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 |
|---|---|---|---|---|
| Rmd | 3fdbcb2 | lukeholman | 2020-11-30 | Tweaks Nov 2020 |
Load packages
library(tidyverse)
library(coxme)
library(lme4)
library(nlme)
library(brms)
library(tidybayes)
library(kableExtra)
library(knitrhooks) # install with devtools::install_github("nathaneastwood/knitrhooks")
output_max_height() # a knitrhook option
options(stringsAsFactors = FALSE)Here we investigated whether there was an effect of treatment of survival time under starvation or desiccation. Single sex triads of flies were housed in vials containing … Vials were monitored every hour/two hours until all flies had died thus all events were observed… Probably some correct terminology here.
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() +
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 
# 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. 
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…
# 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)
print(round(cbind(HazardRatio, HR.se), 3)[-c(1,5), ])
}hazR(weibull.des)HazardRatio HR.se TreatmentP 0.736 0.354 Sexm 2.872 0.866 TreatmentP:Sexm 1.834 0.815
hazR(weibull.sta)HazardRatio HR.se TreatmentP 0.628 0.437 Sexm 3.870 0.878 TreatmentP:Sexm 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 nlme_3.1-149 lme4_1.1-23 [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_3.3.2 [21] 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 whisker_0.4 mime_0.9 [85] projpred_2.0.2 xml2_1.3.2 compiler_4.0.3 [88] bayesplot_1.7.2 shinythemes_1.1.2 rstudioapi_0.11 [91] gamm4_0.2-6 curl_4.3 ggsignif_0.6.0 [94] reprex_0.3.0 statmod_1.4.34 stringi_1.5.3 [97] highr_0.8 ps_1.4.0 Brobdingnag_1.2-6 [100] lattice_0.20-41 nloptr_1.2.2.2 markdown_1.1 [103] KMsurv_0.1-5 shinyjs_2.0.0 vctrs_0.3.4 [106] pillar_1.4.6 lifecycle_0.2.0 bridgesampling_1.0-0 [109] data.table_1.13.0 httpuv_1.5.4 R6_2.4.1 [112] promises_1.1.1 rio_0.5.16 gridExtra_2.3 [115] codetools_0.2-16 boot_1.3-25 colourpicker_1.1.0 [118] MASS_7.3-53 gtools_3.8.2 assertthat_0.2.1 [121] rprojroot_1.3-2 withr_2.3.0 shinystan_2.5.0 [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_2.4 carData_3.0-4 ggpubr_0.4.0 [133] git2r_0.27.1 shiny_1.5.0 lubridate_1.7.9 [136] base64enc_0.1-3 dygraphs_1.1.1.6