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results.qmd
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results.qmd
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---
format:
docx:
reference-doc: "./bib/template.docx"
bibliography: "./bib/bib.ref.bib"
csl: "./bib/behavioral-ecology.csl"
execute:
echo: false
error: false
cache: false
warning: false
crossref:
fig-title: Figure # (default is "Figure")
title-delim: — # (default is ":")
fig-prefix: Fig. # (default is "Figure")
tbl-prefix: Tab. # (default is "Table")
editor_options:
chunk_output_type: console
---
# Results
```{r}
#| label: load_packages
#| output: false
#| warning: false
source("./R/func.R")
pacman::p_load(tidyverse, brms, glmmTMB, gt, latex2exp, posterior, gt, glue, dplyr,magrittr, ggplot2, cowplot, jpeg, magick,rptR,webshot2)
options(digits = 3, scipen = 2)
```
```{r}
#| label: load_data and models
#| output: false
#| warning: false
############################
# Import processed data
#############################
all_data <- read.table("./output/all_data_p.csv",header=T, sep=",")
############################
# Load model
############################
model1 <- readRDS(file = "./output/models/model1.rds")
model1.2 <- readRDS(file = "./output/models/model1.2.rds")
model2 <- readRDS(file = "./output/models/model2.rds")
model2.2 <- readRDS(file = "./output/models/model2.2.rds")
model3<-readRDS(file = "./output/models/model3.rds")
model4 <- readRDS(file = "./output/models/model_4.rds")
model5 <- readRDS(file = "./output/models/model_5.rds")
model6 <- readRDS(file = "./output/models/model_6.rds")
model6_1 <- readRDS(file = "./output/models/model_6_1.rds")
model7 <- readRDS(file = "./output/models/model_7.rds")
model7_2<-readRDS(file = "./output/models/model_7_2.rds")
model8<-readRDS(file = "./output/models/model_8.rds")
model9<-readRDS(file = "./output/models/model_9.rds")
model_T<-readRDS(file = "./output/models/model2_T.rds")
model_13<-readRDS(file = "./output/models/model_13.rds")
model_12<-readRDS(file = "./output/models/model_12.1.rds")
```
```{r}
#| label: Repeatability and behavioral syndromes
#| output: false
#| warning: false
############################
# Parasite count
#############################
### Blackspots distribution
hist(all_data$BS_post_tot)
# Blackspots info
BS_min<-min(all_data$BS_post_tot)
BS_max<-max(all_data$BS_post_tot)
BS_median<-median(all_data$BS_post_tot)
### Cestodes distribution
hist(all_data$P04_alive)
# Cestodes info
cestode_min<-min(all_data$P04_alive)
cestode_max<-max(all_data$P04_alive)
cestode_median<-median(all_data$P04_alive)
mean(all_data$P04_alive)
### Nematodes distribution
hist(all_data$P013_alive)
# Nematodes info
nematode_min<-min(all_data$P013_alive)
nematode_max<-max(all_data$P013_alive)
### Looking at BS gained after infection
# Create new columns in the dataset
all_data$BS_gain<- all_data$BS_post_tot - all_data$BS_pre
# BS gain distribution
hist(all_data$BS_gain)
# BS gain info
gain_min<-min(all_data$BS_gain) ### miscount, some fish have less BS after infection
gain_max<-max(all_data$BS_gain)
gain_median<-median(all_data$BS_gain)
gain_mean<-mean(all_data$BS_gain)
############################
# Repeatability C vs E
############################
#Extract posterior distribution for model 2, looking at repeatability
#post_sd_2 <- as_draws_df(model2, variable = "^sd", regex = TRUE)
#post_sd_C_2 <- post_sd_2[,grepl("C", colnames(post_sd_2))] #ID control
#post_sd_E_2 <- post_sd_2[,grepl("E", colnames(post_sd_2))] #ID experimental
#post_sd_cage_2 <- post_sd_2[,grepl("cage", colnames(post_sd_2))]
# post_sd_sig_2 <- as_draws_df(model2, variable = "^b_sigma", regex = TRUE)
#post_sd_sig_C_2 <- exp(post_sd_sig_2[,grepl("C", colnames(post_sd_sig_2))]) #ID control
# post_sd_sig_E_2 <- exp(post_sd_sig_2[,grepl("E", colnames(post_sd_sig_2))]) #ID experimental
#Extract posterior distribution for model 2, looking at repeatability
post_sd_2 <- as_draws_df(model_T, variable = "^sd", regex = TRUE)
post_sd_C_2 <- post_sd_2[,grepl("C", colnames(post_sd_2))] #ID uninfected
post_sd_E_2 <- post_sd_2[,grepl("E", colnames(post_sd_2))] #ID experimentally infected
post_sd_T_2 <- post_sd_2[,grepl("T", colnames(post_sd_2))] #ID control
post_sd_cage_2 <- post_sd_2[,grepl("cage", colnames(post_sd_2))]
post_sd_sig_2 <- as_draws_df(model_T, variable = "^b_sigma", regex = TRUE)
post_sd_sig_C_2 <- exp(post_sd_sig_2[,grepl("C", colnames(post_sd_sig_2))]) #ID uninfected
post_sd_sig_E_2 <- exp(post_sd_sig_2[,grepl("E", colnames(post_sd_sig_2))]) #ID experimentally infected
post_sd_sig_T_2 <- exp(post_sd_sig_2[,grepl("T", colnames(post_sd_sig_2))]) #ID control
###################################
## CONTROL GROUP & UNINFECTED
###################################
#Boldness for uninfected group
fish_c_b <- post_sd_C_2[,grep("zlogboldness", colnames(post_sd_C_2))]^2
cage_c_b <- post_sd_cage_2[ ,grep("zlogboldness", colnames(post_sd_cage_2))]^2
res_c_b <- post_sd_sig_C_2[,grep("b_sigma_zlogboldness_treatmentC", colnames(post_sd_sig_C_2))]^2
cage_c_b_low <- round(quantile(cage_c_b[,1], c(0.025, 0.975))[1], 3)
cage_c_b_high<- round(quantile(cage_c_b[,1], c(0.025, 0.975))[2], 3)
cage_c_b_mean<-mean(cage_c_b[,1])
fish_c_b_low <- round(quantile(fish_c_b[,1], c(0.025, 0.975))[1], 3)
fish_c_b_high<- round(quantile(fish_c_b[,1], c(0.025, 0.975))[2], 3)
fish_c_b_mean<-mean(fish_c_b[,1])
res_c_b_low <- round(quantile(res_c_b[,1], c(0.025, 0.975))[1], 3)
res_c_b_high <- round(quantile(res_c_b[,1], c(0.025, 0.975))[2], 3)
res_c_b_mean<-mean(res_c_b[,1])
#repeatability
R_C_boldness <- fish_c_b / (fish_c_b + cage_c_b + res_c_b)
hist(R_C_boldness[,1])
R_C_bold <- mean(R_C_boldness[,1])
R_Blb_bold_C <- round(quantile(R_C_boldness[,1], c(0.025, 0.975))[1], 3)
R_Bub_bold_C <- round(quantile(R_C_boldness[,1], c(0.025, 0.975))[2], 3)
#Boldness for control group
fish_t_b <- post_sd_T_2[,grep("zlogboldness", colnames(post_sd_T_2))]^2
