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rejoinder.html
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<!DOCTYPE html>
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<h1 class="title toc-ignore">Rejoinder</h1>
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<div id="data-cleaning" class="section level2">
<h2>Data cleaning</h2>
<pre class="r"><code># loading packages
library(knitr)
library(lme4)
library(lmerTest)
library(formr)
library(effects)
library(ggthemes)
library(codebook)
library(forcats)
library(kableExtra)
knit_print.alpha <- function(x, ...) {
print(x)
}
options(width = 8000)
registerS3method("knit_print", "alpha", knit_print.alpha)
library(tidyverse)
chunk_fig_width <- function(default = 7) {
chunk_opt <- opts_current$get("fig.width")
if(is.null(chunk_opt)) {
chunk_opt <- default
}
chunk_opt
}
chunk_fig_height <- function(default = 7) {
chunk_opt <- opts_current$get("fig.height")
if(is.null(chunk_opt)) {
chunk_opt <- default
}
chunk_opt
}</code></pre>
<p>The following data cleaning code just replicates Gangestad and Dinh’s (2020) script with some minor corrections to make it work (see code comments starting <code>#'</code>).</p>
<pre class="r"><code>knitr::opts_chunk$set(cache = TRUE, autodep = TRUE, error = TRUE)</code></pre>
<pre class="r"><code>ci_method <- "Wald"
options(width=9000, digits = 3)
# G&D used the anonymized data that we had shared
load("diary_anon_old/diary_anonymised.rdata")
diary_anon <- diary
diary_anon %>% summarise(n(), n_distinct(person))</code></pre>
<pre><code>## n() n_distinct(person)
## 1 30956 1208</code></pre>
<pre class="r"><code>diary_anon %>% filter(!is.na(included)) %>% summarise(n(), n_distinct(person))</code></pre>
<pre><code>## n() n_distinct(person)
## 1 28493 1043</code></pre>
<pre class="r"><code>diary_anon %>% filter(!is.na(included_all)) %>% summarise(n(), n_distinct(person))</code></pre>
<pre><code>## n() n_distinct(person)
## 1 28493 1043</code></pre>
<pre class="r"><code>diary_anon %>% filter(!is.na(included_lax)) %>% summarise(n(), n_distinct(person))</code></pre>
<pre><code>## n() n_distinct(person)
## 1 17960 517</code></pre>
<pre class="r"><code>load("full_data.rdata")
diary %>% summarise(n(), n_distinct(person))</code></pre>
<pre><code>## n() n_distinct(person)
## 1 30956 1208</code></pre>
<pre class="r"><code>diary %>% filter(!is.na(included_all)) %>% summarise(n(), n_distinct(session))</code></pre>
<pre><code>## n() n_distinct(session)
## 1 28729 1054</code></pre>
<pre class="r"><code>diary %>% filter(!is.na(included_lax)) %>% summarise(n(), n_distinct(session))</code></pre>
<pre><code>## n() n_distinct(session)
## 1 17960 517</code></pre>
<pre class="r"><code>diary = diary %>%
mutate(
included = included_all,
fertile = if_else(is.na(prc_stirn_b_squished), prc_stirn_b_backward_inferred, prc_stirn_b_squished),
partner_st_vs_lt = partner_attractiveness_shortterm - partner_attractiveness_longterm
) %>% group_by(person) %>%
mutate(
fertile_mean = mean(fertile, na.rm = T)
)
# diary %>% select(person, cycle_nr, day_number, RCD, FCD, created_date, prc_stirn_b, included_levels) %>% full_join(diary_anon %>% select(person, cycle_nr, day_number, RCD, FCD, prc_stirn_b, included_levels), by = c("person", "cycle_nr", "FCD")) %>% filter(!identical(RCD.x, RCD.y)) %>% arrange(person, day_number.x) %>% View()
#' incorrect standardization (ignores multilevel structure), fixed using this function
scale <- function(x) {
if(length(x) == length(diary$person)) {
m <- mean(x[!duplicated(diary$person)], na.rm = T)
