Desiccation and starvation resistance
Last updated: 2021-10-05
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Knit directory: exp_evol_respiration/
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Load packages
library(tidyverse)
library(coxme)
library(brms)
library(tidybayes)
library(ggridges)
library(kableExtra)
library(knitrhooks) # install with devtools::install_github("nathaneastwood/knitrhooks")
library(showtext)
output_max_height() # a knitrhook option
options(stringsAsFactors = FALSE)
# set up nice font for figure
nice_font <- "Lora"
font_add_google(name = nice_font, family = nice_font, regular.wt = 400, bold.wt = 700)
showtext_auto()Load data
# load desiccation resistance data
DesRes <- read.csv("data/2.DesRes.csv") %>%
# add event (all flies died)
mutate(EVENT = 1,
LINE = paste0(Treatment, substr(Replicate, 2, 2)),
ID = paste(LINE, Vial, sep = ''))
# calculate survival times
# paste time and date
DesRes$d <- paste(DesRes$Death_date, DesRes$Death_time, sep = ' ')
# experiment start time
start_timeDes <- "04/02/2017 12:00"
DesRes$survival.time <- as.numeric(strptime(DesRes$d, format = "%d/%m/%Y %H") - strptime(start_timeDes, format = "%d/%m/%Y %H"))
# load starvation resistance data
StaRes <- read.csv("data/2.StarvRes.csv") %>%
# add event (all flies died)
mutate(EVENT = 1,
LINE = paste0(Treatment, substr(Replicate, 2, 2)),
ID = paste(LINE, Vial, sep = ''))
# calculate survival times
# paste time and date
StaRes$d <- paste(StaRes$Death_date, StaRes$Death_time, sep = ' ')
# experiment start time
start_timeSta <- "04/02/2017 10:00"
StaRes$survival.time <- as.numeric(strptime(StaRes$d, format = "%d/%m/%Y %H") - strptime(start_timeSta, format = "%d/%m/%Y %H"))
# 5 individuals have missing survival times which we will right censor at max. survival time
# Two M females (M2) were right censored as survival time not recorded
# Three P females (P2 Vial 6 and P4 vial 9) were right censored as survival time not recorded
StaRes[which(is.na(StaRes$survival.time)), 'EVENT'] <- 0
StaRes[which(is.na(StaRes$survival.time)), 'survival.time'] <- max(na.omit(StaRes$survival.time))Inspecting the raw data
bind_rows(
DesRes %>%
select(Treatment, Sex, survival.time) %>% mutate(var = 'Desiccation'),
StaRes %>% filter(EVENT == 1) %>%
select(Treatment, Sex, survival.time) %>% mutate(var = 'Starvation')
) %>%
mutate(var2 = paste(Treatment, Sex)) %>%
ggplot(aes(x = survival.time, y = Sex, fill = var2)) +
geom_boxplot() +
scale_fill_manual(values = c("pink", "skyblue", "red", "blue"), name = "",
labels = c('Monogamy Females', 'Monogamy Males',
'Polandry Females', 'Polandry Males')) +
labs(x = 'Survival time (hours)') +
facet_wrap(~var, ncol = 2) +
theme_bw() +
NULL
Figure 1: Survival time in hours for flies in each treatment split by sex.
Survival analysis
We modeled desiccation and starvation resistance using survival analysis. We measured time in hours until death (EVENT = 1) for single sex triads of flies housed in vials (n = 7-10 vials per replicate per sex) containing no media and silica gel beads between the cotton and Parafilm enclosing the vial (desiccation resistance) or an agar media providing moisture only (starvation resistance). We monitored vials for deaths every two hours until all flies perished. For the starvation resistance assay five individuals (two M females and three E females) were right censored (EVENT = 0) at the end of the observation period as death times were not recorded or remained alive.