cage_t_b <- post_sd_cage_2[ ,grep("zlogboldness", colnames(post_sd_cage_2))]^2
res_t_b <- post_sd_sig_T_2[,grep("b_sigma_zlogboldness_treatmentT", colnames(post_sd_sig_T_2))]^2
#repeatability
R_T_boldness <- fish_t_b / (fish_t_b + cage_t_b + res_t_b)
hist(R_T_boldness[,1])
R_T_bold <- mean(R_T_boldness[,1])
R_Blb_bold_T <- round(quantile(R_T_boldness[,1], c(0.025, 0.975))[1], 3)
R_Bub_bold_T <- round(quantile(R_T_boldness[,1], c(0.025, 0.975))[2], 3)
#exploration for uninfected group
fish_c_ex <- post_sd_C_2[,grep("zexploration", colnames(post_sd_C_2))]^2
cage_c_ex <- post_sd_cage_2[ ,grep("zexploration", colnames(post_sd_cage_2))]^2
res_c_ex <- post_sd_sig_C_2[,grep("b_sigma_zexploration_treatmentC", colnames(post_sd_sig_C_2))]^2
fish_c_ex_low <- round(quantile(fish_c_ex[,1], c(0.025, 0.975))[1], 3)
fish_c_ex_high<- round(quantile(fish_c_ex[,1], c(0.025, 0.975))[2], 3)
fish_c_ex_mean<-mean(fish_c_ex[,1])
res_c_ex_low <- round(quantile(res_c_ex[,1], c(0.025, 0.975))[1], 3)
res_c_ex_high <- round(quantile(res_c_ex[,1], c(0.025, 0.975))[2], 3)
res_c_ex_mean<-mean(res_c_ex[,1])
#repeatability
R_C_exploration <- fish_c_ex / (fish_c_ex + cage_c_ex + res_c_ex)
hist(R_C_exploration[,1])
R_exp_C_mean<-mean(R_C_exploration[,1])
R_Blb_exp_C <- round(quantile(R_C_exploration[,1], c(0.025, 0.975))[1], 3)
R_Bub_exp_C <- round(quantile(R_C_exploration[,1], c(0.025, 0.975))[2], 3)
#Exploration for control group
fish_t_ex <- post_sd_T_2[,grep("zexploration", colnames(post_sd_T_2))]^2
cage_t_ex <- post_sd_cage_2[ ,grep("zexploration", colnames(post_sd_cage_2))]^2
res_t_ex <- post_sd_sig_T_2[,grep("b_sigma_zexploration_treatmentT", colnames(post_sd_sig_T_2))]^2
#repeatability
R_T_exploration <- fish_t_ex / (fish_t_ex + cage_t_ex + res_t_ex)
hist(R_T_exploration[,1])
R_T_exp <- mean(R_T_exploration[,1])
R_Blb_exp_T <- round(quantile(R_T_exploration[,1], c(0.025, 0.975))[1], 3)
R_Bub_exp_T <- round(quantile(R_T_exploration[,1], c(0.025, 0.975))[2], 3)
#activity for uninfected group
fish_c_ac <- post_sd_C_2[,grep("zlogactivity", colnames(post_sd_C_2))]^2
cage_c_ac <- post_sd_cage_2[ ,grep("zlogactivity", colnames(post_sd_cage_2))]^2
res_c_ac <- post_sd_sig_C_2[,grep("b_sigma_zlogactivity_treatmentC", colnames(post_sd_sig_C_2))]^2
fish_c_ac_low <- round(quantile(fish_c_ac[,1], c(0.025, 0.975))[1], 3)
fish_c_ac_high<- round(quantile(fish_c_ac[,1], c(0.025, 0.975))[2], 3)
fish_c_ac_mean<-mean(fish_c_ac[,1])
res_c_ac_low <- round(quantile(res_c_ac[,1], c(0.025, 0.975))[1], 3)
res_c_ac_high <- round(quantile(res_c_ac[,1], c(0.025, 0.975))[2], 3)
res_c_ac_mean<-mean(res_c_ac[,1])
#repeatability
R_C_activity <- fish_c_ac / (fish_c_ac + cage_c_ac + res_c_ac)
hist(R_C_activity[,1])
R_act_C<- mean(R_C_activity[,1])
R_Blb_act_C <- round(quantile(R_C_activity[,1], c(0.025, 0.975))[1], 3)
R_Bub_act_C <- round(quantile(R_C_activity[,1], c(0.025, 0.975))[2], 3)
#Activity for control group
fish_t_act <- post_sd_T_2[,grep("zlogactivity", colnames(post_sd_T_2))]^2
cage_t_act <- post_sd_cage_2[ ,grep("zlogactivity", colnames(post_sd_cage_2))]^2
res_t_act <- post_sd_sig_T_2[,grep("b_sigma_zlogactivity_treatmentT", colnames(post_sd_sig_T_2))]^2
#repeatability
R_T_activity <- fish_t_act / (fish_t_act + cage_t_act + res_t_act)
hist(R_T_activity[,1])
R_T_act <- mean(R_T_activity[,1])
R_Blb_act_T <- round(quantile(R_T_activity[,1], c(0.025, 0.975))[1], 3)
R_Bub_act_T <- round(quantile(R_T_activity[,1], c(0.025, 0.975))[2], 3)
p_value_act_T<-pmcmc(R_T_activity[,1]*100, null = 10, twotail = FALSE) #not significant
############################
# EXPERIMENTAL GROUP
############################
#Boldness for experimental group
fish_e_b <- post_sd_E_2[,grep("zlogboldness", colnames(post_sd_E_2))]^2
cage_e_b <- post_sd_cage_2[ ,grep("zlogboldness", colnames(post_sd_cage_2))]^2
res_e_b <- post_sd_sig_E_2[,grep("b_sigma_zlogboldness_treatmentE", colnames(post_sd_sig_E_2))]^2
cage_e_b_low <- round(quantile(cage_e_b[,1], c(0.025, 0.975))[1], 3)
cage_e_b_high<- round(quantile(cage_e_b[,1], c(0.025, 0.975))[2], 3)
cage_e_b_mean<-mean(cage_e_b[,1])
fish_e_b_low <- round(quantile(fish_e_b[,1], c(0.025, 0.975))[1], 3)
fish_e_b_high<- round(quantile(fish_e_b[,1], c(0.025, 0.975))[2], 3)
fish_e_b_mean<-mean(fish_e_b[,1])
res_e_b_low <- round(quantile(res_e_b[,1], c(0.025, 0.975))[1], 3)
res_e_b_high <- round(quantile(res_e_b[,1], c(0.025, 0.975))[2], 3)
res_e_b_mean<-mean(res_e_b[,1])
#repeatability
R_E_boldness <- fish_e_b / (fish_e_b + cage_e_b + res_e_b)
hist(R_E_boldness[,1])
R_E_bold<-mean(R_E_boldness[,1])
R_Blb_bold_E <- round(quantile(R_E_boldness[,1], c(0.025, 0.975))[1], 3)
R_Bub_bold_E <- round(quantile(R_E_boldness[,1], c(0.025, 0.975))[2], 3)
#exploration for experimental group
fish_e_ex <- post_sd_E_2[,grep("zexploration", colnames(post_sd_E_2))]^2
cage_e_ex <- post_sd_cage_2[ ,grep("zexploration", colnames(post_sd_cage_2))]^2
res_e_ex <- post_sd_sig_E_2[,grep("b_sigma_zexploration_treatmentE", colnames(post_sd_sig_E_2))]^2
fish_e_ex_low <- round(quantile(fish_e_ex[,1], c(0.025, 0.975))[1], 3)
fish_e_ex_high<- round(quantile(fish_e_ex[,1], c(0.025, 0.975))[2], 3)
fish_e_ex_mean<-mean(fish_e_ex[,1])
res_e_ex_low <- round(quantile(res_e_ex[,1], c(0.025, 0.975))[1], 3)
res_e_ex_high <- round(quantile(res_e_ex[,1], c(0.025, 0.975))[2], 3)
res_e_ex_mean<-mean(res_e_ex[,1])
#repeatability
R_E_exploration <- fish_e_ex / (fish_e_ex + cage_e_ex + res_e_ex)
hist(R_E_exploration[,1])
R_exp_E_mean<-mean(R_E_exploration[,1])
R_Blb_exp_E <- round(quantile(R_E_exploration[,1], c(0.025, 0.975))[1], 3)
R_Bub_exp_E <- round(quantile(R_E_exploration[,1], c(0.025, 0.975))[2], 3)
#activity for experimental group
fish_e_ac <- post_sd_E_2[,grep("zlogactivity", colnames(post_sd_E_2))]^2
cage_e_ac <- post_sd_cage_2[ ,grep("zlogactivity", colnames(post_sd_cage_2))]^2