sd <- sd(x[!duplicated(diary$person)], na.rm = T)
} else {
m <- mean(x, na.rm = T)
sd <- sd(x, na.rm = T)
}
(x-m)/sd
}
#' missing standardization of partner longterm (ignores multilevel structure)
diary$Zpartner_attractiveness_longterm <- scale(diary$partner_attractiveness_longterm)
#' use variable id, named person in dataset
diary$id <- diary$person
#' turned factors into numbers but don't mention it
diary$included <- diary$included_all
diary$included_lax <- diary$included_lax
#' missing variable breakup_mean
#' breakup mean not reported
diary <- diary %>% dplyr::group_by(person) %>%
dplyr::mutate(breakup_mean = mean(breakup, na.rm = TRUE)) %>%
dplyr::ungroup()
##compute corrected ST attractiveness
diary$Zattractiveness_stp <- scale(diary$attractiveness_stp)
diary$Zpartner_attractiveness_physical <- scale(diary$partner_attractiveness_physical)
diary$Zsatisfaction_sexual_intercourse <- scale(diary$satisfaction_sexual_intercourse)
diary$STattractiveness <- rowMeans(diary[,c("Zattractiveness_stp", "Zpartner_attractiveness_physical","Zsatisfaction_sexual_intercourse")], na.rm = TRUE)
diary$ZSTattractiveness <- scale(diary$STattractiveness)
diary$zSTvszLT <- diary$ZSTattractiveness - diary$Zpartner_attractiveness_longterm
diary$ZzSTvszLT <- scale(diary$zSTvszLT)
#remove missing values, summed ST composite
diary$partner_attractiveness_shortterm2 <- rowSums(diary[,c("attractiveness_stp", "partner_attractiveness_physical","satisfaction_sexual_intercourse")], na.rm = F)
diary$Zpartner_attractiveness_shortterm2 <- scale(diary$partner_attractiveness_shortterm2)
diary$zST2vszLT <- diary$Zpartner_attractiveness_shortterm2 - diary$Zpartner_attractiveness_longterm
diary$ZzST2vszLT <- scale(diary$zST2vszLT)
#Compute partner attractiveness +/- 1SD
diary$ZSTattractiveness_p1 <- diary$ZSTattractiveness + 1
diary$Zpartner_attractiveness_shortterm2_p1 <- diary$Zpartner_attractiveness_shortterm2 + 1
diary$ZzSTvszLT_p1 <- diary$ZzSTvszLT + 1
diary$ZzST2vszLT_p1 <- diary$ZzST2vszLT + 1
diary$ZSTattractiveness_m1 <- diary$ZSTattractiveness - 1
diary$Zpartner_attractiveness_shortterm2_m1 <- diary$Zpartner_attractiveness_shortterm2 - 1
diary$ZzSTvszLT_m1 <- diary$ZzSTvszLT - 1
diary$ZzST2vszLT_m1 <- diary$ZzST2vszLT - 1
######other computes
#Effect Codes and Dummy Codes
diary$included_zero <- as.numeric(diary$included) - 1
diary$included_lax_zero <- as.numeric(diary$included_lax) - 1
diary$menses_effect <-ifelse(as.numeric(diary$menstruation) == 3, .5, -.5)
diary$premenses_effect <-ifelse(as.numeric(diary$menstruation) == 2, .5, -.5)
#Standardizing variables and partner attractiveness composites
diary$Zfertile <- scale(diary$fertile)
diary$Zfertile_mean <- scale(diary$fertile_mean)
diary$Zfertile_narrow <- scale(diary$fertile_narrow)
diary$Zfertile_broad <- scale(diary$fertile_broad)
diary$ZMV_diff <- scale(diary$MV_diff)
diary$Zattractiveness_relativ_1 <- scale(diary$attractiveness_relativ_1)
diary$Zattractiveness_stp <- scale(diary$attractiveness_stp)
diary$Zsatisfaction_sexual_intercourse <- scale(diary$satisfaction_sexual_intercourse)
diary$Zpartner_attractiveness_physical <- scale(diary$partner_attractiveness_physical)
diary$Zpartner_attractiveness_longterm <- scale(diary$partner_attractiveness_longterm)
diary$partner_attractiveness_rel_to_self_COMP_OURMEAS <- rowMeans(diary[,c("ZMV_diff", "Zattractiveness_relativ_1")], na.rm = TRUE)
diary$Zpartner_attractiveness_rel_to_self_COMP_OURMEAS <- scale(diary$partner_attractiveness_rel_to_self_COMP_OURMEAS)
