#####set working directory###


###initial libraries to load
require(tidyverse)
require(MCMCglmm)
require(tidybayes)
require(ordinal)
require(codatools)
require(coda)


###load final .csv files###
###data for species level differences in DoG####

dogdat_spec <- read.csv("dogdat.3.2.3.csv")


###data for individual success analysis within session###

dogdat_indiv <- read.csv("dogdat_ind.csv")


###for coping behavior analysis within session###

dogdat_coping <- read.csv("dogdat_coping.csv")

###making sure all variables are correct format###

dogdat_spec$species <- as.factor(dogdat_spec$species)
dogdat_spec$sex <- as.factor(dogdat_spec$sex)
dogdat_spec$maxdog <- as.factor(dogdat_spec$maxdog)


###species level analysis####

prior1.glm <- list(R = list(V = 1, nu = 0.002, fix = 1))

m1 <- MCMCglmm(as.factor(maxdog) ~ sex + species,
               data = dogdat_spec,
               prior = prior1.glm,
               family = "ordinal",
               pr=TRUE,
               verbose=TRUE, 
               saveX = TRUE, saveZ = TRUE,
               thin=10000, burnin= 500000, nitt= 5000000)

summary(m1)

###making sure all variables are correct format and releveling to time = 10###


dogdat_indiv$time <- as.factor(dogdat_indiv$time)
dogdat_indiv$time <- relevel(dogdat_indiv$time, ref = "10")


###individual level analysis####

prior1.indiv <- list(G = list(G1 = list(V = 1, nu = 0.002, alpha.mu = 0, alpha.V = 1000),
                              G2 = list(V = 1, nu = 0.002, alpha.mu = 0, alpha.V = 1000)),
               R = list(V = 1, nu = 0.002, fix = 1))


m1.glmtime10 <- MCMCglmm(cbind(success_sess, failure) ~ as.factor(time) * scale(distract.corr.2) + sex + species * scale(distract.corr.2) + resid + scale(trial_num),
                                          random = ~ name + session,
                                          data = dogdat_indiv,
                                          family = "multinomial2",
                                          prior = prior1.indiv,
                                          pr=TRUE,
                                          verbose=TRUE, 
                                          saveX = TRUE, saveZ = TRUE,
                                          thin=10000, burnin= 500000, nitt= 5000000)

summary(m1.glmtime10)


###coping analysis###

prior_coping <- list(G = list(G1 = list(V = 1, nu = 0.002, alpha.mu = 0, alpha.V = 1000)),
                     R = list(V = 1, nu = 0.002, fix = 1))

m1.coping <-  MCMCglmm(cbind(success_sess, failure) ~ scale(Pacing) + scale(Ring) + scale(Perch) + scale(Table) + scale(Door) + scale(BiteArm) + scale(PXG) + scale(Seeds),
                                   random = ~ name, 
                                   data = dogdat.3.4,
                                   family = "multinomial2",
                                   prior = prior_coping,
                                   pr=TRUE,
                                   verbose=TRUE, 
                                   saveX = TRUE, saveZ = TRUE,
                                   thin=10000, burnin= 500000, nitt= 5000000)


summary(m1.coping)