intercept[3] 0.29 0.12 0.07 0.53 773 1.00
Intercept[4] 1.33 0.12 1.11 1.58 780 1.00
Intercept[5] 2.41 0.12 2.18 2.66 788 1.00
includedhorm_contra 0.25 0.11 0.03 0.46 604 1.01
menstruationpre -0.07 0.07 -0.21 0.06 2105 1.00
menstruationyes -0.27 0.07 -0.40 -0.14 1932 1.00
fertile 0.32 0.15 0.03 0.60 1667 1.00
fertile_mean -0.16 0.50 -1.18 0.84 1211 1.00
includedhorm_contra:menstruationpre 0.01 0.09 -0.15 0.17 2172 1.00
includedhorm_contra:menstruationyes 0.05 0.09 -0.12 0.22 2014 1.00
includedhorm_contra:fertile -0.62 0.19 -0.98 -0.27 1486 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="coefficient-plot-1" class="section level4">
<h4>Coefficient plot</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-11.png" width="768" /></p>
</div>
<div id="marginal-effect-plots-1" class="section level4">
<h4>Marginal effect plots</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-12.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-13.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-14.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-15.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-16.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-17.png" width="768" /></p>
</div>
<div id="diagnostics-1" class="section level4">
<h4>Diagnostics</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-18.png" width="768" /></p>
</div>
</div>
</div>
<div id="choice_of_clothing_1" class="section level2">
<h2>choice_of_clothing_1</h2>
<div id="item-text-2" class="section level3">
<h3>Item text:</h3>
<p><strong>Seriös</strong></p>
</div>
<div id="item-translation-2" class="section level3">
<h3>Item translation:</h3>
<p><strong>16. respectable</strong></p>
</div>
<div id="choices-2" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">6472</td>
<td align="center">0.22</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">3381</td>
<td align="center">0.11</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">5966</td>
<td align="center">0.2</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">7768</td>
<td align="center">0.26</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">4545</td>
<td align="center">0.15</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">1764</td>
<td align="center">0.06</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-19.png" width="768" /></p>
</div>
<div id="model-2" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary-2" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: choice_of_clothing_1 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26562)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 77486.23; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.67 0.05 1.57 1.77 362 1.00
sd(fertile) 1.10 0.12 0.86 1.35 278 1.01
sd(menstruationpre) 0.20 0.13 0.01 0.46 86 1.04
sd(menstruationyes) 0.51 0.07 0.37 0.63 341 1.00
cor(Intercept,fertile) -0.22 0.08 -0.38 -0.04 710 1.00
cor(Intercept,menstruationpre) -0.13 0.29 -0.72 0.58 1408 1.00
cor(fertile,menstruationpre) 0.10 0.36 -0.70 0.74 370 1.01
cor(Intercept,menstruationyes) -0.01 0.10 -0.19 0.19 1589 1.00
cor(fertile,menstruationyes) 0.20 0.16 -0.12 0.48 309 1.01
cor(menstruationpre,menstruationyes) 0.21 0.36 -0.68 0.81 62 1.06
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept[1] -1.87 0.13 -2.14 -1.62 516 1.01
Intercept[2] -1.04 0.13 -1.31 -0.79 513 1.01
Intercept[3] 0.13 0.13 -0.13 0.38 515 1.01
Intercept[4] 1.78 0.13 1.52 2.03 515 1.01
Intercept[5] 3.66 0.13 3.39 3.92 533 1.01
includedhorm_contra -0.02 0.12 -0.26 0.23 328 1.01
menstruationpre -0.16 0.06 -0.27 -0.05 1764 1.00
menstruationyes -0.14 0.06 -0.27 -0.02 1604 1.00
fertile -0.13 0.13 -0.38 0.14 1381 1.00
fertile_mean -0.56 0.56 -1.65 0.48 946 1.00
includedhorm_contra:menstruationpre 0.08 0.07 -0.07 0.23 1167 1.00
includedhorm_contra:menstruationyes 0.11 0.08 -0.05 0.27 809 1.00
includedhorm_contra:fertile 0.07 0.17 -0.26 0.41 1140 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="coefficient-plot-2" class="section level4">
<h4>Coefficient plot</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-20.png" width="768" /></p>
</div>
<div id="marginal-effect-plots-2" class="section level4">
<h4>Marginal effect plots</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-21.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-22.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-23.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-24.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-25.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-26.png" width="768" /></p>
</div>
<div id="diagnostics-2" class="section level4">
<h4>Diagnostics</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-27.png" width="768" /></p>