Kaplan-Meier survival curve
First we plot Kaplan-Meier survival curves.
survminer::ggsurvplot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes),
conf.int = TRUE,
risk.table = FALSE,
linetype = "Sex",
palette = c("pink", "skyblue", "red", "blue"),
xlab = "Time (hours)",
legend = 'right',
legend.title = "",
legend.labs = c("M \u2640","M \u2642",
'E \u2640','E \u2642'),
break.time.by = 12,
ggtheme = theme_bw()) 
#ggsave(filename = 'figures/desiccation.pdf', width = 5.5, height = 5, dpi = 600, useDingbats = FALSE)
survminer::ggsurvplot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes),
conf.int = TRUE,
risk.table = FALSE,
linetype = "Sex",
palette = c("pink", "skyblue", "red", "blue"),
xlab = "Time (hours)",
legend = 'right',
legend.title = "",
legend.labs = c("M \u2640","M \u2642",
'E \u2640','E \u2642'),
break.time.by = 12,
ggtheme = theme_bw()) 
#ggsave(filename = 'figures/starvation.pdf', width = 5.5, height = 5, dpi = 600, useDingbats = FALSE)Figure 2: Kaplan-Meier survival curves for flies in each treatment split by sex. + indicates censored individuals (n = 5).
Median survival times
bind_rows(
summary(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes))$table %>%
as_tibble() %>%
mutate(Treatment = c('M', 'M', 'E', 'E'),
Sex = c('Female', 'Male', 'Female', 'Male')),
summary(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes))$table %>%
as_tibble() %>%
mutate(Treatment = c('M', 'M', 'E', 'E'),
Sex = c('Female', 'Male', 'Female', 'Male'))) %>%
mutate(`Median (± 95% CI)` = paste0(median, ' (', `0.95LCL`, '-', `0.95UCL`, ')')) %>%
dplyr::select(Treatment, Sex, N = records, `N events` = events, `Median (± 95% CI)`) %>%
#write_csv('output/stress_medians.csv')
kable() %>%
kable_styling(full_width = FALSE) %>%
group_rows("Desiccation", 1, 4) %>%
group_rows("Starvation", 5, 8)| Treatment | Sex | N | N events | Median (± 95% CI) |
|---|---|---|---|---|
| Desiccation | ||||
| M | Female | 108 | 108 | 38 (38-42) |
| M | Male | 111 | 111 | 32 (32-34) |
| E | Female | 114 | 114 | 40 (38-42) |
| E | Male | 105 | 105 | 32 (32-34) |
| Starvation | ||||
| M | Female | 120 | 118 | 59 (54-64) |
| M | Male | 120 | 120 | 42 (40-46) |
| E | Female | 120 | 117 | 68 (64-72) |
| E | Male | 120 | 120 | 44 (42-44) |
Check proportional hazards assumption
Next we need to check that the proportional hazards assumption is not violated before fitting the model, where crossing hazards (lines) indicate violation of the proportional hazards assumption. For both desiccation and starvation we see crossing hazards for the male survival curves. We therefore fit accelerated failure time (AFT) models with a Weibull distribution (see here).
# assess proportional hazards assumption
par(mar = c(2, 2, 2, 2), mfrow = c(1, 2))
plot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes),
lty = 1:2, lwd = 2,
col = c("pink", "skyblue", "red", "blue"),
main = 'Desiccation',
fun = "cloglog")
legend("topleft", c("M \u2640","M \u2642",'E \u2640','E \u2642'),
col = c("pink", "skyblue", "red", "blue"),
lty = 1:2,
lwd = 2,
bty = 'n'
)
plot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes),
lty = 1:2, lwd = 2,
col = c("pink", "skyblue", "red", "blue"),
main = 'Starvation',
fun = "cloglog")
par(mfrow = c(1, 1))Fit the survival models for desiccation and starvation resistance
We fit an accelerated failure time model in brms using family = weibull(), with time (hours) to event (death) as the response and sexual selection treatment (Treatment; Monogamy or Elevated polyandry), Sex (female or male) and their interaction as predictors. See here for a helpful explanation on fitting survival models in brms. We also include replicate treatment as a random intercept term for each of the 8 lines and a random slope term to allow the effect of selection treatment to vary across replicate lines. We also include vial ID as a random intercept term as individuals housed in the same vial may show a correlated response.