res_e_ac <- post_sd_sig_E_2[,grep("b_sigma_zlogactivity_treatmentE", colnames(post_sd_sig_E_2))]^2
fish_e_ac_low <- round(quantile(fish_e_ac[,1], c(0.025, 0.975))[1], 3)
fish_e_ac_high<- round(quantile(fish_e_ac[,1], c(0.025, 0.975))[2], 3)
fish_e_ac_mean<-mean(fish_e_ac[,1])
res_e_ac_low <- round(quantile(res_e_ac[,1], c(0.025, 0.975))[1], 3)
res_e_ac_high <- round(quantile(res_e_ac[,1], c(0.025, 0.975))[2], 3)
res_e_ac_mean<-mean(res_e_ac[,1])
#repeatability
R_E_activity <- fish_e_ac / (fish_e_ac + cage_e_ac + res_e_ac)
hist(R_E_activity[,1])
R_act_E<-mean(R_E_activity[,1])
R_Blb_act_E <- round(quantile(R_E_activity[,1], c(0.025, 0.975))[1], 3)
R_Bub_act_E <- round(quantile(R_E_activity[,1], c(0.025, 0.975))[2], 3)
#############################
# Overall Repeatability
#############################
#Boldness
R_bold <- overall_repeatability(post_sd_C_2, post_sd_E_2, post_sd_cage_2, post_sd_sig_C_2, post_sd_sig_E_2, trait = "zlogboldness")
R_bold_mean <- mean(R_bold)
R_Blb_bold <- round(quantile(R_bold, c(0.025, 0.975))[1], 3)
R_Bub_bold <- round(quantile(R_bold, c(0.025, 0.975))[2], 3)
#Exploration
R_exp <- overall_repeatability(post_sd_C_2, post_sd_E_2, post_sd_cage_2, post_sd_sig_C_2, post_sd_sig_E_2, trait = "zexploration")
R_exp_mean<-mean(R_exp)
R_Blb_exp <- round(quantile(R_exp, c(0.025, 0.975))[1], 3)
R_Bub_exp <- round(quantile(R_exp, c(0.025, 0.975))[2], 3)
#Activity
R_act <- overall_repeatability(post_sd_C_2, post_sd_E_2, post_sd_cage_2, post_sd_sig_C_2, post_sd_sig_E_2, trait = "zlogactivity")
R_act_mean<-mean(R_act)
R_Blb_act <- round(quantile(R_act, c(0.025, 0.975))[1], 3)
R_Bub_act <- round(quantile(R_act, c(0.025, 0.975))[2], 3)
#################################
# Compare R in E and C treatments
#################################
#Is the repeatability different before and after treatment ?
#Boldness
p_value_bold<-pmcmc((R_C_boldness[,1]) - (R_E_boldness[,1])) # Not different
# That makes sense given:
par(mfrow = c(1,2))
hist(R_E_boldness[,1])
hist(R_C_boldness[,1])
#Exploration
p_value_exp<-pmcmc((R_C_exploration[,1]) - (R_E_exploration[,1])) #not different
# Histograms
hist(R_E_exploration[,1])
hist(R_C_exploration[,1])
#Activity
p_value_act<-pmcmc((R_C_activity[,1]) - (R_E_activity[,1])) #not different
# Histograms
hist(R_E_activity[,1])
hist(R_C_activity[,1])
#Is the repeatability different from 10? #trouver papier avec seuil de valeur
#Boldness control
p_value_bold_C<-pmcmc(R_C_boldness[,1]*100, null = 10, twotail = FALSE)#not significative
#Boldness experimental
p_value_bold_E<-pmcmc(R_E_boldness[,1]*100, null = 10, twotail = FALSE)#not significative
#Average boldness
p_value_bold_a<-pmcmc(R_bold*100, null = 10, twotail = FALSE)#not significative
#Exploration control
p_value_exp_C<-pmcmc(R_C_exploration[,1]*100, null = 10, twotail = FALSE)#significative
hist(R_C_exploration[,1])
hist(R_E_exploration[,1])
#Exploration experimental
p_value_exp_E<-pmcmc(R_E_exploration[,1]*100, null = 10, twotail = FALSE)#not significative
#Average exploration
p_value_exp_a<-pmcmc(R_exp*100, null = 10, twotail = FALSE)#not
#Activity control
p_value_act_C<-pmcmc(R_C_activity[,1]*100, null = 10, twotail = FALSE)##significative
hist(R_C_activity[,1])
#Activity experimental
p_value_act_E<-pmcmc(R_E_activity[,1]*100, null = 10, twotail = FALSE)#significative
#Activity average
p_value_act_a<-pmcmc(R_act*100, null = 10,twotail = FALSE) #significative
#################################
# Behavioural Syndromes
#################################
# Extract correlations from model 2
post_cor <- as_draws_df(model2, variable = "^cor", regex= TRUE)
head(post_cor)
# Extract the C and E groups separately
cor_bold_treatC <- post_cor[,grep("treatmentC__[a-z]*_treatmentC", colnames(post_cor))]
cor_bold_treatE <- post_cor[,grep("treatmentE__[a-z]*_treatmentE", colnames(post_cor))]
# See if syndromes differ in C and E
#Boldness and activity
#Control
bsyn_b_act_C <- data.frame(cor_bold_treatC[,1])
b_act_C_mean<-mean(bsyn_b_act_C[,1])
low_b_act_C<-quantile(bsyn_b_act_C[,1], 0.025)
high_b_act_C<-quantile(bsyn_b_act_C[,1], 0.975)
blb_bul_b_act_C<-quantile(bsyn_b_act_C[,1], c(0.025, 0.975))
#Experimental
bsyn_b_act_E <- data.frame(cor_bold_treatE[,1])
b_act_E_mean<-mean(bsyn_b_act_E[,1])
blb_bul_b_act_E<-quantile(bsyn_b_act_E[,1], c(0.025, 0.975))
low_b_act_E<-quantile(bsyn_b_act_E[,1], 0.025)
high_b_act_E<-quantile(bsyn_b_act_E[,1], 0.975)
#is it significant
pmcmc_b_act_E<-pmcmc(bsyn_b_act_E[,1], twotail = TRUE)
pmcmc_b_act_C<-pmcmc(bsyn_b_act_C[,1], twotail = TRUE)#control significant
#is the difference significant
pmcmc_b_act<-pmcmc(bsyn_b_act_C[,1] - bsyn_b_act_E[,1], twotail = TRUE)
#Boldness and exploration
#Control
bsyn_b_exp_C <- data.frame(cor_bold_treatC[,2])
b_exp_C_mean<-mean(bsyn_b_exp_C[,1])
blb_bul_b_exp_C<-quantile(bsyn_b_exp_C[,1], c(0.025, 0.975))
low_b_exp_C<-quantile(bsyn_b_exp_C[,1], 0.025)
high_b_exp_C<-quantile(bsyn_b_exp_C[,1], 0.975)
#Experimental
bsyn_b_exp_E <- data.frame(cor_bold_treatE[,2])
b_exp_E_mean<-mean(bsyn_b_exp_E[,1])
blb_bul_b_exp_E<-quantile(bsyn_b_exp_E[,1], c(0.025, 0.975))
low_b_exp_E<-quantile(bsyn_b_exp_E[,1], 0.025)
high_b_exp_E<-quantile(bsyn_b_exp_E[,1], 0.975)
#is it significant
pmcmc_b_exp_C<-pmcmc(bsyn_b_exp_C[,1], twotail = TRUE) #no
pmcmc_b_exp_E<-pmcmc(bsyn_b_exp_E[,1], twotail = TRUE)# no
#is the difference significant
pmcmc_b_exp<-pmcmc(bsyn_b_exp_C[,1] - bsyn_b_exp_E[,1], twotail = TRUE)
#Activity and exploration
#Control
bsyn_act_exp_C <- data.frame(cor_bold_treatC[,3])
act_exp_C_mean<-mean(bsyn_act_exp_C[,1])
blb_bul_act_exp_C<-quantile(bsyn_act_exp_C[,1], c(0.025, 0.975))
low_act_exp_C<-quantile(bsyn_act_exp_C[,1], 0.025)
high_act_exp_C<-quantile(bsyn_act_exp_C[,1], 0.975)
#Experimental
bsyn_act_exp_E <- data.frame(cor_bold_treatE[,3])
act_exp_E_mean<-mean(bsyn_act_exp_E[,1])
blb_bul_act_exp_E<-quantile(bsyn_act_exp_E[,1], c(0.025, 0.975))
low_act_exp_E<-quantile(bsyn_act_exp_E[,1], 0.025)