</div>
</div>
</div>
<div id="choice_of_clothing_2" class="section level2">
<h2>choice_of_clothing_2</h2>
<div id="item-text-3" class="section level3">
<h3>Item text:</h3>
<p><strong>Praktisch</strong></p>
</div>
<div id="item-translation-3" class="section level3">
<h3>Item translation:</h3>
<p><strong>17. practical</strong></p>
</div>
<div id="choices-3" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">628</td>
<td align="center">0.02</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">1046</td>
<td align="center">0.03</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">3185</td>
<td align="center">0.11</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">9221</td>
<td align="center">0.31</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">8696</td>
<td align="center">0.29</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">7116</td>
<td align="center">0.24</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-28.png" width="768" /></p>
</div>
<div id="model-3" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary-3" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: choice_of_clothing_2 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26558)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 72212.25; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.23 0.04 1.16 1.31 1105 1.00
sd(fertile) 1.14 0.14 0.85 1.40 390 1.01
sd(menstruationpre) 0.45 0.08 0.27 0.60 295 1.01
sd(menstruationyes) 0.28 0.11 0.03 0.46 217 1.01
cor(Intercept,fertile) -0.28 0.08 -0.43 -0.12 4000 1.00
cor(Intercept,menstruationpre) -0.17 0.10 -0.35 0.05 4000 1.00
cor(fertile,menstruationpre) 0.17 0.21 -0.30 0.48 236 1.01
cor(Intercept,menstruationyes) 0.02 0.21 -0.32 0.55 955 1.00
cor(fertile,menstruationyes) 0.36 0.28 -0.40 0.79 547 1.01
cor(menstruationpre,menstruationyes) 0.51 0.30 -0.30 0.90 391 1.01
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept[1] -4.61 0.12 -4.85 -4.39 1749 1
Intercept[2] -3.46 0.11 -3.67 -3.24 1589 1
Intercept[3] -2.09 0.11 -2.30 -1.88 1570 1
Intercept[4] -0.22 0.11 -0.43 -0.01 1577 1
Intercept[5] 1.41 0.11 1.20 1.62 1583 1
includedhorm_contra 0.07 0.10 -0.12 0.26 1100 1
menstruationpre 0.13 0.06 0.01 0.25 4000 1
menstruationyes 0.14 0.06 0.03 0.25 4000 1
fertile 0.16 0.13 -0.09 0.41 4000 1
fertile_mean -0.48 0.47 -1.39 0.44 2384 1
includedhorm_contra:menstruationpre -0.19 0.08 -0.35 -0.03 4000 1
includedhorm_contra:menstruationyes -0.19 0.07 -0.33 -0.04 4000 1
includedhorm_contra:fertile -0.10 0.16 -0.42 0.21 3177 1
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="coefficient-plot-3" class="section level4">
<h4>Coefficient plot</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-29.png" width="768" /></p>
</div>
<div id="marginal-effect-plots-3" class="section level4">
<h4>Marginal effect plots</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-30.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-31.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-32.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-33.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-34.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-35.png" width="768" /></p>
</div>
<div id="diagnostics-3" class="section level4">
<h4>Diagnostics</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-36.png" width="768" /></p>
</div>
</div>
</div>
<div id="choice_of_clothing_3" class="section level2">
<h2>choice_of_clothing_3</h2>
<div id="item-text-4" class="section level3">
<h3>Item text:</h3>
<p><strong>Sportlich</strong></p>
</div>
<div id="item-translation-4" class="section level3">
<h3>Item translation:</h3>
<p><strong>18. athletic</strong></p>
</div>
<div id="choices-4" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">2782</td>
<td align="center">0.09</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">2963</td>
<td align="center">0.1</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">5880</td>
<td align="center">0.2</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">8896</td>
<td align="center">0.3</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">6298</td>
<td align="center">0.21</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">3068</td>
<td align="center">0.1</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-37.png" width="768" /></p>
</div>
<div id="model-4" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary-4" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: choice_of_clothing_3 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26554)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 79384.6; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.49 0.04 1.41 1.58 885 1.00