if(!file.exists("output/des_brm.rds")){ # if the model doesn't exist fit it, else load it
des_brm <- brm(survival.time | cens(1 - EVENT) ~ Treatment * Sex + (Treatment|LINE) + (1|ID),
prior = c(set_prior("normal(0,0.5)", class = "b"),
set_prior("cauchy(0,0.1)", class = "sd")),
iter = 5000, chains = 4, cores = 4,
control = list(max_treedepth = 20,
adapt_delta = 0.999),
data = DesRes, family = weibull())
#saveRDS(des_brm, "output/des_brm_noslope.rds") # save with no random slope term
saveRDS(des_brm, "output/des_brm.rds")
} else {
des_brm <- readRDS('output/des_brm_noslope.rds')
}
if(!file.exists("output/sta_brm.rds")){ # if the model doesn't exist fit it, else load it
sta_brm <- brm(survival.time | cens(EVENT) ~ Treatment * Sex + (Treatment|LINE) + (1|ID),
prior = c(set_prior("normal(0,0.5)", class = "b"),
set_prior("cauchy(0,0.1)", class = "sd")),
iter = 5000, chains = 4, cores = 4,
control = list(max_treedepth = 20,
adapt_delta = 0.999),
# brm uses 0 = event, 1 = censor so need to recode
data = StaRes %>% mutate(EVENT = if_else(EVENT == 1, 0, 1)),
family = weibull())
#saveRDS(sta_brm, "output/sta_brm_noslope.rds") # save with no random slope term
saveRDS(sta_brm, "output/sta_brm.rds")
} else {
sta_brm <- readRDS('output/sta_brm_noslope.rds')
}Table of model parameter estimates - eclosion time
Formatted table
Taking the exponent of the coefficients gives an estimate of the multiplicative effect of the time to event compared to baseline (Monogamy females) (see here). For instance, for desiccation resistance, being male accelerates time to event by a factor of exp(-0.167) = 0.847 (95% confidence intervals = 0.795 - 0.902), i.e. Monogamy males live 0.847 times as long as Monogamy females.
des_test <- bind_rows(
hypothesis(des_brm, 'TreatmentP = 0')$hypothesis,
hypothesis(des_brm, 'Sexm = 0')$hypothesis,
hypothesis(des_brm, 'TreatmentP:Sexm = 0')$hypothesis
) %>%
mutate(Parameter = c('Treatment (E)', 'Sex (M)', 'Treatment (E) x Sex (M)'),
across(2:5, round, 3)) %>%
relocate(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, Star)
sta_test <- bind_rows(
hypothesis(sta_brm, 'TreatmentP = 0')$hypothesis,
hypothesis(sta_brm, 'Sexm = 0')$hypothesis,
hypothesis(sta_brm, 'TreatmentP:Sexm = 0')$hypothesis
) %>%
mutate(Parameter = c('Treatment (E)', 'Sex (M)', 'Treatment (E) x Sex (M)'),
across(2:5, round, 3)) %>%
relocate(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, Star)
des_pvals <- bayestestR::p_direction(des_brm) %>%
as.data.frame() %>%
mutate(vars = map_chr(str_split(Parameter, "_"), ~ .x[2]),
p_val = 1 - pd,
star = ifelse(p_val < 0.05, "\\*", "")) %>%
select(vars, p_val, star)
sta_pvals <- bayestestR::p_direction(sta_brm) %>%
as.data.frame() %>%
mutate(vars = map_chr(str_split(Parameter, "_"), ~ .x[2]),
p_val = 1 - pd,
star = ifelse(p_val < 0.05, "\\*", "")) %>%
select(vars, p_val, star)
bind_rows(
des_test %>%
mutate(vars = c('TreatmentP', 'Sexm', 'TreatmentP:Sexm')) %>%
left_join(des_pvals %>% filter(vars != 'Intercept'),
by = c("vars")) %>%
select(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, `p` = p_val, star),
sta_test %>%
mutate(vars = c('TreatmentP', 'Sexm', 'TreatmentP:Sexm')) %>%
left_join(sta_pvals %>% filter(vars != 'Intercept'),
by = c("vars")) %>%
select(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, `p` = p_val, star)
) %>%
mutate(p = ifelse(p > 0.001, round(p, 3), '< 0.001')) %>%
rename(` ` = star) %>%
kable() %>%
kable_styling(full_width = FALSE) %>%
group_rows("Desiccation", 1, 3) %>%
group_rows("Starvation", 4, 6)| Parameter | Estimate | Est.Error | CI.Lower | CI.Upper | p | |
|---|---|---|---|---|---|---|
| Desiccation | ||||||
| Treatment (E) | 0.054 | 0.085 | -0.117 | 0.224 | 0.24 | |
| Sex (M) | -0.167 | 0.032 | -0.229 | -0.103 | < 0.001 | * |
| Treatment (E) x Sex (M) | -0.097 | 0.046 | -0.188 | -0.007 | 0.018 | * |