high_act_exp_E<-quantile(bsyn_act_exp_E[,1], 0.975)
#is it significant
pmcmc_act_exp_E<- pmcmc(bsyn_act_exp_E[,1], twotail = TRUE) #not significant
pmcmc_act_exp_C<- pmcmc(bsyn_act_exp_C[,1], twotail = TRUE) #significant
#is the difference significant
pmcmc_act_exp<-pmcmc(bsyn_act_exp_E[,1] - bsyn_act_exp_C[,1], twotail = TRUE)#yes
# Overall syndrome
# Take average of the two treatments
bsyn_b_act <- cbind(bsyn_b_act_C[,1], bsyn_b_act_E[,1]); mean(rowMeans(bsyn_b_act)); quantile(rowMeans(bsyn_b_act), c(0.025, 0.975))
# Pool the two MCMC chains from the treatments into a giant vector 32,000 rows (2*16,000)
#Boldness and activity
bsyn_b_act2 <- rbind(c(bsyn_b_act_C[,1], bsyn_b_act_E[,1]))
b_act_o<-mean(bsyn_b_act2)
q_b_act<-quantile(bsyn_b_act2, c(0.025, 0.975))
low_b_act_o<-quantile(bsyn_b_act2, 0.025)
high_b_act_o<-quantile(bsyn_b_act2, 0.975)
#Boldness and exploration
bsyn_b_exp <- rbind(c(bsyn_b_exp_C[,1], bsyn_b_exp_E[,1]))
b_exp_o<-mean(bsyn_b_exp)
q_b_exp<-quantile(bsyn_b_exp, c(0.025, 0.975))
low_b_exp_o<-quantile(bsyn_b_exp, 0.025)
high_b_exp_o<-quantile(bsyn_b_exp, 0.975)
#Activity and exploration
bsyn_act_exp <- rbind(c(bsyn_act_exp_C[,1], bsyn_act_exp_E[,1]))
act_exp_o<-mean(bsyn_act_exp)
q_act_exp<-quantile(bsyn_act_exp, c(0.025, 0.975))
low_act_exp_o<-quantile(bsyn_act_exp, 0.025)
high_act_exp_o<-quantile(bsyn_act_exp, 0.975)
#Values in model 1.2 for logactivity-boldness
#estimate = -0.48
#CI = -0.93, 0.18 ##pool is closest to model 1.2
```
### Methods
#### *Statistical analysis*
All statistical analyses were performed in R (vers. `r paste0(R.Version()$major, ".", R.Version()$minor)`). Adjusted fish mass was calculated as the fish mass minus parasite mass [@Lagrue_2015-wg]. Parasite mass was estimated with the mean mass of 20 parasites (Vic, in prep). For bass tapeworm, adult average mass was 0.003g and the larval form was 0.0008g. We decided to exclude nematodes since the mass varied greatly between individuals, and was rarely found in our fish sample. Black spot mass was considered too small to be subtracted from total mass ($10^{-7}g$). Body condition was calculated with the Fulton index ($mass/length^3 (cm)$) [@Jakob_1996-wg]. We had 4 measures of body condition, one for each trial. We used the adjusted fish mass for the 2 measures (trial 3 and 4) of body condition after the experimental infection (we assume there is no parasites before the experimental infection). We calculated parasite density by dividing the total number of parasite (black spots post-infection and adult cestodes alive and larvae form) with the adjusted fish mass before sacrifice.
Exploration was visually normally distributed while activity and shyness-boldness were log-transformed to better approximate normality. All variable were z-scaled (i.e., $\left(x - \bar{x} \right) / \sigma$) before fitting the models to ease model interpretation and improve parameter estimation. We fitted our models within a Bayesian statistical framework using the *brms* [vers. `r utils::packageVersion("brms")` [@Burkner2017-wg] package and *rstan* [vers. `r utils::packageVersion("rstan")`] [@noauthor_2021-az]. We ran four MCMC chains each with 6000 iterations, sampling every iteration. We discarded the first 2000 iterations as a warm up. We visually inspected chains to ensure that they were mixing well and that chains had converged (i.e., Rhat < 1.02), and ensured that we had an effective sample size >1000 for all parameters. Statistical significance of estimates from the models were inferred from whether or not confidence intervals included zero.
To test if we had evidence of personality (i.e. traits were repeatable) and if they formed behavioural syndromes (i.e. correlations between traits), we built a multi-responses model (Number of observations: 288) with each of the three traits as response variables. Treatments (uninfected; infected; control) were included as fixed effects for each trait because we were interested in whether infection changed fish behaviour. Treatment "uninfected" (n = 72) is before the experimental infection, including all fish; treatment "infected" (n = 60) is after the experimental infection, i.e. individuals that went into cages in the lake to be infected; and "control" (n = 12) is 2 groups that stayed in the lab and lived under the same conditions, except individuals didn't go into a cage. Fish identity and cage number (n = 12, 10 cages + 2 control groups) were included as random effects. Model 1 had tank effect as a fixed factor compared to Model 2. We compared the models using the *loo* package [vers. `r utils::packageVersion("loo")`]. Model 2 was better fitted than model 1 and we decided not to include tank effect in our models.
Repeatability (R) was calculated as:
$$
R = \frac{\sigma^{2}_{ID}}{\sigma^{2}_{ID} + \sigma^{2}_{cage} + exp(\sigma_{R})^{2}}
$$ {#eq-eq1}
from @eq-eq1, $\sigma^{2}_{ID}$ is the standard deviation for fish identity, $\sigma^{2}_{cage}$ the standard deviation for cage number and $exp(\sigma_{R})^{2}$ is the residual variance distributed for each treatments. We also calculated repeatability across treatments by pooling the posterior distribution of $exp(\sigma_{R})^{2}$ and $\sigma^{2}_{ID}$. To estimate if repeatability was different from 0, we have chosen 0.10 as a threshold since values can't be of 0 in the distribution (i.e. R will always be significantly different than 0) and we expected R to be higher than 10% for traits to be minimally repeatable. Correlation between traits were extracted from the posterior distribution of the same model but excluded control fish to simplify correlation estimations. We wanted to determine if there was a behavioural syndrome between traits in the uninfected and infected state. To measure the average correlation for each trait, we pooled together the two MCMC chains for the uninfected and infected group.