diary$Proprietariness<- rowSums(diary[,c("male_jealousy_2","male_mate_retention_1")], na.rm = FALSE)
diary$Attentiveness<- rowSums(diary[,c("male_mate_retention_2","male_attention_1")], na.rm = FALSE)
diary$MV_self<- rowMeans(diary[,c("MV_1","MV_2","MV_3","MV_4","MV_5")], na.rm = TRUE)
diary$ZMV_self<- scale(diary$MV_self)
diary$MV_part<- rowMeans(diary[,c("MV_P_1","MV_P_2","MV_P_3","MV_P_4","MV_P_5")], na.rm = TRUE)
diary$ZMV_part<- scale(diary$MV_part)
diary$MV_diff_OURS=diary$MV_part - diary$MV_self
diary$ZMV_diff_OURS <- scale(diary$MV_diff_OURS)
diary$partner_attractiveness_rel_to_self_OURMEAS_LONG<- rowMeans(diary[,c("ZMV_diff_OURS","Zattractiveness_relativ_1")], na.rm = TRUE)
diary$Zpartner_attractiveness_rel_to_self_OURMEAS_LONG <- scale(diary$partner_attractiveness_rel_to_self_OURMEAS_LONG)
diary$partner_attractiveness_COMP5var_LONG<- rowMeans(diary[,c("Zattractiveness_stp","Zsatisfaction_sexual_intercourse","ZMV_part","Zpartner_attractiveness_physical","Zattractiveness_relativ_1")], na.rm = TRUE)
diary$Zpartner_attractiveness_COMP5var_LONG <- scale(diary$partner_attractiveness_COMP5var_LONG)
#Compute partner attractiveness +/- 1SD
diary$Zpartner_attractiveness_physical_p1 <- diary$Zpartner_attractiveness_physical + 1
diary$Zpartner_attractiveness_rel_to_self_OURMEAS_LONG_p1 <- diary$Zpartner_attractiveness_rel_to_self_OURMEAS_LONG + 1
diary$Zpartner_attractiveness_COMP5var_LONG_p1 <- diary$Zpartner_attractiveness_COMP5var_LONG + 1
diary$Zpartner_attractiveness_physical_m1 <- diary$Zpartner_attractiveness_physical - 1
diary$Zpartner_attractiveness_rel_to_self_OURMEAS_LONG_m1 <- diary$Zpartner_attractiveness_rel_to_self_OURMEAS_LONG - 1
diary$Zpartner_attractiveness_COMP5var_LONG_m1 <- diary$Zpartner_attractiveness_COMP5var_LONG - 1
#filter: preregistered sample of 123
# diary2 <- diary [which (diary$included_zero >= 0
# & diary$cycle_regularity <= 2 & diary$breakup_mean == 2), ]
diary2 <- diary [which (diary$included_zero >= 0
& as.numeric(diary$cycle_regularity) <= 2 & diary$breakup_mean == 2), ]</code></pre>
</div>
<div id="study-1-gocd1-follow-up" class="section level2">
<h2>Study 1 (GOCD1) Follow-up</h2>
<p>In our follow-up we revert some of the deviations by Gangestad and Dinh (2020), because they were not explained and we do not see how they make sense/why they were necessary:</p>
<ul>
<li>we do not standardise the fertility predictor as it is best seen as a probability of being in the fertile window, i.e. a single-imputed dummy variable. Estimates based on it disattenuate for invalidity as simulated by Gangestad et al. 2016.</li>
<li>We do not effect-code the premenses and menses variable, but dummy-code them instead, as we had done before.</li>
</ul>
<p>Neither of these changes should affect the test statistics.</p>
<pre class="r"><code>diary$premenstruation <- if_else(diary$menstruation == "pre", "yes", "no")
diary$menstruation <- if_else(diary$menstruation == "yes", "yes", "no")</code></pre>
<div id="corrected-preregistered-moderator-analyses" class="section level3">
<h3>Corrected preregistered moderator analyses</h3>
<div id="extra-pair-desire-and-behaviour" class="section level4">
<h4>Extra-pair desire and behaviour</h4>
<pre class="r"><code>response <- list()
###############Table 1
response$Table1_extra_pair_phys = lmer(extra_pair ~
+ Zpartner_attractiveness_physical*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax == "cycling"))
summary(response$Table1_extra_pair_phys)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +Zpartner_attractiveness_physical * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax == "cycling")