| Starvation | ||||||
| Treatment (E) | 0.083 | 0.097 | -0.112 | 0.273 | 0.181 | |
| Sex (M) | -0.322 | 0.035 | -0.391 | -0.256 | < 0.001 | * |
| Treatment (E) x Sex (M) | -0.126 | 0.049 | -0.222 | -0.029 | 0.005 | * |
Complete output from summary.brmsfit()
The shape parameter (\(1/\)scale parameter; see here) describes the change in hazard over time where:
- \(p\) = 1: constant hazard
- \(p\) > 1: increasing hazard over time
- \(p\) < 1: decreasing hazard over time
Desiccation
des_brm Family: weibull
Links: mu = log; shape = identity
Formula: survival.time | cens(1 - EVENT) ~ Treatment * Sex + (1 | LINE) + (1 | ID)
Data: DesRes (Number of observations: 438)
Draws: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
total post-warmup draws = 10000
Group-Level Effects:
~ID (Number of levels: 146)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.11 0.01 0.09 0.13 1.00 3552 5532
~LINE (Number of levels: 8)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.11 0.04 0.06 0.20 1.00 3911 5251
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 3.66 0.06 3.54 3.78 1.00 4601 5308
TreatmentP 0.05 0.09 -0.12 0.22 1.00 4334 6055
Sexm -0.17 0.03 -0.23 -0.10 1.00 5200 6875
TreatmentP:Sexm -0.10 0.05 -0.19 -0.01 1.00 4810 6616
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 7.56 0.36 6.89 8.28 1.00 6108 7957
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Starvation
sta_brm Family: weibull
Links: mu = log; shape = identity
Formula: survival.time | cens(EVENT) ~ Treatment * Sex + (1 | LINE) + (1 | ID)
Data: StaRes %>% mutate(EVENT = if_else(EVENT == 1, 0, 1 (Number of observations: 480)
Draws: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
total post-warmup draws = 10000
Group-Level Effects:
~ID (Number of levels: 160)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.12 0.01 0.09 0.14 1.00 4057 6009
~LINE (Number of levels: 8)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.13 0.04 0.07 0.23 1.00 3677 5482
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 4.11 0.07 3.97 4.25 1.00 3511 4489
TreatmentP 0.08 0.10 -0.11 0.27 1.00 3727 4908
Sexm -0.32 0.03 -0.39 -0.26 1.00 4942 5685
TreatmentP:Sexm -0.13 0.05 -0.22 -0.03 1.00 4874 6607
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 5.96 0.27 5.45 6.49 1.00 6453 7484
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Posterior effect size of treatment and sex on survival
# get posterior predictions
post_des <- posterior_samples(des_brm) %>%
as_tibble() %>%
select(contains("b_"), -contains("Intercept")) %>%
mutate(draw = 1:n()) %>%
pivot_longer(-draw) %>%
mutate(key = str_remove_all(name, "b_"))
post_sta <- posterior_samples(sta_brm) %>%
as_tibble() %>%
select(contains("b_"), -contains("Intercept")) %>%
mutate(draw = 1:n()) %>%
pivot_longer(-draw) %>%
mutate(key = str_remove_all(name, "b_"))
bind_rows(post_des %>% mutate(var = 'Desiccation'),
post_sta %>% mutate(var = 'Starvation')) %>%
mutate(key = recode(key, TreatmentP = "Treatment (E)", Sexm = 'Sex (M)', `TreatmentP:Sexm` = 'Treatment (E) x Sex (M)')) %>%
ggplot(aes(x = value, y = key, fill = key)) +
geom_vline(xintercept = 0, linetype = 2) +
stat_halfeye(alpha = .8) +
scale_fill_brewer(palette = "Spectral") +
coord_cartesian(xlim = c(-0.4, 0.4)) +
labs(x = "Effect size", y = "Model parameter") +
facet_wrap(~var) +
theme_ridges() +
theme(legend.position = 'none',
legend.title = element_blank()) +
NULL
Posterior effect size of treatment on survival, for each sex
Here, we use the model to predict the mean survival coefficient in each treatment and sex (averaged across the eight replicate selection lines). We then calculate the effect size of treatment by subtracting the (sex-specific) mean for the M treatment from the mean for the E treatment. We see that there is no overall effect of treatment in males or females.