To test random slopes, we built model 3 the same way as model 2, but we estimated the correlation of change between the slopes of each trait. With this model, we could see how individuals’ changes in one trait correlated with the change in other traits.
To test if the experimental infection influenced personality, we sublet the data set according to the treatment (uninfected: n = 144 observations; infected: n = 120 observations). We built Model 4 to see if parasite density had an effect on the traits. Parasite density (post-infection black spot and alive cestodes together) was included as a fixed factor, fish identity and cage number as random factors. Model 4 assumed than parasite density effect is linear, so we included parasite density squared as a fixed factor in model 4.1. Our sample was too small to estimate model 4.1.
To test if body condition influenced personality, we fitted model 5 with body condition as a fixed factor, fish identity and cage number as random effects for our uninfected group. Model 6 was fitted with parasite density and body condition for our infected group. Finally, to see if the parasite species had different effects on personality, we built model 7 with cestode density, black spot density (post-infection) and body condition as fixed factors; fish identity and cage number as random factors. We suspected an interaction between body condition and parasite density and so we fitted a new model to test this. We also wanted to see if there was an interaction between the two species of parasites and we built a new model with that interaction. Both interactions were not significant and so we decided not to include them in our final models.
### Results
#### *Experimental infection*
The caging experiment successfully infected our fish (@fig-fig1). Control fish (i.e., that stayed in the laboratory) had no living parasites, which indicated that the praziquantel treatment was effective. The two most abundant species found in the experimentally infected fish were trematodes causing the black spot disease (Trematoda: *Apophallus sp.* and *Uvulifer sp.*; min-max: `r BS_min` - `r BS_max`; median: `r BS_median`) and the bass tapeworm (Cestoda: *Proteocephalus ambloplites*; min-max: `r cestode_min` - `r cestode_max` ; median: `r cestode_median`). The most abundant species of trematode causing black spots was *Apophallus sp*., but *Uvulifer sp.* was found more frequently inside the muscles (MG, personal observations). Experimental fish gained, on average, `r gain_mean` black spots across the different cages, and were found on the fins, body, gills and inside the muscles. Bass tapeworms were mostly found in the liver, stomach and digestive tract, and occasionally around the spleen (parasite count: 9), and rarely on the gills (parasite count: 3) or the heart (parasite count: 2). Other unknown nematode species were found rarely in the body cavity (alive parasite count: 4).
```{r}
#| label: fig-fig1
#| fig-cap: Mean black spot (A) and bass tapeworm (B) density per cage (n=10) according to geographic location of cage sites on Lake Cromwell, Station de Biologie des Laurentides, Québec, Canada. Map coordonates are expressed in decimal degrees. Black square is the water entry point.
#| out-width: 100%
#| fig-width: 8
#| fig-height: 8
fig1 <- image_read("./output/figures/map.png")
fig1
```
#### *Evidence of personality and behavioural syndromes*
With a threshold of 10%, only activity was repeatable across trials regardless of the infection status (Average: R = `r R_act_mean`, 95% CI = `r R_Blb_act`, `r R_Bub_act`, $pMCMC_{\alpha = 0.10}$ = `r p_value_act_a`). Shyness was not significantly repeatable in both treatment (Average: R = `r R_bold_mean`, 95% CI = `r R_Blb_bold`, `r R_Bub_bold`, $pMCMC_{\alpha = 0.10}$ = `r p_value_bold_a`). Exploration was repeatable in the uninfected state (R = `r R_exp_C_mean`, 95% CI = `r R_Blb_exp_C`, `r R_Bub_exp_C`, $pMCMC_{\alpha = 0.10}$ = `r p_value_exp_C`;), but not in the experimentally infected state (R = `r R_exp_E_mean`, 95% CI = `r R_Blb_exp_E`, `r R_Bub_exp_E`, $pMCMC_{\alpha = 0.10}$ = `r p_value_exp_E`). We did not observe among-individual variation to be higher in the uninfected vs experimentally infected state for all traits. Repeatability was not significatly different between treatment for shyness ($pMCMC_{\alpha = 0.05}$ = `r p_value_bold`), exploration ($pMCMC_{\alpha = 0.05}$ = `r p_value_exp`) and activity ($pMCMC_{\alpha = 0.05}$ = `r p_value_act`). Repeatability for control fish is uncertain because our sample size is small and confidence intervals are large. Shyness (R = `r R_T_bold`, 95% CI = `r R_Blb_bold_T`, `r R_Bub_bold_T`, $pMCMC_{\alpha = 0.10}$ = `r p_value_bold_T`), exploration (R = `r R_T_exp`, 95% CI = `r R_Blb_exp_T`, `r R_Bub_exp_T`, $pMCMC_{\alpha = 0.10}$ = `r p_value_exp_T`), and activity (R = `r R_T_act`, 95% CI = `r R_Blb_act_T`, `r R_Bub_act_T`, $pMCMC_{\alpha = 0.10}$ = `r p_value_act_T`) were not repeatable over the last two trials for control fish.
We found evidence of behavioural syndromes between some traits depending on the infection status (@fig-fig2). Activity and shyness were negatively and significantly correlated in the uninfected state (cor = `r b_act_C_mean`,95% CI = `r blb_bul_b_act_C`, $pMCMC_{\alpha = 0.05}$ = `r pmcmc_b_act_C`), but not when fish were experimentally infected (cor = `r b_act_E_mean`,95% CI = `r blb_bul_b_act_E`, $pMCMC_{\alpha = 0.05}$ = `r pmcmc_b_act_E`). Activity and exploration were positively and significantly correlated in the uninfected state as well (cor = `r act_exp_C_mean`,95% CI = `r blb_bul_act_exp_C`, $pMCMC_{\alpha = 0.05}$ = `r pmcmc_act_exp_C`), but not after the experimental infection (cor = `r act_exp_E_mean`,95% CI = `r blb_bul_act_exp_E`, $pMCMC_{\alpha = 0.05}$ = `r pmcmc_act_exp_E`). Shyness and exploration were not significantly correlated in both treatment (cor = `r mean(bsyn_b_exp)`,95% CI = `r q_b_exp`). Correlations were not significantly different between the uninfected and experimentally infected status for these syndromes (Shyness-Activity: $pMCMC_{\alpha = 0.05}$ = `r pmcmc_b_act`; Shyness-Exploration: $pMCMC_{\alpha = 0.05}$ = `r pmcmc_b_exp`; Activity-Exploration: $pMCMC_{\alpha = 0.05}$ = `r pmcmc_act_exp`).