sd(fertile) 0.97 0.15 0.67 1.24 379 1.01
sd(menstruationpre) 0.46 0.07 0.32 0.58 501 1.00
sd(menstruationyes) 0.54 0.06 0.40 0.65 433 1.00
cor(Intercept,fertile) -0.22 0.09 -0.39 -0.03 4000 1.00
cor(Intercept,menstruationpre) -0.36 0.09 -0.52 -0.18 4000 1.00
cor(fertile,menstruationpre) 0.24 0.19 -0.16 0.55 326 1.00
cor(Intercept,menstruationyes) -0.20 0.08 -0.36 -0.04 2353 1.00
cor(fertile,menstruationyes) 0.31 0.16 -0.04 0.59 293 1.00
cor(menstruationpre,menstruationyes) 0.90 0.07 0.73 0.98 504 1.01
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept[1] -2.98 0.12 -3.21 -2.74 1008 1
Intercept[2] -1.86 0.12 -2.10 -1.63 997 1
Intercept[3] -0.55 0.12 -0.78 -0.31 994 1
Intercept[4] 1.09 0.12 0.86 1.33 993 1
Intercept[5] 2.83 0.12 2.59 3.07 990 1
includedhorm_contra 0.18 0.11 -0.03 0.39 548 1
menstruationpre 0.10 0.06 -0.02 0.22 2095 1
menstruationyes 0.02 0.06 -0.10 0.14 2580 1
fertile 0.06 0.13 -0.19 0.30 2697 1
fertile_mean -0.40 0.52 -1.44 0.63 913 1
includedhorm_contra:menstruationpre -0.07 0.08 -0.22 0.08 2266 1
includedhorm_contra:menstruationyes -0.06 0.08 -0.22 0.10 2890 1
includedhorm_contra:fertile -0.04 0.16 -0.34 0.27 2589 1
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="coefficient-plot-4" class="section level4">
<h4>Coefficient plot</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-38.png" width="768" /></p>
</div>
<div id="marginal-effect-plots-4" class="section level4">
<h4>Marginal effect plots</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-39.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-40.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-41.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-42.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-43.png" width="768" /><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-44.png" width="768" /></p>
</div>
<div id="diagnostics-4" class="section level4">
<h4>Diagnostics</h4>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-45.png" width="768" /></p>
</div>
</div>
</div>
<div id="choice_of_clothing_4" class="section level2">
<h2>choice_of_clothing_4</h2>
<div id="item-text-5" class="section level3">
<h3>Item text:</h3>
<p><strong>Sexy</strong></p>
</div>
<div id="item-translation-5" class="section level3">
<h3>Item translation:</h3>
<p><strong>19. sexy</strong></p>
</div>
<div id="choices-5" class="section level3 accordion">
<h3>Choices:</h3>
<table style="width:81%;">
<colgroup>
<col width="12%" />
<col width="38%" />
<col width="16%" />
<col width="12%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">choice</th>
<th align="center">value</th>
<th align="center">frequency</th>
<th align="center">percent</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">Stimme nicht zu</td>
<td align="center">5639</td>
<td align="center">0.19</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">Stimme überwiegend nicht zu</td>
<td align="center">4092</td>
<td align="center">0.14</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">Stimme eher nicht zu</td>
<td align="center">7002</td>
<td align="center">0.23</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">Stimme eher zu</td>
<td align="center">8183</td>
<td align="center">0.27</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">Stimme überwiegend zu</td>
<td align="center">3918</td>
<td align="center">0.13</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">Stimme voll zu</td>
<td align="center">1050</td>
<td align="center">0.04</td>
</tr>
</tbody>
</table>
<p><img src="4_stan_brms_by_item_files/figure-html/unnamed-chunk-3-46.png" width="768" /></p>
</div>
<div id="model-5" class="section level3 tab-content">
<h3>Model</h3>
<div id="model-summary-5" class="section level4">
<h4>Model summary</h4>
<pre><code> Family: cumulative(logit)
Formula: choice_of_clothing_4 ~ included * (menstruation + fertile) + fertile_mean + (1 + fertile + menstruation | person)
disc = 1
Data: diary (Number of observations: 26551)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
ICs: LOO = 77225.54; WAIC = Not computed
Group-Level Effects:
~person (Number of levels: 1043)
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sd(Intercept) 1.48 0.04 1.40 1.57 1019 1.00
sd(fertile) 1.32 0.13 1.07 1.56 469 1.01
sd(menstruationpre) 0.52 0.07 0.37 0.65 444 1.00
sd(menstruationyes) 0.60 0.06 0.47 0.72 755 1.00
cor(Intercept,fertile) -0.12 0.08 -0.26 0.04 1413 1.00
cor(Intercept,menstruationpre) -0.04 0.09 -0.21 0.15 1148 1.00
cor(fertile,menstruationpre) 0.48 0.13 0.19 0.70 490 1.00
cor(Intercept,menstruationyes) -0.09 0.08 -0.25 0.07 1408 1.00
cor(fertile,menstruationyes) 0.37 0.12 0.10 0.58 457 1.01