new_data_stress <- expand_grid(Sex = c("m", "f"),
Treatment = c("M", "P"),
LINE = NA) %>%
mutate(type = 1:4)
fitted_des <- posterior_epred(
des_brm, newdata = new_data_stress, re_formula = NA,
summary = FALSE) %>%
reshape2::melt() %>% rename(draw = Var1, type = Var2) %>%
as_tibble() %>%
left_join(new_data_stress, by = "type") %>%
select(draw, value, Sex, Treatment)
treat_diff_des <- fitted_des %>%
spread(Treatment, value) %>%
mutate(`Difference in means (Poly - Mono)` = P - M)
fitted_sta <- posterior_epred(
sta_brm, newdata = new_data_stress, re_formula = NA,
summary = FALSE) %>%
reshape2::melt() %>% rename(draw = Var1, type = Var2) %>%
as_tibble() %>%
left_join(new_data_stress, by = "type") %>%
select(draw, value, Sex, Treatment)
treat_diff_sta <- fitted_sta %>%
spread(Treatment, value) %>%
mutate(`Difference in means (Poly - Mono)` = P - M)Dessication resistance
treat_diff_des %>%
mutate(Sex = factor(ifelse(Sex == "m", "Males", "Females"))) %>%
ggplot(aes(x = `Difference in means (Poly - Mono)`, y = Sex, fill = Sex)) +
geom_vline(xintercept = 0, linetype = 2) +
stat_halfeye() +
scale_fill_brewer(palette = 'Pastel1', direction = 1, name = "") +
scale_colour_brewer(palette = 'Pastel1', direction = 1, name = "") +
labs(x = 'Difference in means between\nselection treatments (E - M)', y = 'Sex') +
theme_bw() +
theme(legend.position = 'none',
strip.background = element_blank(),
panel.grid.major.x = element_blank()) +
NULL
Starvation resistance
treat_diff_sta %>%
mutate(Sex = factor(ifelse(Sex == "m", "Males", "Females"))) %>%
ggplot(aes(x = `Difference in means (Poly - Mono)`, y = Sex, fill = Sex)) +
geom_vline(xintercept = 0, linetype = 2) +
stat_halfeye() +
scale_fill_brewer(palette = 'Pastel1', direction = 1, name = "") +
scale_colour_brewer(palette = 'Pastel1', direction = 1, name = "") +
labs(x = 'Difference in means between\nselection treatments (E - M)', y = 'Sex') +
theme_bw() +
theme(legend.position = 'none',
strip.background = element_blank(),
panel.grid.major.x = element_blank()) +
NULL
Posterior difference in treatment effect size between sexes
This section examines the treatment \(\times\) sex interaction term, by calculating the difference in the effect size of the E/M treatment between sexes. We find evidence for a treatment \(\times\) sex interaction, i.e. the difference in survival time between the sexes was greater in the E treatment than the M treatment.