##
## REML criterion at convergence: 3706
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.221 -0.566 -0.124 0.449 5.668
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.428 0.654
## Residual 0.326 0.571
## Number of obs: 1915, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7541 0.0582 150.1600 30.14 < 2e-16 ***
## Zpartner_attractiveness_physical -0.0926 0.0602 149.5375 -1.54 0.13
## fertile_narrow 0.2661 0.0640 1784.7375 4.16 3.4e-05 ***
## Zpartner_attractiveness_physical:fertile_narrow -0.0554 0.0651 1783.6675 -0.85 0.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Zprt__ frtl_n
## Zprtnr_ttr_ 0.004
## fertil_nrrw -0.207 -0.004
## Zprtnr_t_:_ -0.004 -0.202 0.001</code></pre>
<pre class="r"><code>response$Table1_extra_pair_phys_hc = lmer(extra_pair ~
+ Zpartner_attractiveness_physical*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax != "cycling"))
summary(response$Table1_extra_pair_phys_hc)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +Zpartner_attractiveness_physical * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax != "cycling")
##
## REML criterion at convergence: 7596
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.746 -0.535 -0.152 0.375 7.957
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.250 0.500
## Residual 0.262 0.512
## Number of obs: 4463, groups: id, 352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7059 0.0289 392.7145 59.00 <2e-16 ***
## Zpartner_attractiveness_physical -0.0539 0.0298 394.6595 -1.81 0.072 .
## fertile_narrow -0.0279 0.0379 4157.8756 -0.74 0.462
## Zpartner_attractiveness_physical:fertile_narrow -0.0570 0.0398 4162.3364 -1.43 0.153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Zprt__ frtl_n
## Zprtnr_ttr_ -0.085
## fertil_nrrw -0.246 0.019
## Zprtnr_t_:_ 0.019 -0.245 -0.076</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST = lmer(extra_pair ~
+ ZSTattractiveness*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax == "cycling"))
summary(response$Table1_extra_pair_ST)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +ZSTattractiveness * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax == "cycling")
##
## REML criterion at convergence: 3708
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.189 -0.571 -0.122 0.447 5.642
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.437 0.661
## Residual 0.326 0.571
## Number of obs: 1915, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7554 0.0588 149.5092 29.85 < 2e-16 ***
## ZSTattractiveness -0.0138 0.0622 150.4847 -0.22 0.82
## fertile_narrow 0.2687 0.0640 1784.0645 4.20 2.9e-05 ***
## ZSTattractiveness:fertile_narrow -0.0833 0.0667 1785.7665 -1.25 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ZSTttr frtl_n
## ZSTttrctvns -0.049
## fertil_nrrw -0.205 0.004
## ZSTttrctv:_ 0.004 -0.210 -0.035</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST_hc = lmer(extra_pair ~
+ ZSTattractiveness*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax != "cycling"))
summary(response$Table1_extra_pair_ST_hc)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +ZSTattractiveness * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax != "cycling")
##
## REML criterion at convergence: 7597
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.752 -0.537 -0.151 0.380 7.961
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.250 0.500
## Residual 0.262 0.512
## Number of obs: 4463, groups: id, 352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7097 0.0291 392.7492 58.70 <2e-16 ***
