resistance
lukeholman
2020-11-30
Last updated: 2020-12-03
Checks: 6 1
Knit directory: exp_evol_respiration/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 287d75b | Martin Garlovsky | 2020-12-03 | MDG commit |
| html | 45751bf | Martin Garlovsky | 2020-12-03 | MDG commit |
| Rmd | c175be4 | Martin Garlovsky | 2020-12-03 | MDG commit |
| html | c175be4 | Martin Garlovsky | 2020-12-03 | MDG commit |
| html | df61dde | Martin Garlovsky | 2020-12-03 | MDG commit |
| Rmd | 0714753 | Martin Garlovsky | 2020-12-03 | workflowr::wflow_git_commit(all = T) |
| Rmd | 3fdbcb2 | lukeholman | 2020-11-30 | Tweaks Nov 2020 |
Load packages
library(tidyverse)
library(coxme)
library(lme4)
library(brms)
library(tidybayes)
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)))
# 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$d, format = "%d/%m/%Y %H") - strptime(start_timeDes, format = "%d/%m/%Y %H"))
des.surv <- Surv(DesRes$survival.time, DesRes$EVENT)
# load starvation resistance data
StaRes <- read.csv("data/3.StarvRes.csv") %>%
# add event (all flies died)
mutate(EVENT = 1,
LINE = paste0(Treatment, substr(Replicate, 2, 2)))
# calculate survival times
# paste time and date
StaRes$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"))
sta.surv <- Surv(StaRes$survival.time, StaRes$EVENT)Inspecting the raw data
bind_rows(
DesRes %>%
select(Treatment, Sex, survival.time) %>% mutate(var = 'Desiccation'),
StaRes %>%
select(Treatment, Sex, survival.time) %>% mutate(var = 'Starvation')
) %>%
ggplot(aes(x = survival.time, y = Sex, fill = Treatment)) +
geom_boxplot() +
scale_fill_brewer(palette = 'Set1', direction = -1, name = "") +
labs(x = 'Survival time (hours)') +
facet_wrap(~var, ncol = 2) +
theme_bw() +
theme(legend.position = 'top') +
NULL
Figure 1: Survival time in hours for flies in each treatment split by sex.
Fit the model for desiccation/starvation resistance
Plot the survival curves and median survival times 
| Version | Author | Date |
|---|---|---|
| df61dde | Martin Garlovsky | 2020-12-03 |
| Version | Author | Date |
|---|---|---|
| df61dde | Martin Garlovsky | 2020-12-03 |
# median eclosion times
survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes)Call: survfit(formula = Surv(survival.time, EVENT) ~ Treatment + Sex,
data = DesRes)
n events median 0.95LCL 0.95UCL
Treatment=M, Sex=f 108 108 38 38 42
Treatment=M, Sex=m 111 111 32 32 34
Treatment=P, Sex=f 114 114 40 38 42
Treatment=P, Sex=m 105 105 32 32 34
survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes)Call: survfit(formula = Surv(survival.time, EVENT) ~ Treatment + Sex,
data = StaRes)
5 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
Treatment=M, Sex=f 118 118 56 52 60
Treatment=M, Sex=m 120 120 40 38 44
Treatment=P, Sex=f 117 117 66 62 68
Treatment=P, Sex=m 120 120 42 40 42
Next we need to check that the ‘proportional hazards’ assumption is not violated before fitting the full model. 