Figure
Dessication
treat_diff_des %>%
rename(d = `Difference in means (Poly - Mono)`) %>%
select(draw, Sex, d) %>%
group_by(draw) %>%
summarise(`Difference in effect size between sexes (male - female)` = d[2] - d[1],
.groups = "drop") %>%
ggplot(aes(x = `Difference in effect size between sexes (male - female)`, y = 1, fill = stat(x < 0))) +
geom_vline(xintercept = 0, linetype = 2) +
stat_halfeye() +
scale_fill_brewer(palette = 'Pastel2', direction = 1, name = "") +
theme_bw() +
theme(legend.position = 'none',
text = element_text(family = nice_font),
strip.background = element_blank()) +
ylab("Posterior density") +
#ggsave("figures/des_interaction_plot.pdf", height=4, width=4) +
NULL
Starvation
treat_diff_sta %>%
rename(d = `Difference in means (Poly - Mono)`) %>%
select(draw, Sex, d) %>%
group_by(draw) %>%
summarise(`Difference in effect size between sexes (male - female)` = d[2] - d[1],
.groups = "drop") %>%
ggplot(aes(x = `Difference in effect size between sexes (male - female)`, y = 1, fill = stat(x < 0))) +
geom_vline(xintercept = 0, linetype = 2) +
stat_halfeye() +
scale_fill_brewer(palette = 'Pastel2', direction = 1, name = "") +
theme_bw() +
theme(legend.position = 'none',
text = element_text(family = nice_font),
strip.background = element_blank()) +
ylab("Posterior density") +
#ggsave("figures/sta_interaction_plot.pdf", height=4, width=4) +
NULL
Table
treatsex_interaction_des <- treat_diff_des %>%
select(draw, Sex, d = `Difference in means (Poly - Mono)`) %>%
arrange(draw, Sex) %>%
group_by(draw) %>%
summarise(`Difference in effect size between sexes (male - female)` = d[2] - d[1],
.groups = "drop") # males - females
treatsex_interaction_sta <- treat_diff_sta %>%
select(draw, Sex, d = `Difference in means (Poly - Mono)`) %>%
arrange(draw, Sex) %>%
group_by(draw) %>%
summarise(`Difference in effect size between sexes (male - female)` = d[2] - d[1],
.groups = "drop") # males - females
bind_rows(treatsex_interaction_des %>% mutate(variable = 'Dessication'),
treatsex_interaction_sta %>% mutate(variable = 'Starvation')) %>%
rename(x = `Difference in effect size between sexes (male - female)`) %>%
group_by(variable) %>%
summarise(`Difference in effect size between sexes (male - female)` = median(x),
`Lower 95% CI` = quantile(x, probs = 0.025),
`Upper 95% CI` = quantile(x, probs = 0.975),
p = 1 - as.numeric(bayestestR::p_direction(x)),
` ` = ifelse(p < 0.05, "\\*", ""),
.groups = "drop") %>%
kable(digits=3) %>%
kable_styling(full_width = FALSE)| variable | Difference in effect size between sexes (male - female) | Lower 95% CI | Upper 95% CI | p | |
|---|---|---|---|---|---|
| Dessication | -3.562 | -7.090 | -0.065 | 0.023 | * |
| Starvation | -7.111 | -13.423 | -0.573 | 0.016 | * |
