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<a class="navbar-brand" href="index.html">Fertility diary</a>
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<li>
<a href="1_power_analysis.html">Power analysis</a>
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<div id="model-summary" class="section level3 tab-content">
<h3>Model summary</h3>
<div id="model-summary-1" class="section level4">
<h4>Model summary</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">print_summary</span>()</code></pre></div>
<p>Effect size standardised by residual variance (<span class="math inline">\(b * 0.57 * 3/2 * SD_{residual}\)</span>): 0.26.</p>
</div>
<div id="marginal-effect-plots" class="section level4">
<h4>Marginal effect plots</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_all_effects</span>()</code></pre></div>
</div>
<div id="outcome-distribution" class="section level4">
<h4>Outcome distribution</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_outcome</span>(diary) +<span class="st"> </span><span class="kw">xlab</span>(outcome_label)</code></pre></div>
</div>
<div id="diagnostics" class="section level4">
<h4>Diagnostics</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">print_diagnostics</span>()</code></pre></div>
</div>
</div>
<div id="curves" class="section level3 tab-content active">
<h3>Curves</h3>
<p>Here, we continuously plot the outcome over the course of the cycle. Because cycle lengths vary, we subset the data to cycles in a certain range. If the red curve has the same shape as the pink curve, our predictor accurately maps the relationship between conception risk and the outcome.</p>
<div id="cycle-lengths-from-21-to-36" class="section level4 tab-content">
<h4>Cycle lengths from 21 to 36</h4>
<div id="backward-counted" class="section level5 tab-content active">
<h5>Backward-counted</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_curve</span>(diary %>%<span class="st"> </span><span class="kw">filter</span>(minimum_cycle_length_diary <=<span class="st"> </span><span class="dv">36</span>, minimum_cycle_length_diary ><span class="st"> </span><span class="dv">20</span>) %>%<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">fertile=</span>prc_stirn_b))</code></pre></div>
<!-- ##### Backward-counted, inferred {.tab-content} -->
<!-- ```{r curve21_36_bci,results='asis'} -->
<!-- model %>% -->
<!-- plot_curve(diary %>% filter(minimum_cycle_length_diary <= 36, minimum_cycle_length_diary > 20) %>% mutate(RCD = (RCD_inferred - 1)*-1, fertile=prc_stirn_b_backward_inferred), caption_x = "Days until next menstruation, inferred where unknown") -->
<!-- ``` -->
</div>
<div id="forward-counted" class="section level5 tab-content">
<h5>Forward-counted</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_curve</span>(diary %>%<span class="st"> </span><span class="kw">filter</span>(minimum_cycle_length_diary <=<span class="st"> </span><span class="dv">36</span>, minimum_cycle_length_diary ><span class="st"> </span><span class="dv">20</span>) %>%<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">RCD =</span> FCD, <span class="dt">fertile =</span> prc_stirn_b_forward_counted), <span class="dt">caption_x =</span> <span class="st">"Days since last menstruation"</span>)</code></pre></div>
</div>
</div>
<div id="cycle-lengths-from-27-to-30" class="section level4 tab-content">
<h4>Cycle lengths from 27 to 30</h4>
<div id="backward-counted-1" class="section level5 tab-content">
<h5>Backward-counted</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_curve</span>(diary %>%<span class="st"> </span><span class="kw">filter</span>(minimum_cycle_length_diary <=<span class="st"> </span><span class="dv">30</span>, minimum_cycle_length_diary >=<span class="st"> </span><span class="dv">27</span>) %>%<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">fertile=</span>prc_stirn_b))</code></pre></div>
<!-- ##### Backward-counted, inferred {.tab-content} -->
<!-- ```{r curve27_30_bci,results='asis'} -->
<!-- model %>% -->
<!-- plot_curve(diary %>% filter(minimum_cycle_length_diary <= 30, minimum_cycle_length_diary >= 27) %>% mutate(RCD = (RCD_inferred - 1)*-1, fertile=prc_stirn_b_backward_inferred), caption_x = "Days until next menstruation, inferred where unknown") -->
<!-- ``` -->
</div>
<div id="forward-counted-1" class="section level5 tab-content">
<h5>Forward-counted</h5>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">plot_curve</span>(diary %>%<span class="st"> </span><span class="kw">filter</span>(minimum_cycle_length_diary <=<span class="st"> </span><span class="dv">30</span>, minimum_cycle_length_diary >=<span class="st"> </span><span class="dv">27</span>) %>%<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">RCD =</span> FCD, <span class="dt">fertile =</span> prc_stirn_b_forward_counted), <span class="dt">caption_x =</span> <span class="st">"Days since last menstruation"</span>)</code></pre></div>
</div>
</div>
</div>
<div id="robustness-checks" class="section level3 tab-content">
<h3>Robustness checks</h3>
<div id="m_r1-random-slopes-for-conception-risk-and-menstruation" class="section level4">
<h4>M_r1: Random slopes for conception risk and menstruation</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">tryCatch</span>({
<span class="co"># refit model with random effects for fertile and menstruation dummies</span>