| Version | Author | Date |
|---|---|---|
| df61dde | Martin Garlovsky | 2020-12-03 |
| Version | Author | Date |
|---|---|---|
| df61dde | Martin Garlovsky | 2020-12-03 |
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 and a frailty term to account for replicates within each treatment. We can define the degrees of freedom explicitly
Fit the Accelerated failure time models
weibull.des <- survreg(Surv(survival.time, EVENT) ~ Treatment * Sex + frailty(LINE, df = 6),
data = DesRes, dist = "weibull")
weibull.sta <- survreg(Surv(survival.time, EVENT) ~ Treatment * Sex + frailty(LINE, df = 6),
data = StaRes, dist = "weibull")
bind_rows(anova(weibull.des), anova(weibull.sta)) %>%
cbind(Parameter = c('Null', 'Treatment', 'Sex', '`frailty(LINE)`', 'Treatment x Sex')) %>%
mutate(across(1:5, round, 3)) %>%
select(Parameter, Df, `Resid. Df`, Deviance, `Pr(>Chi)`) %>%
filter(Parameter!='`frailty(LINE)`') %>%
kable() %>%
kable_styling() %>%
kable_styling(full_width = FALSE) %>%
group_rows("Desiccation", 1, 4) %>%
group_rows("Starvation", 5, 8)| Parameter | Df | Resid. Df | Deviance | Pr(>Chi) |
|---|---|---|---|---|
| Desiccation | ||||
| Null | NA | 436.000 | NA | NA |
| Treatment | 1.000 | 435.000 | 3.849 | 0.050 |
| Sex | 1.000 | 434.000 | 154.904 | 0.000 |
| Treatment x Sex | 1.010 | 429.020 | 8.682 | 0.003 |
| Starvation | ||||
| Null | NA | 473.000 | NA | NA |
| Treatment | 1.000 | 472.000 | 2.290 | 0.130 |
| Sex | 1.000 | 471.000 | 208.841 | 0.000 |
| Treatment x Sex | 1.017 | 466.025 | 20.486 | 0.000 |
We see equivocal support for a treatment effect for desiccation resistance and no effect for starvation resistance. For both assays there is support for a sex effect and a treatment x sex interaction.
Calculate hazard ratios
We can use the following equation to translate the AFT coeffiecnts (\(\beta\)) to a hazard ratio (\(\alpha\)): \[ \beta = -\alpha * p \] where \(p\) is the shape (a.k.a. scale) parameter. We can also calculate standard errors… Hazard ratios give the probability of the event occuring compared to ‘control’ in our case compared to Monogamy females. Hazard ratios > 1 indicate increased hazard (i.e. increased probability of event compared to control), hazard ratio < 1 indicate decreased hazard.
# function to get hazard ratios and standard errors
hazR <- function(mod) {
a = c(coefficients(summary(mod)))
coef = (a * -1 * 1/mod$scale)
HazardRatio = exp(coef)
b = summary(mod)$table[, 2]
se = (b * -1 * 1/mod$scale)
HR.se = exp(se)
return(data.frame(round(cbind(HazardRatio, HR.se), 3)[-c(1,5), ]))
}For both desiccation and starvation resistance Polyandrous females live longer than Monogamy females (although not significantly so). Males die sooner than females, and Polyandry males die sooner than Monogamy males.