sessionInfo()R version 4.0.3 (2020-10-10) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib locale: [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] showtext_0.9-4 showtextdb_3.0 sysfonts_0.8.5 knitrhooks_0.0.4 [5] knitr_1.33 kableExtra_1.3.4 ggridges_0.5.3 tidybayes_3.0.1 [9] brms_2.16.1 Rcpp_1.0.7 coxme_2.2-16 bdsmatrix_1.3-4 [13] survival_3.2-12 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 [17] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3 tibble_3.1.3 [21] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2 loaded via a namespace (and not attached): [1] utf8_1.2.2 tidyselect_1.1.1 lme4_1.1-27.1 [4] htmlwidgets_1.5.3 grid_4.0.3 munsell_0.5.0 [7] codetools_0.2-18 DT_0.18 miniUI_0.1.1.1 [10] withr_2.4.2 Brobdingnag_1.2-6 colorspace_2.0-2 [13] highr_0.9 rstudioapi_0.13 stats4_4.0.3 [16] ggsignif_0.6.2 bayesplot_1.8.1 labeling_0.4.2 [19] git2r_0.28.0 rstan_2.21.1 KMsurv_0.1-5 [22] farver_2.1.0 datawizard_0.2.0 bridgesampling_1.1-2 [25] rprojroot_2.0.2 coda_0.19-4 vctrs_0.3.8 [28] generics_0.1.0 xfun_0.25 R6_2.5.1 [31] markdown_1.1 gamm4_0.2-6 projpred_2.0.2 [34] assertthat_0.2.1 promises_1.2.0.1 scales_1.1.1 [37] gtable_0.3.0 processx_3.5.2 rlang_0.4.11 [40] systemfonts_1.0.2 splines_4.0.3 rstatix_0.7.0 [43] broom_0.7.9 checkmate_2.0.0 inline_0.3.19 [46] yaml_2.2.1 reshape2_1.4.4 abind_1.4-5 [49] modelr_0.1.8 threejs_0.3.3 crosstalk_1.1.1 [52] backports_1.2.1 httpuv_1.6.2 rsconnect_0.8.24 [55] tensorA_0.36.2 tools_4.0.3 ellipsis_0.3.2 [58] RColorBrewer_1.1-2 jquerylib_0.1.4 posterior_1.0.1 [61] plyr_1.8.6 base64enc_0.1-3 ps_1.6.0 [64] prettyunits_1.1.1 ggpubr_0.4.0 zoo_1.8-9 [67] haven_2.4.3 fs_1.5.0 magrittr_2.0.1 [70] data.table_1.14.0 ggdist_3.0.0 openxlsx_4.2.4 [73] colourpicker_1.1.0 reprex_2.0.1 survminer_0.4.9 [76] mvtnorm_1.1-2 whisker_0.4 matrixStats_0.60.0 [79] hms_1.1.0 shinyjs_2.0.0 mime_0.11 [82] evaluate_0.14 arrayhelpers_1.1-0 xtable_1.8-4 [85] shinystan_2.5.0 rio_0.5.27 readxl_1.3.1 [88] gridExtra_2.3 rstantools_2.1.1 compiler_4.0.3 [91] V8_3.4.2 crayon_1.4.1 minqa_1.2.4 [94] StanHeaders_2.21.0-7 htmltools_0.5.1.1 mgcv_1.8-36 [97] later_1.3.0 tzdb_0.1.2 RcppParallel_5.1.4 [100] lubridate_1.7.10 DBI_1.1.1 dbplyr_2.1.1 [103] MASS_7.3-54 boot_1.3-28 Matrix_1.3-4 [106] car_3.0-11 cli_3.0.1 parallel_4.0.3 [109] insight_0.14.3 igraph_1.2.6 pkgconfig_2.0.3 [112] km.ci_0.5-2 foreign_0.8-81 xml2_1.3.2 [115] svUnit_1.0.6 dygraphs_1.1.1.6 svglite_2.0.0 [118] bslib_0.2.5.1 webshot_0.5.2 rvest_1.0.1 [121] distributional_0.2.2 callr_3.7.0 digest_0.6.27 [124] rmarkdown_2.10 cellranger_1.1.0 survMisc_0.5.5 [127] curl_4.3.2 shiny_1.6.0 gtools_3.9.2 [130] nloptr_1.2.2.2 lifecycle_1.0.0 nlme_3.1-152 [133] jsonlite_1.7.2 carData_3.0-4 viridisLite_0.4.0 [136] fansi_0.5.0 pillar_1.6.2 lattice_0.20-44 [139] loo_2.4.1 fastmap_1.1.0 httr_1.4.2 [142] pkgbuild_1.2.0 glue_1.4.2 xts_0.12.1 [145] bayestestR_0.10.5 zip_2.2.0 shinythemes_1.2.0 [148] stringi_1.7.3 sass_0.4.0