with_ind_diff =<span class="st"> </span><span class="kw">update</span>(model, <span class="dt">formula =</span> . ~<span class="st"> </span>. -<span class="st"> </span>(<span class="dv">1</span>|<span class="st"> </span>person) +<span class="st"> </span>(<span class="dv">1</span> +<span class="st"> </span>fertile +<span class="st"> </span>menstruation |<span class="st"> </span>person))
<span class="co"># pull the random effects, format as tibble</span>
rand =<span class="st"> </span><span class="kw">coef</span>(with_ind_diff)$person %>%<span class="st"> </span>
<span class="st"> </span>tibble::<span class="kw">rownames_to_column</span>(<span class="st">"person"</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">person =</span> <span class="kw">as.numeric</span>(person))
<span class="co"># pull the fixed effects</span>
fixd =<span class="st"> </span><span class="kw">data.frame</span>(<span class="kw">fixef</span>(with_ind_diff)) %>%<span class="st"> </span>
<span class="st"> </span>tibble::<span class="kw">rownames_to_column</span>(<span class="st">"effect"</span>)
<span class="kw">names</span>(fixd) =<span class="st"> </span><span class="kw">c</span>(<span class="st">"effect"</span>, <span class="st">"pop_effect_size"</span>)
<span class="co"># pull apart the coefficients so that we can account for the fact that the random effect variation implicitly includes HC explaining the mean population-level effect of fertile/menstruation dummies among HC users</span>
fixd =<span class="st"> </span>fixd %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">separate</span>(effect, <span class="kw">c</span>(<span class="st">"included"</span>, <span class="st">"effect"</span>), <span class="dt">sep =</span> <span class="st">":"</span>, <span class="dt">fill =</span> <span class="st">"left"</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">included =</span> <span class="kw">if_else</span>(<span class="kw">is.na</span>(included), <span class="st">"cycling"</span>, <span class="kw">str_replace</span>(included, <span class="st">"included"</span>, <span class="st">""</span>)))
fixd[<span class="dv">2</span>,<span class="kw">c</span>(<span class="st">"included"</span>, <span class="st">"effect"</span>)] =<span class="st"> </span><span class="kw">c</span>(<span class="st">"horm_contra"</span>, <span class="st">"(Intercept)"</span>)
rand =<span class="st"> </span>rand %>%<span class="st"> </span>
<span class="st"> </span><span class="co"># merge diary data on the random effects, so that we know who is a HC users and who isn't</span>
<span class="st"> </span><span class="kw">inner_join</span>(diary %>%<span class="st"> </span><span class="kw">select</span>(person, included) %>%<span class="st"> </span><span class="kw">unique</span>(), <span class="dt">by =</span> <span class="st">'person'</span>) %>%
<span class="st"> </span><span class="co"># gather into long format, to have the dataset by predictor</span>
<span class="st"> </span><span class="kw">gather</span>(effect, value, -person, -included) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">inner_join</span>(fixd, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">'effect'</span>, <span class="st">'included'</span>)) %>%<span class="st"> </span>
<span class="st"> </span><span class="co"># pull the fixed effects</span>
<span class="st"> </span><span class="kw">mutate</span>(
<span class="co"># only for those who are HC users, add the moderated population effect size for this effect (the random effects have the reference category mean)</span>
<span class="dt">value =</span> <span class="kw">if_else</span>(included ==<span class="st"> "horm_contra"</span>, value +<span class="st"> </span>pop_effect_size, value),
<span class="dt">effect =</span> <span class="kw">recode</span>(effect, <span class="st">"includedhorm_contra"</span> =<span class="st"> "HC user"</span>,
<span class="st">"includedhorm_contra:fertile"</span> =<span class="st"> "HC user x fertile"</span>,
<span class="st">"includedhorm_contra:menstruationpre"</span> =<span class="st"> "HC user x premens."</span>,
<span class="st">"includedhorm_contra:menstruationyes"</span> =<span class="st"> "HC user x mens."</span>,
<span class="st">"menstruationyes"</span> =<span class="st"> "mens."</span>,
<span class="st">"menstruationpre"</span> =<span class="st"> "premens."</span>)) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">group_by</span>(included, effect) %>%<span class="st"> </span>
<span class="st"> </span><span class="co"># filter out predictors that aren't modelled as varying/random</span>
<span class="st"> </span><span class="kw">filter</span>(<span class="kw">sd</span>(value) ><span class="st"> </span><span class="dv">0</span>)
<span class="co"># plot dot plot of random effects</span>
<span class="kw">print</span>(
<span class="kw">ggplot</span>(rand, <span class="kw">aes</span>(<span class="dt">x =</span> included, <span class="dt">y =</span> value, <span class="dt">color =</span> included, <span class="dt">fill =</span> included)) +
<span class="st"> </span><span class="kw">facet_wrap</span>( ~<span class="st"> </span>effect, <span class="dt">scales =</span> <span class="st">"free"</span>) +<span class="st"> </span>
<span class="st"> </span><span class="co"># geom_violin(alpha = 0.4, size = 0) + </span>