bind_rows(hazR(weibull.des), hazR(weibull.sta)) %>% as.tibble() %>%
cbind(Parameter = c('Treatment', 'Sex', 'Treatment x Sex')) %>%
select(Parameter, `Hazard ratio` = HazardRatio, `Std. Err.` = HR.se) %>%
kable() %>%
kable_styling() %>%
kable_styling(full_width = FALSE) %>%
group_rows("Desiccation", 1, 3) %>%
group_rows("Starvation", 4, 6)| Parameter | Hazard ratio | Std. Err. |
|---|---|---|
| Desiccation | ||
| Treatment | 0.736 | 0.354 |
| Sex | 2.872 | 0.866 |
| Treatment x Sex | 1.834 | 0.815 |
| Starvation | ||
| Treatment | 0.628 | 0.437 |
| Sex | 3.870 | 0.878 |
| Treatment x Sex | 2.354 | 0.831 |
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 tidybayes_2.3.1 [5] brms_2.14.4 Rcpp_1.0.5 lme4_1.1-23 Matrix_1.2-18 [9] coxme_2.2-16 bdsmatrix_1.3-4 survival_3.2-7 forcats_0.5.0 [13] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 [17] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 loaded via a namespace (and not attached): [1] readxl_1.3.1 backports_1.1.10 workflowr_1.6.2 [4] plyr_1.8.6 igraph_1.2.6 splines_4.0.3 [7] svUnit_1.0.3 crosstalk_1.1.0.1 rstantools_2.1.1 [10] inline_0.3.16 digest_0.6.25 htmltools_0.5.0 [13] rsconnect_0.8.16 fansi_0.4.1 magrittr_2.0.1 [16] openxlsx_4.2.2 modelr_0.1.8 RcppParallel_5.0.2 [19] matrixStats_0.57.0 xts_0.12.1 prettyunits_1.1.1 [22] colorspace_1.4-1 blob_1.2.1 rvest_0.3.6 [25] ggdist_2.3.0 haven_2.3.1 xfun_0.19 [28] callr_3.5.1 crayon_1.3.4 jsonlite_1.7.1 [31] zoo_1.8-8 glue_1.4.2 survminer_0.4.8 [34] gtable_0.3.0 webshot_0.5.2 V8_3.4.0 [37] distributional_0.2.1 car_3.0-10 pkgbuild_1.1.0 [40] rstan_2.21.2 abind_1.4-5 scales_1.1.1 [43] mvtnorm_1.1-1 DBI_1.1.0 rstatix_0.6.0 [46] miniUI_0.1.1.1 viridisLite_0.3.0 xtable_1.8-4 [49] foreign_0.8-80 km.ci_0.5-2 stats4_4.0.3 [52] StanHeaders_2.21.0-6 DT_0.16 htmlwidgets_1.5.2 [55] httr_1.4.2 threejs_0.3.3 RColorBrewer_1.1-2 [58] arrayhelpers_1.1-0 ellipsis_0.3.1 pkgconfig_2.0.3 [61] loo_2.3.1 farver_2.0.3 dbplyr_1.4.4 [64] labeling_0.3 tidyselect_1.1.0 rlang_0.4.8 [67] reshape2_1.4.4 later_1.1.0.1 munsell_0.5.0 [70] cellranger_1.1.0 tools_4.0.3 cli_2.1.0 [73] generics_0.0.2 broom_0.7.1 ggridges_0.5.2 [76] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1 [79] processx_3.4.4 fs_1.5.0 zip_2.1.1 [82] survMisc_0.5.5 nlme_3.1-149 whisker_0.4 [85] mime_0.9 projpred_2.0.2 xml2_1.3.2 [88] compiler_4.0.3 bayesplot_1.7.2 shinythemes_1.1.2 [91] rstudioapi_0.11 gamm4_0.2-6 curl_4.3 [94] ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.34 [97] stringi_1.5.3 highr_0.8 ps_1.4.0 [100] Brobdingnag_1.2-6 lattice_0.20-41 nloptr_1.2.2.2 [103] markdown_1.1 KMsurv_0.1-5 shinyjs_2.0.0 [106] vctrs_0.3.4 pillar_1.4.6 lifecycle_0.2.0 [109] bridgesampling_1.0-0 data.table_1.13.0 httpuv_1.5.4 [112] R6_2.4.1 promises_1.1.1 rio_0.5.16 [115] gridExtra_2.3 codetools_0.2-16 boot_1.3-25 [118] colourpicker_1.1.0 MASS_7.3-53 gtools_3.8.2 [121] assertthat_0.2.1 rprojroot_1.3-2 withr_2.3.0 [124] shinystan_2.5.0 mgcv_1.8-33 parallel_4.0.3 [127] hms_0.5.3 grid_4.0.3 coda_0.19-4 [130] minqa_1.2.4 rmarkdown_2.4 carData_3.0-4 [133] ggpubr_0.4.0 git2r_0.27.1 shiny_1.5.0 [136] lubridate_1.7.9 base64enc_0.1-3 dygraphs_1.1.1.6