```{r}
#| label: diff_infected_uninfected
#| output: false
#| warning: false
#Create dataframe with estimates and CI
#Extract post sd from model 2 for each traits and treatment
int_b <- as_draws_df(model2, variable = "^b", regex = TRUE)
#intercept for control
int_b_c<-int_b[,grep("zlogboldness_Intercept", colnames(int_b))]
int_exp_c<-int_b[,grep("zexploration_Intercept", colnames(int_b))]
int_act_c<-int_b[,grep("zlogactivity_Intercept", colnames(int_b))]
#mean estimates for control
int_b_c_num<- as.numeric(unlist(int_b_c))
mean_int_b_c<-mean(int_b_c_num)
int_exp_c_num<- as.numeric(unlist(int_exp_c))
mean_int_exp_c<-mean(int_exp_c_num)
int_act_c_num<- as.numeric(unlist(int_act_c))
mean_int_act_c<-mean(int_act_c_num)
#contrast between intercept C and E
int_b_con<-int_b[,grep("b_zlogboldness_treatmentE", colnames(int_b))]
int_exp_con<-int_b[,grep("b_zexploration_treatmentE", colnames(int_b))]
int_act_con<-int_b[,grep("b_zlogactivity_treatmentE", colnames(int_b))]
#mean estimates for contrast
int_b_con_num<- as.numeric(unlist(int_b_con))
mean_con_b<-mean(int_b_con_num)
blb_b_con <- round(quantile(int_b_con_num, c(0.025, 0.975))[1], 3)
bub_b_con <- round(quantile(int_b_con_num, c(0.025, 0.975))[2], 3)
int_exp_con_num<- as.numeric(unlist(int_exp_con))
mean_con_exp<-mean(int_exp_con_num)
blb_exp_con <- round(quantile(mean_con_exp, c(0.025, 0.975))[1], 3)
bub_exp_con <- round(quantile(mean_con_exp, c(0.025, 0.975))[2], 3)
int_act_con_num<- as.numeric(unlist(int_act_con))
mean_con_act<-mean(int_act_con_num)
blb_act_con <- round(quantile(mean_con_act, c(0.025, 0.975))[1], 3)
bub_act_con <- round(quantile(mean_con_act, c(0.025, 0.975))[2], 3)
#subtract intercept for C - contrast to get intercept for E
int_b_e<-(int_b_c_num + int_b_con_num)
int_b_e_num<- as.numeric(unlist(int_b_e))
mean_int_b_e<-mean(int_b_e_num)
int_exp_e<-(int_exp_con_num + int_exp_c_num)
int_exp_e_num<- as.numeric(unlist(int_exp_e))
mean_int_exp_e<-mean(int_exp_e_num)
int_act_e<-(int_act_con_num + int_act_c_num)
int_act_e_num<- as.numeric(unlist(int_act_e))
mean_int_act_e<-mean(int_act_e_num)
#is E and C significantly different for each trait?
pm_b<-pmcmc(int_b_c_num - int_b_e_num, twotail = TRUE) #significantly different
pm_exp<-pmcmc(int_exp_c_num - int_exp_e_num, twotail = TRUE) #significantly different
pm_act<-pmcmc(int_act_c_num - int_act_e_num, twotail = TRUE) #not significant
#95% IC
#Boldness
#Control
blb_b_c <- round(quantile(int_b_c_num, c(0.025, 0.975))[1], 3)
bub_b_c <- round(quantile(int_b_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_b_e<- round(quantile(int_b_e, c(0.025, 0.975))[1], 3)
bub_b_e<- round(quantile(int_b_e, c(0.025, 0.975))[2], 3)
#Exploration
#Control
blb_exp_c <- round(quantile(int_exp_c_num, c(0.025, 0.975))[1], 3)
bub_exp_c <- round(quantile(int_exp_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_exp_e <- round(quantile(int_exp_e, c(0.025, 0.975))[1], 3)
bub_exp_e <- round(quantile(int_exp_e, c(0.025, 0.975))[2], 3)
#Activity
#Control
blb_act_c <- round(quantile(int_act_c_num, c(0.025, 0.975))[1], 3)
bub_act_c <- round(quantile(int_act_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_act_e <- round(quantile(int_act_e, c(0.025, 0.975))[1], 3)
bub_act_e <- round(quantile(int_act_e, c(0.025, 0.975))[2], 3)
#Extract post sd from model 2 for each traits and treatment
int_b <- as_draws_df(model_T, variable = "^b", regex = TRUE)
#intercept for uninfected
int_b_c<-int_b[,grep("zlogboldness_Intercept", colnames(int_b))]
int_exp_c<-int_b[,grep("zexploration_Intercept", colnames(int_b))]
int_act_c<-int_b[,grep("zlogactivity_Intercept", colnames(int_b))]
#mean estimates for uninfected
#boldness
int_b_c_num<- as.numeric(unlist(int_b_c))
mean_int_b_c<-mean(int_b_c_num)
#exploration
int_exp_c_num<- as.numeric(unlist(int_exp_c))
mean_int_exp_c<-mean(int_exp_c_num)
#activity
int_act_c_num<- as.numeric(unlist(int_act_c))
mean_int_act_c<-mean(int_act_c_num)
#contrast between intercept C and E
int_b_con<-int_b[,grep("b_zlogboldness_treatmentE", colnames(int_b))]
int_exp_con<-int_b[,grep("b_zexploration_treatmentE", colnames(int_b))]
int_act_con<-int_b[,grep("b_zlogactivity_treatmentE", colnames(int_b))]
#contrast between intercept C and T
int_b_con_T<-int_b[,grep("b_zlogboldness_treatmentT", colnames(int_b))]
int_exp_con_T<-int_b[,grep("b_zexploration_treatmentT", colnames(int_b))]
int_act_con_T<-int_b[,grep("b_zlogactivity_treatmentT", colnames(int_b))]
#mean estimates for contrast C-E
#boldness
int_b_con_num<- as.numeric(unlist(int_b_con))
mean_con_b<-mean(int_b_con_num)
blb_b_con <- round(quantile(int_b_con_num, c(0.025, 0.975))[1], 3)
bub_b_con <- round(quantile(int_b_con_num, c(0.025, 0.975))[2], 3)
#exploration
int_exp_con_num<- as.numeric(unlist(int_exp_con))
mean_con_exp<-mean(int_exp_con_num)
blb_exp_con <- round(quantile(int_exp_con_num, c(0.025, 0.975))[1], 3)
bub_exp_con <- round(quantile(int_exp_con_num, c(0.025, 0.975))[2], 3)
#activity
int_act_con_num<- as.numeric(unlist(int_act_con))
mean_con_act<-mean(int_act_con_num)
blb_act_con <- round(quantile(int_act_con_num, c(0.025, 0.975))[1], 3)
bub_act_con <- round(quantile(int_act_con_num, c(0.025, 0.975))[2], 3)
#mean estimates for contrast C-T
#boldness
int_b_con_num_T<- as.numeric(unlist(int_b_con_T))
mean_con_b_T<-mean(int_b_con_num_T)
blb_b_con_T <- round(quantile(int_b_con_num_T, c(0.025, 0.975))[1], 3)
bub_b_con_T <- round(quantile(int_b_con_num_T, c(0.025, 0.975))[2], 3)
#exploration
int_exp_con_num_T<- as.numeric(unlist(int_exp_con_T))
mean_con_exp_T<-mean(int_exp_con_num_T)
blb_exp_con_T <- round(quantile(int_exp_con_num_T, c(0.025, 0.975))[1], 3)
bub_exp_con_T <- round(quantile(int_exp_con_num_T, c(0.025, 0.975))[2], 3)
#activity
int_act_con_num_T<- as.numeric(unlist(int_act_con_T))
mean_con_act_T<-mean(int_act_con_num_T)
blb_act_con_T <- round(quantile(int_act_con_num_T, c(0.025, 0.975))[1], 3)
bub_act_con_T <- round(quantile(int_act_con_num_T, c(0.025, 0.975))[2], 3)
#subtract intercept for C - contrast E to get intercept for E
#boldness
int_b_e<-(int_b_c_num + int_b_con_num)
int_b_e_num<- as.numeric(unlist(int_b_e))
mean_int_b_e<-mean(int_b_e_num)
#exploration
int_exp_e<-(int_exp_con_num + int_exp_c_num)
int_exp_e_num<- as.numeric(unlist(int_exp_e))
mean_int_exp_e<-mean(int_exp_e_num)
#activity
int_act_e<-(int_act_con_num + int_act_c_num)
int_act_e_num<- as.numeric(unlist(int_act_e))
mean_int_act_e<-mean(int_act_e_num)
#subtract intercept for C - contrast T to get intercept for T
#boldness
int_b_e_T<-(int_b_c_num + int_b_con_num_T)
int_b_e_num_T<- as.numeric(unlist(int_b_e_T))
mean_int_b_e_T<-mean(int_b_e_num_T)
#exploration
int_exp_e_T<-(int_exp_con_num_T + int_exp_c_num)
int_exp_e_num_T<- as.numeric(unlist(int_exp_e_T))
mean_int_exp_e_T<-mean(int_exp_e_num_T)
#activity
int_act_e_T<-(int_act_con_num_T + int_act_c_num)
int_act_e_num_T<- as.numeric(unlist(int_act_e_T))
mean_int_act_e_T<-mean(int_act_e_num_T)
#is E and C significantly different for each trait?