<span class="st"> </span><span class="kw">geom_dotplot</span>(<span class="dt">binaxis=</span><span class="st">'y'</span>, <span class="dt">dotsize =</span> <span class="fl">0.1</span>, <span class="dt">method =</span> <span class="st">"histodot"</span>) +
<span class="co"># geom_jitter(alpha = 0.05) + </span>
<span class="st"> </span><span class="kw">coord_flip</span>() +<span class="st"> </span>
<span class="st"> </span><span class="kw">geom_pointrange</span>(<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">color =</span> <span class="st">'darkred'</span>, <span class="dt">size =</span> <span class="fl">1.2</span>) +
<span class="st"> </span><span class="kw">scale_color_manual</span>(<span class="st">"Contraception status"</span>, <span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"horm_contra"</span>=<span class="st">"black"</span>,<span class="st">"cycling"</span>=<span class="st"> "red"</span>), <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">"horm_contra"</span>=<span class="st">"hormonally</span><span class="ch">\n</span><span class="st">contracepting"</span>,<span class="st">"cycling"</span>=<span class="st">"cycling"</span>), <span class="dt">guide =</span> F) +
<span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="st">"Contraception status"</span>, <span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"horm_contra"</span>=<span class="st">"black"</span>,<span class="st">"cycling"</span>=<span class="st"> "red"</span>), <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">"horm_contra"</span>=<span class="st">"hormonally</span><span class="ch">\n</span><span class="st">contracepting"</span>,<span class="st">"1"</span>=<span class="st">"cycling"</span>), <span class="dt">guide =</span> F) +<span class="st"> </span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"M_r1: allowing participant-varying slopes"</span>, <span class="dt">subtitle =</span> <span class="st">"for the conception risk measure and the menstruation dummies"</span>) +
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="st">"Hormonal contraception"</span>, <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="st">"horm_contra"</span>, <span class="st">"cycling"</span>), <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">"yes"</span>, <span class="st">"no"</span>)) +
<span class="st"> </span><span class="kw">scale_y_continuous</span>(<span class="st">"Random effect size distribution"</span>))
<span class="kw">print_summary</span>(with_ind_diff)
<span class="kw">cat</span>(<span class="kw">pander</span>(<span class="kw">anova</span>(model, with_ind_diff)))
}, <span class="dt">error =</span> function(e){
with_ind_diff =<span class="st"> </span>model
<span class="kw">cat_message</span>(e, <span class="st">"danger"</span>)
})</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">robustness_check_ovu_shift</span>(model, diary)</code></pre></div>
</div>
<div id="m_m2-moderation-by-participant-age" class="section level4">
<h4><em>M_m2</em>: Moderation by participant age</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"age_group"</span>, diary, <span class="dt">xlevels =</span> <span class="dv">5</span>)</code></pre></div>
</div>
<div id="m_m3-moderation-by-weekend" class="section level4">
<h4><em>M_m3</em>: Moderation by weekend</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"weekend"</span>, diary, <span class="dt">xlevels =</span> <span class="dv">2</span>) </code></pre></div>
</div>
<div id="m_m4-moderation-by-weekday" class="section level4">
<h4><em>M_m4</em>: Moderation by weekday</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"weekday"</span>, diary, <span class="dt">xlevels =</span> <span class="dv">7</span>)</code></pre></div>
</div>
<div id="m_m5-moderation-by-exclusion-threshold" class="section level4">
<h4><em>M_m5</em>: Moderation by exclusion threshold</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"included_levels"</span>, diary, <span class="dt">xlevels =</span> <span class="dv">4</span>)</code></pre></div>
</div>
<div id="m_m6-moderation-by-cycle-length" class="section level4">
<h4><em>M_m6</em>: Moderation by cycle length</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"cycle_length_groups"</span>, diary, <span class="dt">xlevels =</span> <span class="dv">4</span>)</code></pre></div>
</div>
<div id="m_m7-moderation-by-certainty-about-menstruation-parameters" class="section level4">
<h4><em>M_m7</em>: Moderation by certainty about menstruation parameters</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"certainty_menstruation"</span>, diary)</code></pre></div>
</div>
<div id="m_m8-moderation-by-cycle-regularity" class="section level4">
<h4><em>M_m8</em>: Moderation by cycle regularity</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"cycle_regularity"</span>, diary)</code></pre></div>
</div>
<div id="m_m9-moderation-by-cohabitation-status" class="section level4">
<h4><em>M_m9</em>: Moderation by cohabitation status</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"cohabitation"</span>, diary)</code></pre></div>
</div>
<div id="m_m10-moderation-by-relationship-status" class="section level4">
<h4><em>M_m10</em>: Moderation by relationship status</h4>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">test_moderator</span>(<span class="st">"relationship_status_clean"</span>, diary)</code></pre></div>
</div>
</div>
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