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<a class="navbar-brand" href="index.html">Fertility diary</a>
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<a href="1_power_analysis.html">Power analysis</a>
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<div id="item-level-model-for-extra-pair-items" class="section level1 tab-content">
<h1>Item-level model for extra-pair items</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>
<pre class="r"><code>library(knitr)
opts_chunk$set(fig.width = 12, fig.height = 12, cache = F, warning = F, message = F)</code></pre>
<pre class="r"><code>library(formr); library(data.table); library(stringr); library(ggplot2); library(plyr); library(dplyr);library(car); library(psych);
source("0_helpers.R")
library(brms)
load("full_data.rdata")
diary$included = diary$included_all
diary_long = diary %>% group_by(person) %>%
filter(!is.na(included_all), !is.na(fertile_fab)) %>%
mutate(included = included_all,
fertile = fertile_fab,
fertile_mean = mean(fertile_fab, na.rm = T)) %>%
select(person, menstruation, RCD_inferred, fertile_mean, fertile, fertile_fab, included, extra_pair_2, extra_pair_3, extra_pair_4, extra_pair_5, extra_pair_6, extra_pair_7, extra_pair_8, extra_pair_9, extra_pair_10, extra_pair_11, extra_pair_12, extra_pair_13) %>%
tidyr::gather(variable, value, -person, -menstruation, -fertile_mean,-fertile, -fertile_fab, -included, -RCD_inferred)
items = readxl::read_xlsx("item_tables/Daily_items_bearbeitetAM.xlsx", 1) %>% rename(`Item.name` = `Item name`)
diary_long = diary_long %>% left_join(items %>% select(Item.name, Item), by = c("variable" = "Item.name"))</code></pre>
<p>Dan Engber helpfully pointed out that he inferred from the plot caption in the manuscript, that the curves reflected shapes standardised across hormonal contraceptive users and non-users. In fact, each curve shown was standardised within group, because we wanted to draw the eye to the differences in <em>changes</em>, not in mean levels. This leads to an inconsistency with the other graph by group, where mean levels play a role too. Unfortunately, we did not explain this inconsistency in the text. Here, I show the same graph, but standardised only by item and not within the groups of HC users and non-users. We still see what looks like “post-menstrual catch-up” in in-pair sexual desire for hormonal contraceptive users. Importantly, the pattern we see for non-hormonal contraceptive users may also include some such post-menstrual catch-up (although it’s less visible against the background of ovulatory change). So as not to confuse such non-ovulatory cyclical changes (which may be behaviorally driven rather than hormonally) with those related to ovulation, we used the hormonal contraceptive users as quasi-control group.</p>
</div>
<div id="item-level-raw-means-over-cycle-days" class="section level2">
<h2>Item-level raw means over cycle days</h2>
<pre class="r"><code>theme_henrik <- function(grid=TRUE, legend.position=NA) {
th <- ggplot2::theme_minimal(base_size = 12)
th <- th + theme(text = element_text(color='#333333'))
th <- th + theme(legend.background = element_blank())
th <- th + theme(legend.key = element_blank())
# Straight out of hrbrthemes
if (inherits(grid, "character") | grid == TRUE) {
th <- th + theme(panel.grid=element_line(color="#cccccc", size=0.3))
th <- th + theme(panel.grid.major=element_line(color="#cccccc", size=0.3))
th <- th + theme(panel.grid.minor=element_line(color="#cccccc", size=0.15))
if (inherits(grid, "character")) {
if (regexpr("X", grid)[1] < 0) th <- th + theme(panel.grid.major.x=element_blank())
if (regexpr("Y", grid)[1] < 0) th <- th + theme(panel.grid.major.y=element_blank())
if (regexpr("x", grid)[1] < 0) th <- th + theme(panel.grid.minor.x=element_blank())
if (regexpr("y", grid)[1] < 0) th <- th + theme(panel.grid.minor.y=element_blank())
}
} else {