pm_b<-pmcmc(int_b_c_num - int_b_e_num, twotail = TRUE) #significantly different
pm_exp<-pmcmc(int_exp_c_num - int_exp_e_num, twotail = TRUE) #significantly different
pm_act<-pmcmc(int_act_c_num - int_act_e_num, twotail = TRUE) #not significant
pm_b_T<-pmcmc(int_b_c_num - int_b_e_num_T, twotail = TRUE)
pm_exp_T<-pmcmc(int_exp_c_num - int_exp_e_num_T, twotail = TRUE)
pm_act_T<-pmcmc(int_act_c_num - int_act_e_num_T, twotail = TRUE)
#95% IC
#Boldness
#Control
blb_b_c <- round(quantile(int_b_c_num, c(0.025, 0.975))[1], 3)
bub_b_c <- round(quantile(int_b_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_b_e<- round(quantile(int_b_e, c(0.025, 0.975))[1], 3)
bub_b_e<- round(quantile(int_b_e, c(0.025, 0.975))[2], 3)
#Exploration
#Control
blb_exp_c <- round(quantile(int_exp_c_num, c(0.025, 0.975))[1], 3)
bub_exp_c <- round(quantile(int_exp_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_exp_e <- round(quantile(int_exp_e, c(0.025, 0.975))[1], 3)
bub_exp_e <- round(quantile(int_exp_e, c(0.025, 0.975))[2], 3)
#Activity
#Control
blb_act_c <- round(quantile(int_act_c_num, c(0.025, 0.975))[1], 3)
bub_act_c <- round(quantile(int_act_c_num, c(0.025, 0.975))[2], 3)
#Experimental
blb_act_e <- round(quantile(int_act_e, c(0.025, 0.975))[1], 3)
bub_act_e <- round(quantile(int_act_e, c(0.025, 0.975))[2], 3)
```
#### *Shyness and exploration responses differ between uninfected and infected state*
We compared mean responses for each trait between the uninfected, infected and control group. Mean shyness and exploration responses were significantly different between the uninfected and experimentally infected state, but not for activity (@fig-fig2). For shyness, we observed a significant decrease in the mean response (Contrast = `r mean_con_b`, 95% CI = `r blb_b_con`, `r bub_b_con`, $pMCMC_{\alpha = 0.05}$ = `r pm_b`), meaning that fish tended to get bolder after the experimental infection. Fish were also significantly more exploratory in the infected state (Contrast = `r mean_con_exp`, 95% CI = `r blb_exp_con`, `r bub_exp_con`, $pMCMC_{\alpha = 0.05}$ = `r pm_exp`). Fish tended to decrease their activity when they were experimentally infected compared to their uninfected state (Contrast = `r mean_con_act`, 95% CI = `r blb_act_con`, `r bub_act_con`, $pMCMC_{\alpha = 0.05}$ = `r pm_act`). Control fish (for trial 3-4) showed the same pattern: mean shyness and exploration responses were significantly different than the uninfected state, but we saw no difference in activity. For shyness, we observed a significant decrease in the mean response (Contrast = `r mean_con_b_T`, 95% CI = `r blb_b_con_T`, `r bub_b_con_T`, $pMCMC_{\alpha = 0.05}$ = `r pm_b_T`), meaning that fish tended to get bolder over time just by staying in the lab. Control fish became also significantly more exploratory in the lab (Contrast = `r mean_con_exp_T`, 95% CI = `r blb_exp_con_T`, `r bub_exp_con_T`, $pMCMC_{\alpha = 0.05}$ = `r pm_exp_T`). Activity did not change over time for control fish (Contrast = `r mean_con_act_T`, 95% CI = `r blb_act_con_T`, `r bub_act_con_T`, $pMCMC_{\alpha = 0.05}$ = `r pm_act_T`).
```{r}
#| label: fig-fig2
#| fig-cap: Contrast in the mean responses and the 95% confidence intervals of shyness (A), exploration (B) and activity (C) of pumpkinseed sunfish (Lepomis gibbosus) between the uninfected (all fish, trial 1-2, n =72), experimentally infected group (trial 3-4, caging experiment n = 60) and control fish (trial 3-4, stayed in the lab, n = 12). All variables are z-scaled.