## ZSTattractiveness -0.0584 0.0303 391.8794 -1.93 0.055 .
## fertile_narrow -0.0263 0.0381 4157.7004 -0.69 0.490
## ZSTattractiveness:fertile_narrow -0.0424 0.0397 4155.1257 -1.07 0.286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ZSTttr frtl_n
## ZSTttrctvns -0.144
## fertil_nrrw -0.246 0.032
## ZSTttrctv:_ 0.032 -0.245 -0.135</code></pre>
<pre class="r"><code>response$Table1_extra_pair_STvsLT = lmer(extra_pair ~
+ ZzSTvszLT*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax == "cycling"))
summary(response$Table1_extra_pair_STvsLT)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +ZzSTvszLT * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax == "cycling")
##
## REML criterion at convergence: 3707
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.166 -0.569 -0.128 0.458 5.642
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.431 0.656
## Residual 0.326 0.571
## Number of obs: 1915, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7539 0.0584 149.9194 30.05 < 2e-16 ***
## ZzSTvszLT 0.0958 0.0586 148.9878 1.64 0.10
## fertile_narrow 0.2673 0.0640 1784.6259 4.18 3.1e-05 ***
## ZzSTvszLT:fertile_narrow -0.0718 0.0628 1780.4692 -1.14 0.25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ZzSTLT frtl_n
## ZzSTvszLT -0.002
## fertil_nrrw -0.206 0.004
## ZzSTvszLT:_ 0.004 -0.209 -0.023</code></pre>
<pre class="r"><code>response$Table1_extra_pair_STvsLT_hc = lmer(extra_pair ~
+ ZzSTvszLT*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax != "cycling"))
summary(response$Table1_extra_pair_STvsLT_hc)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +ZzSTvszLT * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax != "cycling")
##
## REML criterion at convergence: 7599
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.767 -0.540 -0.153 0.380 7.922
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.253 0.503
## Residual 0.262 0.512
## Number of obs: 4463, groups: id, 352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7015 0.0289 393.3034 58.83 <2e-16 ***
## ZzSTvszLT 0.0574 0.0297 393.7343 1.93 0.054 .
## fertile_narrow -0.0329 0.0378 4158.0587 -0.87 0.384
## ZzSTvszLT:fertile_narrow -0.0564 0.0400 4169.1154 -1.41 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ZzSTLT frtl_n
## ZzSTvszLT -0.001
## fertil_nrrw -0.245 -0.004
## ZzSTvszLT:_ -0.004 -0.246 0.017</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST_LTadj = lmer(extra_pair ~
+ (Zpartner_attractiveness_longterm + ZSTattractiveness)*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax == "cycling"))
summary(response$Table1_extra_pair_ST_LTadj)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +(Zpartner_attractiveness_longterm + ZSTattractiveness) * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax == "cycling")
##
## REML criterion at convergence: 3711
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.165 -0.566 -0.125 0.452 5.642
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.426 0.653
## Residual 0.326 0.571
## Number of obs: 1915, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7595 0.0582 149.1139 30.25 < 2e-16 ***
## Zpartner_attractiveness_longterm -0.1362 0.0622 147.2207 -2.19 0.03 *
## ZSTattractiveness 0.0257 0.0641 149.9797 0.40 0.69
## fertile_narrow 0.2690 0.0641 1783.7343 4.20 2.8e-05 ***
## Zpartner_attractiveness_longterm:fertile_narrow 0.0317 0.0679 1779.3932 0.47 0.64
## ZSTattractiveness:fertile_narrow -0.0928 0.0691 1783.8600 -1.34 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Zprt__ ZSTttr frtl_n Zp__:_
## Zprtnr_ttr_ -0.032
## ZSTttrctvns -0.038 -0.281
## fertil_nrrw -0.207 -0.002 0.005
## Zprtnr_t_:_ -0.003 -0.201 0.059 0.002
## ZSTttrctv:_ 0.005 0.060 -0.215 -0.035 -0.262</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST_LTadj_hc = lmer(extra_pair ~
+ (Zpartner_attractiveness_longterm + ZSTattractiveness)*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax != "cycling"))
summary(response$Table1_extra_pair_ST_LTadj_hc)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +(Zpartner_attractiveness_longterm + ZSTattractiveness) * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax != "cycling")