th <- th + theme(panel.grid=element_blank())
}
th <- th + theme(axis.text = element_text())
th <- th + theme(axis.ticks = element_blank())
th <- th + theme(axis.text.x=element_text(margin=margin(t=0.5)))
th <- th + theme(axis.text.y=element_text(margin=margin(r=0.5)))
th <- th + theme(plot.title = element_text(),
plot.subtitle = element_text(margin=margin(b=15)),
plot.caption = element_text(face='italic', size=10))
if (!is.na(legend.position)) th <- th + theme(legend.position = legend.position)
return (th)
}
diary %>% group_by(person) %>%
filter(!is.na(fertile_fab), !is.na(included_all),
RCD > -1 * minimum_cycle_length_diary, RCD > -29) %>%
filter(!is.na(included_all), !is.na(fertile_fab)) %>%
mutate(included = included_all,
fertile_mean = mean(fertile_fab, na.rm = T)) %>%
select(person, n_days, menstruation, RCD_inferred, fertile_mean, fertile_fab, included, extra_pair_2, extra_pair_3, extra_pair_4, extra_pair_5, extra_pair_6, extra_pair_7, extra_pair_8, extra_pair_9, extra_pair_10, extra_pair_11, extra_pair_12, extra_pair_13, desirability_partner, desirability_1, sexual_intercourse_1, attention_2
# ,extra_pair, in_pair_desire
) %>%
tidyr::gather(variable, value, -person, -menstruation, -fertile_mean, -fertile_fab, -included, -RCD_inferred, -n_days) %>%
left_join(items %>% select(Item.name, Item), by = c("variable" = "Item.name")) %>%
mutate(RCD_inferred = -1* RCD_inferred + 1, variable = factor(str_wrap(str_sub(Item,4),40))) %>%
filter(!is.na(value), RCD_inferred > -29) %>%
group_by(included, person, variable) %>%
mutate(value = value - min(value),
value = value / max(value)) %>%
group_by(included, Item, variable, RCD_inferred) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(included, variable) %>%
arrange(RCD_inferred) %>%
mutate(
value_min = value - min(value),
p_peak = value_min / max(value_min), # Normalize as percentage of peak popularity
p_smooth = (
coalesce(lag(p_peak), p_peak[RCD_inferred==0]) + p_peak + coalesce(lead(p_peak), p_peak[RCD_inferred==-28])) / 3 # Moving average, accounting for cyclical nature
) %>% # When there's no lag or lead, we get NA. Use the pointwise data
group_by(included) %>%
mutate(variable = reorder(variable, p_smooth, FUN=which.max)) %>% # order by peak time
arrange(variable) %>%
mutate(variable.f = reorder(as.character(variable), desc(variable))) ->
item_timeseries</code></pre>
<pre class="r"><code>ts_plot = item_timeseries %>%
filter(!is.na(included)) %>%
{
items <- levels(.$variable)
ggplot(., aes(x = RCD_inferred, group = variable.f, fill = factor(paste0(included,as.integer(variable.f) %% 2)))) +
geom_ribbon(aes(ymin = as.integer(variable), ymax = as.integer(variable) + 2 * (p_smooth)), color='white', size=0.4) +
scale_x_continuous("Days until next menstruation", breaks = c(-0,-7,-14,-21,-28), labels = c("0","7","14","21","28")) +
# "Re-add" activities by names as labels in the Y scale
scale_y_continuous(breaks = 1:length(items) + 0.4, labels = function(y) {items[y]})+
# Zebra color for readability; will change colors of labels in Inkscape later
scale_fill_manual(values = c('horm_contra0' = '#444444', 'horm_contra1' = '#222222', 'cycling0' = '#F13B0E', 'cycling1' = '#B7320E'))+
ggtitle("") +
theme_henrik(grid='', legend.position='none') +
theme(
axis.ticks.x = element_line(size=0.3),
axis.ticks.y = element_line(size=0.3),
axis.text.y = element_text(size = 10))+
facet_wrap(~ included, labeller = labeller(included = c(`cycling` = 'Naturally cycling', `horm_contra` = 'Hormonal contraceptive user'))) +
geom_segment(aes(x = if_else(included=="cycling",-14,NA_real_), xend = if_else(included=="cycling",-14,NA_real_)), color = 'darkred', linetype = 'dashed', y = 0.7, yend = 17.7)+
geom_text(data = data.frame(included = 'cycling', variable.f = 1, RCD_inferred = -14), label = 'est. day of ovulation', color = 'darkred', y = 0.5)
}
ggsave(ts_plot, file = "item_timeseries.pdf", width = 10, height = 10)