#| out-width: 100%
#| fig-width: 8
#| fig-height: 5
######################
#Model T contrats + raw units
######################
all_data_T <- read.table("./output/all_data_p_T.csv",header=T, sep=",")
int_w <- as_draws_df(model_T, variable = "^b", regex = TRUE)
bold_int<-int_w[,grep("zlogboldness_Intercept", colnames(int_w))]
exp_int<-int_w[,grep("zexploration_Intercept", colnames(int_w))]
act_int<-int_w[,grep("zlogactivity_Intercept", colnames(int_w))]
bold_T<-int_w[,grep("b_zlogboldness_treatmentT", colnames(int_w))]
exp_T<-int_w[,grep("b_zexploration_treatmentT", colnames(int_w))]
act_T<-int_w[,grep("b_zlogactivity_treatmentT", colnames(int_w))]
bold_E<-int_w[,grep("b_zlogboldness_treatmentE", colnames(int_w))]
exp_E<-int_w[,grep("b_zexploration_treatmentE", colnames(int_w))]
act_E<-int_w[,grep("b_zlogactivity_treatmentE", colnames(int_w))]
######################
#EXPLORATION
######################
#EXPLORATION FOR UNINFECTED (INTERCEPT). MARYANE: THIS ISN'T CORRECT BECAUSE YOU'RE DOING THIS WITH THE POSTERIOR DISTRIBUTION. YOU NEED TO USE THE SUMMARY (MEAN AND SD FOR Z FROM THE RAW DATA)
#intercept from model
int_exp_c_num<- as.numeric(unlist(exp_int))
# First, get the mean and sd of the un-transformed raw data that was used to scale. Remember, you used "scale(exploration, center = TRUE, sd = TRUE)", so you used the SD and mean from the raw data to z-transform. See line 226 of
mean_exploration <- mean(all_data_T$exploration)
sd_exploration <- sd(all_data_T$exploration)
# Now, exp_int is the mean of the z-transformed variable. So, we need to use the raw data mean and sd to convert back to raw scale
raw_int_exp_c<-(int_exp_c_num * sd_exploration) + mean_exploration
range(raw_int_exp_c) # Seems sensible
raw_exp_c<- as.numeric(unlist(raw_int_exp_c))
raw_exp_c_mean<-mean(raw_exp_c)
#EXPLORATION FOR CONTRAST (TREATMENT T)
int_exp_con_T_num<- as.numeric(unlist(exp_T))
#EXPLORATION FOR CONTRAST (TREATMENT E)
int_exp_con_E_num<- as.numeric(unlist(exp_E))
#EXPLORATION FOR E
int_exp_E<-(int_exp_con_E_num + int_exp_c_num)
int_exp_E_num<- as.numeric(unlist(int_exp_E))
#raw units for exploration E
raw_int_exp_E<-(int_exp_E_num * sd_exploration) + mean_exploration
range(raw_int_exp_E)
raw_exp_E<- as.numeric(unlist(raw_int_exp_E))
raw_exp_E_mean<-mean(raw_exp_E)
#EXPLORATION FOR T
int_exp_T<-(int_exp_con_num + int_exp_c_num)
int_exp_T_num<- as.numeric(unlist(int_exp_T))
#Contrast I - T
int_exp_E_T<-(int_exp_T_num - int_exp_E_num)
raw_int_exp_E_T<-(int_exp_E_T * sd_exploration) + mean_exploration
range(raw_int_exp_E_T)
raw_exp_E_T<- as.numeric(unlist(raw_int_exp_E_T))
raw_exp_E_T_mean<-mean(raw_int_exp_E_T)
blb_exp_E_T <- round(quantile(raw_int_exp_E_T, c(0.025, 0.975))[1], 3)
bub_exp_E_T <- round(quantile(raw_int_exp_E_T, c(0.025, 0.975))[2], 3)
#raw units
raw_int_exp_T<-(int_exp_T_num * sd_exploration) + mean_exploration
range(raw_int_exp_T)
raw_exp_T<- as.numeric(unlist(raw_int_exp_T))
raw_exp_T_mean<-mean(raw_exp_T)
#pmcmc test
pm_exp_T<-pmcmc(int_exp_c_num - int_exp_T_num,twotail = TRUE) #significantly different
#Exploration confidence intervals
#Control
blb_exp_T <- round(quantile(raw_exp_T, c(0.025, 0.975))[1], 3)
bub_exp_T <- round(quantile(raw_exp_T, c(0.025, 0.975))[2], 3)
#infected
blb_exp_E <- round(quantile(raw_exp_E, c(0.025, 0.975))[1], 3)
bub_exp_E <- round(quantile(raw_exp_E, c(0.025, 0.975))[2], 3)
#uninfected
blb_exp_c <- round(quantile(raw_exp_c, c(0.025, 0.975))[1], 3)
bub_exp_c <- round(quantile(raw_exp_c, c(0.025, 0.975))[2], 3)
######################
#BOLDNESS
######################
#BOLDNESS FOR UNINFECTED (INTERCEPT)
int_bold_c_num<- as.numeric(unlist(bold_int))
#get raw mean and sd from raw data
mean_boldness <- mean(all_data_T$log_boldness)
sd_boldness <- sd(all_data_T$log_boldness)
#raw units
raw_int_b_c<-(int_bold_c_num * sd_boldness) + mean_boldness
range(raw_int_b_c)
raw_bold_c<- as.numeric(unlist(raw_int_b_c))
raw_bold_c_mean<-mean(raw_bold_c)
#BOLDNESS FOR CONTRAST (TREATMENT T) U-T
int_bold_con_T_num<- as.numeric(unlist(bold_T))
#EXPLORATION FOR CONTRAST (TREATMENT E)
int_bold_con_E_num<- as.numeric(unlist(bold_E))
#BOLDNESS FOR E
int_bold_E<-(int_bold_con_E_num + int_bold_c_num)
int_bold_E_num<- as.numeric(unlist(int_bold_E))
#raw units
raw_int_b_E<-(int_bold_E_num * sd_boldness) + mean_boldness
range(raw_int_b_E)
raw_bold_E<- as.numeric(unlist(raw_int_b_E))
raw_bold_E_mean<-mean(raw_bold_E)
#BOLDNESS FOR T
int_bold_T<-(int_bold_con_T_num + int_bold_c_num)
int_bold_T_num<- as.numeric(unlist(int_bold_T))
#Contrast T and E
int_bold_E_T<-(int_bold_T_num - int_bold_E_num)
raw_int_b_E_T<-(int_bold_E_T * sd_boldness) + mean_boldness
range(raw_int_b_E_T)
raw_bold_E_T<- as.numeric(unlist(raw_int_b_E_T))
raw_bold_E_T_mean<-mean(raw_int_b_E_T)
blb_bold_E_T <- round(quantile(raw_int_b_E_T, c(0.025, 0.975))[1], 3)
bub_bold_E_T <- round(quantile(raw_int_b_E_T, c(0.025, 0.975))[2], 3)
#raw units
raw_int_b_T<-(int_bold_T_num * sd_boldness) + mean_boldness
range(raw_int_b_T)
raw_bold_T<- as.numeric(unlist(raw_int_b_T))
raw_bold_T_mean<-mean(raw_bold_T)
#Boldness confidence intervals
#Control
blb_bold_T <- round(quantile(raw_bold_T, c(0.025, 0.975))[1], 3)
bub_bold_T <- round(quantile(raw_bold_T, c(0.025, 0.975))[2], 3)
#infected
blb_bold_E <- round(quantile(raw_bold_E, c(0.025, 0.975))[1], 3)
bub_bold_E <- round(quantile(raw_bold_E, c(0.025, 0.975))[2], 3)
#uninfected
blb_bold_c <- round(quantile(raw_bold_c, c(0.025, 0.975))[1], 3)
bub_bold_c <- round(quantile(raw_bold_c, c(0.025, 0.975))[2], 3)
######################
#ACTIVITY
######################
#ACTIVITY FOR UNINFECTED (INTERCEPT)
int_act_c_num<- as.numeric(unlist(act_int))
#mean and sd from raw data
mean_activity <- mean(all_data_T$log_activity)
sd_activity <- sd(all_data_T$log_activity)
#raw units
raw_int_act_c<-(int_act_c_num * sd_activity) + mean_activity
range(raw_int_act_c)
raw_act_c<- as.numeric(unlist(raw_int_act_c))
raw_act_c_mean<-mean(raw_act_c)
#ACTIVITY FOR CONTRAST (TREATMENT T)
int_act_con_T_num<- as.numeric(unlist(act_T))
#ACTIVITY FOR T
int_act_T<-(int_act_con_T_num + int_act_c_num)
int_act_T_num<- as.numeric(unlist(int_act_T))
#raw units
raw_int_act_T<-(int_act_T_num * sd_activity) + mean_activity
range(raw_int_act_T)
raw_act_T<- as.numeric(unlist(raw_int_act_T))
raw_act_T_mean<-mean(raw_act_T)
#ACTIVITY FOR CONTRAST (TREATMENT E)
int_act_con_E_num<- as.numeric(unlist(act_E))
#ACTIVITY FOR E
int_act_E<-(int_act_con_E_num + int_act_c_num)
int_act_E_num<- as.numeric(unlist(int_act_E))
#Contrast T and E
int_act_E_T<-(int_act_T_num - int_act_E_num)
raw_int_act_E_T<-(int_act_E_T * sd_activity) + mean_activity
range(raw_int_act_E_T)
raw_act_E_T<- as.numeric(unlist(raw_int_act_E_T))
raw_act_E_T_mean<-mean(raw_int_act_E_T)
blb_act_E_T <- round(quantile(raw_int_act_E_T, c(0.025, 0.975))[1], 3)
bub_act_E_T <- round(quantile(raw_int_act_E_T, c(0.025, 0.975))[2], 3)