Desiccation and starvation resistance
Last updated: 2021-01-18
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Knit directory: exp_evol_respiration/
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| Rmd | 3fdbcb2 | lukeholman | 2020-11-30 | Tweaks Nov 2020 |
Load packages
library(tidyverse)
library(coxme)
library(lme4)
library(brms)
library(tidybayes)
library(ggridges)
library(kableExtra)
library(knitrhooks) # install with devtools::install_github("nathaneastwood/knitrhooks")
output_max_height() # a knitrhook option
options(stringsAsFactors = FALSE)Load data
# load desiccation resistance data
DesRes <- read.csv("data/3.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$d, format = "%d/%m/%Y %H") - strptime(start_timeDes, format = "%d/%m/%Y %H"))
# load starvation resistance data
StaRes <- read.csv("data/3.StarvRes.csv") %>%
# add event (all flies died)
mutate(EVENT = 1,
LINE = paste0(Treatment, substr(Replicate, 2, 2)),
ID = paste(LINE, Vial, sep = ''))
# calculate survival times
# paste time and date
StaRes$d <- paste(StaRes$Death_date, StaRes$Death_time, sep = ' ')
# experiment start time
start_timeSta <- "04/02/2017 12:00"
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
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 = 20 per replicate per sex) containing moisture only (agar media) or food and moisture (normal media). Our observation period concluded when all flies perished. However, due to… some individuals were right censored (EVENT = 0) at the end of the observation period…
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("Monogamy \u2640","Monogamy \u2642",
'Polandry \u2640','Polandry \u2642'),
break.time.by = 12,
ggtheme = theme_bw()) 
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("Monogamy \u2640","Monogamy \u2642",
'Polandry \u2640','Polandry \u2642'),
break.time.by = 12,
ggtheme = theme_bw()) 
Figure 2: Kaplan-Meier survival curves for flies in each treatment split by sex. + indicates censored individuals (n = 5).
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 will 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",'P \u2640','P \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 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(0,1)", 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.rds")
} else {
des_brm <- readRDS('output/des_brm.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.rds")
} else {
sta_brm <- readRDS('output/sta_brm.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(-0.183) = 0.832, i.e. Monogamy males live 0.832 times shorter than 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 (P)', 'Sex (M)', 'Treatment (P) 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 (P)', 'Sex (M)', 'Treatment (P) 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 (P) | 0.052 | 0.099 | -0.146 | 0.249 | 0.277 | |
| Sex (M) | -0.183 | 0.025 | -0.232 | -0.136 | < 0.001 | * |
| Treatment (P) x Sex (M) | -0.103 | 0.035 | -0.174 | -0.035 | 0.002 | * |
| Starvation | ||||||
| Treatment (P) | 0.088 | 0.108 | -0.132 | 0.302 | 0.184 | |
| Sex (M) | -0.334 | 0.036 | -0.404 | -0.265 | < 0.001 | * |
| Treatment (P) x Sex (M) | -0.131 | 0.050 | -0.228 | -0.031 | 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 + (Treatment | LINE)
Data: DesRes (Number of observations: 438)
Samples: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
total post-warmup samples = 10000
Group-Level Effects:
~LINE (Number of levels: 8)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
sd(Intercept) 0.11 0.04 0.06 0.23 1.00 4265
sd(TreatmentP) 0.08 0.07 0.00 0.27 1.00 2753
cor(Intercept,TreatmentP) -0.07 0.56 -0.95 0.94 1.00 5698
Tail_ESS
sd(Intercept) 4823
sd(TreatmentP) 4090
cor(Intercept,TreatmentP) 5886
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 3.67 0.06 3.54 3.80 1.00 3653 4692
TreatmentP 0.05 0.10 -0.15 0.25 1.00 3968 4620
Sexm -0.18 0.02 -0.23 -0.14 1.00 9161 8098
TreatmentP:Sexm -0.10 0.04 -0.17 -0.04 1.00 8761 7867
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 5.74 0.20 5.35 6.15 1.00 13757 7786