ts_plot</code></pre>
<p><img src="3_stan_brms_long2_files/figure-html/unnamed-chunk-2-1.png" width="1152" /></p>
</div>
<div id="item-level-raw-means-over-cycle-days-modified" class="section level2">
<h2>Item-level raw means over cycle days (modified)</h2>
<pre class="r"><code>diary %>% group_by(person) %>%
filter(!is.na(fertile_fab), !is.na(included_all),
RCD > -1 * minimum_cycle_length_diary, RCD > -29) %>%
filter(!is.na(included_all), !is.na(fertile_fab)) %>%
mutate(included = included_all,
fertile_mean = mean(fertile_fab, na.rm = T)) %>%
select(person, n_days, menstruation, RCD_inferred, fertile_mean, fertile_fab, included, extra_pair_2, extra_pair_3, extra_pair_4, extra_pair_5, extra_pair_6, extra_pair_7, extra_pair_8, extra_pair_9, extra_pair_10, extra_pair_11, extra_pair_12, extra_pair_13, desirability_partner, desirability_1, sexual_intercourse_1, attention_2
# ,extra_pair, in_pair_desire
) %>%
tidyr::gather(variable, value, -person, -menstruation, -fertile_mean, -fertile_fab, -included, -RCD_inferred, -n_days) %>%
left_join(items %>% select(Item.name, Item), by = c("variable" = "Item.name")) %>%
mutate(RCD_inferred = -1* RCD_inferred + 1, variable = factor(str_wrap(str_sub(Item,4),40))) %>%
filter(!is.na(value), RCD_inferred > -29) %>%
group_by(included, person, variable) %>%
mutate(value = value - min(value),
value = value / max(value)) %>%
group_by(included, Item, variable, RCD_inferred) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(variable) %>%
arrange(RCD_inferred) %>%
mutate(
value_min = value - min(value),
p_peak = value_min / max(value_min)) %>%
group_by(included, variable) %>% # Normalize as percentage of peak popularity
mutate(
p_smooth = (
coalesce(lag(p_peak), p_peak[RCD_inferred==0]) + p_peak + coalesce(lead(p_peak), p_peak[RCD_inferred==-28])) / 3 # Moving average, accounting for cyclical nature
) %>% # When there's no lag or lead, we get NA. Use the pointwise data
group_by(included) %>%
mutate(variable = reorder(variable, p_smooth, FUN=which.max)) %>% # order by peak time
arrange(variable) %>%
mutate(variable.f = reorder(as.character(variable), desc(variable))) ->
item_timeseries</code></pre>
<pre class="r"><code>ts_plot = item_timeseries %>%
filter(!is.na(included)) %>%
{
items <- levels(.$variable)
ggplot(., aes(x = RCD_inferred, group = variable.f, fill = factor(paste0(included,as.integer(variable.f) %% 2)))) +
geom_ribbon(aes(ymin = as.integer(variable), ymax = as.integer(variable) + 2 * (p_smooth)), color='white', size=0.4) +
scale_x_continuous("Days until next menstruation", breaks = c(-0,-7,-14,-21,-28), labels = c("0","7","14","21","28")) +
# "Re-add" activities by names as labels in the Y scale
scale_y_continuous(breaks = 1:length(items) + 0.4, labels = function(y) {items[y]})+
# Zebra color for readability; will change colors of labels in Inkscape later
scale_fill_manual(values = c('horm_contra0' = '#444444', 'horm_contra1' = '#222222', 'cycling0' = '#F13B0E', 'cycling1' = '#B7320E'))+
ggtitle("") +
theme_henrik(grid='', legend.position='none') +
theme(
axis.ticks.x = element_line(size=0.3),
axis.ticks.y = element_line(size=0.3),
axis.text.y = element_text(size = 10))+
facet_wrap(~ included, labeller = labeller(included = c(`cycling` = 'Naturally cycling', `horm_contra` = 'Hormonal contraceptive user'))) +
geom_segment(aes(x = if_else(included=="cycling",-14,NA_real_), xend = if_else(included=="cycling",-14,NA_real_)), color = 'darkred', linetype = 'dashed', y = 0.7, yend = 17.7)+
geom_text(data = data.frame(included = 'cycling', variable.f = 1, RCD_inferred = -14), label = 'est. day of ovulation', color = 'darkred', y = 0.5)
}
ggsave(ts_plot, file = "item_timeseries2.pdf", width = 10, height = 10)
ts_plot</code></pre>
<p><img src="3_stan_brms_long2_files/figure-html/unnamed-chunk-4-1.png" width="1152" /></p>
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