##
## REML criterion at convergence: 7584
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.790 -0.537 -0.157 0.376 7.934
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.235 0.484
## Residual 0.262 0.512
## Number of obs: 4463, groups: id, 352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7284 0.0286 392.3561 60.43 < 2e-16 ***
## Zpartner_attractiveness_longterm -0.1669 0.0351 394.0969 -4.75 2.8e-06 ***
## ZSTattractiveness -0.0299 0.0301 392.5224 -0.99 0.32
## fertile_narrow -0.0310 0.0386 4160.9641 -0.80 0.42
## Zpartner_attractiveness_longterm:fertile_narrow 0.0422 0.0497 4180.4838 0.85 0.40
## ZSTattractiveness:fertile_narrow -0.0504 0.0407 4157.8658 -1.24 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Zprt__ ZSTttr frtl_n Zp__:_
## Zprtnr_ttr_ -0.139
## ZSTttrctvns -0.112 -0.200
## fertil_nrrw -0.252 0.038 0.023
## Zprtnr_t_:_ 0.036 -0.249 0.054 -0.162
## ZSTttrctv:_ 0.023 0.056 -0.253 -0.095 -0.216</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST_vs_LTadj = lmer(extra_pair ~
+ (Zpartner_attractiveness_longterm * ZSTattractiveness)*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax == "cycling"))
summary(response$Table1_extra_pair_ST_vs_LTadj)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +(Zpartner_attractiveness_longterm * ZSTattractiveness) * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax == "cycling")
##
## REML criterion at convergence: 3718
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.159 -0.564 -0.127 0.453 5.639
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.425 0.652
## Residual 0.327 0.571
## Number of obs: 1915, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.78e+00 5.99e-02 1.48e+02 29.66 < 2e-16 ***
## Zpartner_attractiveness_longterm -1.40e-01 6.22e-02 1.46e+02 -2.25 0.026 *
## ZSTattractiveness 7.06e-03 6.63e-02 1.50e+02 0.11 0.915
## fertile_narrow 2.66e-01 6.57e-02 1.78e+03 4.05 5.3e-05 ***
## Zpartner_attractiveness_longterm:ZSTattractiveness -5.67e-02 5.14e-02 1.46e+02 -1.10 0.272
## Zpartner_attractiveness_longterm:fertile_narrow 3.22e-02 6.80e-02 1.78e+03 0.47 0.636
## ZSTattractiveness:fertile_narrow -8.82e-02 7.13e-02 1.78e+03 -1.24 0.216
## Zpartner_attractiveness_longterm:ZSTattractiveness:fertile_narrow 1.00e-02 5.67e-02 1.78e+03 0.18 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Zprt__ ZSTttr frtl_n Zp__:ZST Zp__:_ ZSTt:_
## Zprtnr_ttr_ -0.045
## ZSTttrctvns -0.097 -0.256
## fertil_nrrw -0.207 0.000 0.020
## Zprtn__:ZST -0.239 0.058 0.255 0.048
## Zprtnr_t_:_ -0.001 -0.201 0.054 -0.009 -0.009
## ZSTttrctv:_ 0.021 0.055 -0.222 -0.087 -0.068 -0.241
## Zpr__:ZST:_ 0.048 -0.009 -0.067 -0.221 -0.202 0.052 0.243</code></pre>
<pre class="r"><code>nomod <- lmer(extra_pair ~ Zpartner_attractiveness_longterm * ZSTattractiveness + fertile_narrow + (1 | id), data = diary %>% filter(included_lax == "cycling"))
anova(nomod, response$Table1_extra_pair_ST_vs_LTadj)</code></pre>
<pre><code>## refitting model(s) with ML (instead of REML)</code></pre>
<pre><code>## Data: diary %>% filter(included_lax == "cycling")
## Models:
## nomod: extra_pair ~ Zpartner_attractiveness_longterm * ZSTattractiveness +
## nomod: fertile_narrow + (1 | id)
## response$Table1_extra_pair_ST_vs_LTadj: extra_pair ~ +(Zpartner_attractiveness_longterm * ZSTattractiveness) *
## response$Table1_extra_pair_ST_vs_LTadj: fertile_narrow + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## nomod 7 3703 3742 -1845 3689
## response$Table1_extra_pair_ST_vs_LTadj 10 3707 3763 -1844 3687 1.79 3 0.62</code></pre>
<pre class="r"><code>response$Table1_extra_pair_ST_vs_LTadj_hc = lmer(extra_pair ~
+ (Zpartner_attractiveness_longterm + ZSTattractiveness)*fertile_narrow + (1 | id), data = diary %>%
filter(included_lax != "cycling"))
summary(response$Table1_extra_pair_ST_vs_LTadj_hc)</code></pre>
<pre><code>## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: extra_pair ~ +(Zpartner_attractiveness_longterm + ZSTattractiveness) * fertile_narrow + (1 | id)
## Data: diary %>% filter(included_lax != "cycling")