Samples were drawn 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 + (Treatment | LINE) + (1 | ID)
Data: StaRes %>% mutate(EVENT = if_else(EVENT == 1, 0, 1 (Number of observations: 480)
Samples: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
total post-warmup samples = 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 0.10 0.14 1.00 3767 5984
~LINE (Number of levels: 8)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
sd(Intercept) 0.14 0.05 0.07 0.27 1.00 3864
sd(TreatmentP) 0.08 0.08 0.00 0.27 1.00 3801
cor(Intercept,TreatmentP) -0.24 0.56 -0.98 0.91 1.00 6686
Tail_ESS
sd(Intercept) 4216
sd(TreatmentP) 4370
cor(Intercept,TreatmentP) 5591
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 4.07 0.08 3.92 4.23 1.00 3627 4963
TreatmentP 0.09 0.11 -0.13 0.30 1.00 4401 4844
Sexm -0.33 0.04 -0.40 -0.27 1.00 4801 6136
TreatmentP:Sexm -0.13 0.05 -0.23 -0.03 1.00 4838 5556
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape 5.68 0.26 5.19 6.19 1.00 5846 6682
Samples were drawn 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 (P)", Sexm = 'Sex (M)', `TreatmentP:Sexm` = 'Treatment (P) 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
sessionInfo()R version 4.0.3 (2020-10-10) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Mojave 10.14.6 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_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] knitrhooks_0.0.4 knitr_1.30 kableExtra_1.3.1 ggridges_0.5.3 [5] tidybayes_2.3.1 brms_2.14.4 Rcpp_1.0.5 lme4_1.1-26 [9] Matrix_1.2-18 coxme_2.2-16 bdsmatrix_1.3-4 survival_3.2-7 [13] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 [17] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 [21] tidyverse_1.3.0 workflowr_1.6.2 loaded via a namespace (and not attached): [1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6 [4] igraph_1.2.6 splines_4.0.3 svUnit_1.0.3 [7] crosstalk_1.1.1 rstantools_2.1.1 inline_0.3.17 [10] digest_0.6.27 htmltools_0.5.0 rsconnect_0.8.16 [13] fansi_0.4.1 magrittr_2.0.1 openxlsx_4.2.3 [16] modelr_0.1.8 RcppParallel_5.0.2 matrixStats_0.57.0 [19] xts_0.12.1 prettyunits_1.1.1 colorspace_2.0-0 [22] rvest_0.3.6 ggdist_2.4.0 haven_2.3.1 [25] xfun_0.20 callr_3.5.1 crayon_1.3.4 [28] jsonlite_1.7.2 zoo_1.8-8 glue_1.4.2 [31] survminer_0.4.8 gtable_0.3.0 webshot_0.5.2 [34] V8_3.4.0 distributional_0.2.1 car_3.0-10 [37] pkgbuild_1.2.0 rstan_2.21.2 abind_1.4-5 [40] scales_1.1.1 mvtnorm_1.1-1 DBI_1.1.0 [43] rstatix_0.6.0 miniUI_0.1.1.1 viridisLite_0.3.0 [46] xtable_1.8-4 foreign_0.8-80 km.ci_0.5-2 [49] stats4_4.0.3 StanHeaders_2.21.0-7 DT_0.17 [52] htmlwidgets_1.5.3 httr_1.4.2 threejs_0.3.3 [55] RColorBrewer_1.1-2 arrayhelpers_1.1-0 ellipsis_0.3.1 [58] pkgconfig_2.0.3 loo_2.4.1 farver_2.0.3 [61] dbplyr_2.0.0 labeling_0.4.2 tidyselect_1.1.0 [64] rlang_0.4.10 reshape2_1.4.4 later_1.1.0.1 [67] munsell_0.5.0 cellranger_1.1.0 tools_4.0.3 [70] cli_2.2.0 generics_0.1.0 broom_0.7.3 [73] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1 [76] processx_3.4.5 fs_1.5.0 zip_2.1.1 [79] survMisc_0.5.5 nlme_3.1-149 whisker_0.4 [82] mime_0.9 projpred_2.0.2 xml2_1.3.2 [85] compiler_4.0.3 bayesplot_1.8.0 shinythemes_1.1.2 [88] rstudioapi_0.13 gamm4_0.2-6 curl_4.3 [91] ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.35 [94] stringi_1.5.3 highr_0.8 ps_1.5.0 [97] Brobdingnag_1.2-6 lattice_0.20-41 nloptr_1.2.2.2 [100] markdown_1.1 KMsurv_0.1-5 shinyjs_2.0.0 [103] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0 [106] bridgesampling_1.0-0 insight_0.11.1 data.table_1.13.6 [109] httpuv_1.5.4 R6_2.5.0 promises_1.1.1 [112] rio_0.5.16 gridExtra_2.3 codetools_0.2-16 [115] boot_1.3-25 colourpicker_1.1.0 MASS_7.3-53 [118] gtools_3.8.2 assertthat_0.2.1 rprojroot_2.0.2 [121] withr_2.3.0 shinystan_2.5.0 bayestestR_0.8.0 [124] mgcv_1.8-33 parallel_4.0.3 hms_0.5.3 [127] grid_4.0.3 coda_0.19-4 minqa_1.2.4 [130] rmarkdown_2.6 carData_3.0-4 ggpubr_0.4.0 [133] git2r_0.28.0 shiny_1.5.0 lubridate_1.7.9.2 [136] base64enc_0.1-3 dygraphs_1.1.1.6