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occupancy_singlespp_model8.R
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library(here)
library(nimble)
library(MCMCvis)
library(coda)
start.time <- Sys.time()
# MCMC settings
ni <- 40000
nt <- 3
nb <- 5000
nc <- 3
tot.samples <- floor((ni-nb)/nt)*nc
#import data
data1 <- read.csv("data/bird.binary.noCorrRes.csv")
#set up sites, site type, years, species
#sites - put in alphabetical order
Sites <- sort(unique(data1$Site))
#site type - note that CD sites are 1 and PCD sites are 2
site.type <- Sites
for(s in 1:length(Sites)){
site.type[s] <- data1$Site.Type[max(which(data1$Site == Sites[s]))]
}
site.type <- as.numeric(as.factor(site.type))
#years - put in order
Years <- sort(unique(data1$Year))
Species <- c(9:61)
#set up observation data dim = sites, years, species, reps
array2 <- array(NA,dim= c(7,12,53,32))
for(j in 1:length(Sites)){
for(y in 1:length(Years)){
for(k in 1:length(Species)){
nums <- intersect(which(data1$Site == Sites[j]),which(data1$Year == Years[y]))
if(length(nums)>0){
array2[j,y,k,1:length(nums)] <- data1[nums,Species[k]]
}
}
}
}
#effort array - make effort 0 anytime the data are NA
array.eff <- array(1,dim=c(7,12,53,32))
array.eff[is.na(array2==TRUE)] <- 0
#now make the data that are NAs 0s
array2[is.na(array2==TRUE)] <- 0
#set up to hold analysis results
#species are rows
#columns are mean predictions site type CD 1:12, mean predictions site type PCD 1:12, sd predictions, LCI predictions, UCI predictions, WAIC, multivariate R-hat, coefficients, sd coefficience, LCI coefficients, UCI coefficients
results.mat <- matrix(NA,nrow=length(Species),ncol = 126)
#save all the samples from stochastic realizations
z.pred <- array(NA,dim=c(length(Species),tot.samples,24,10))
#loop through species
for(i in 1:length(Species)){
#pull out data for single species analysis
spp.i <- array2[,,i,]
eff.i <- array.eff[,,i,]
##NIMBLE code
occ1 <- nimbleCode( {
#Likelihood
for(s in 1:n.sites) {
for(y in 1:n.years){
# State Process
z[s,y] ~ dbern(psi[s,y])
# Observation Process
for(n in 1:n.reps) {
spp.i[s,y,n] ~ dbern(z[s,y] * p[s,y] * eff.i[s,y,n]) #state * p * effort (0 or 1)
}#n replicate
}#y year
}#j site
#model on detection
#model on occupancy
for(s in 1:n.sites){
for(y in 1:n.years){
logit(p[s,y]) <- int.p + p.ran.site[s] + p.ran.year[y]
logit(psi[s,y]) <- int.psi + beta.site.type[site.type[s]] + beta.year*year.norm[y] + beta.year2*year2.norm[y] + beta.site.type.year[site.type[s]]*year.norm[y] + beta.site.type.year2[site.type[s]]*year2.norm[y]
}
}
for(s in 1:2){
for(y in 1:n.years){
psi.pred[s,y] <- 1/(1+exp(-(int.psi + beta.site.type[site.type[s]] + beta.year*year.norm[y] + beta.year2*year2.norm[y] + beta.site.type.year[site.type[s]]*year.norm[y] + beta.site.type.year2[site.type[s]]*year2.norm[y])))
}
}
#Random effects
for(s in 1:n.sites){
p.ran.site[s] ~ dnorm(0,sd = sd.site)
}
for(y in 1:n.years){
p.ran.year[y] ~ dnorm(0,sd = sd.year)
}
#Priors
int.p ~ dnorm(0,sd=1)
sd.site ~ dgamma(1,1)
sd.year ~ dgamma(1,1)
int.psi ~ dnorm(0,sd=1)
beta.site.type[1] <- 0
beta.site.type[2] ~ dnorm(0,sd=1)
beta.year ~ dnorm(0,sd=1)
beta.year2 ~ dnorm(0,sd=1)
beta.site.type.year[1] <- 0
beta.site.type.year[2] ~ dnorm(0,sd=1)
beta.site.type.year2[1] <- 0
beta.site.type.year2[2] ~ dnorm(0,sd=1)
})
######################################################################
# #
# Data and Constants #
# #
######################################################################
# Bundle data
data <- list(spp.i=spp.i)
#get constants
n.sites <- dim(spp.i)[1]
n.years <- dim(spp.i)[2]
n.reps <- dim(spp.i)[3]
year.norm <- (c(1:12) - mean(c(1:12)))/sd(c(1:12))
year2.norm <- pow(year.norm,2)
constants <- list(eff.i=eff.i,site.type=site.type,year.norm=year.norm,year2.norm=year2.norm,n.sites=n.sites,n.years=n.years,n.reps=n.reps)
######################################################################
# #
# Inits #
# #
######################################################################
z.init <- matrix(0,nrow=n.sites,ncol=n.years)
for(s in 1:n.sites){
for(y in 1:n.years){
if(any(spp.i[s,y,]==1)){
z.init[s,y] <- 1
}
}
}
inits <- list(z=z.init)
######################################################################
# #
# Run the model #
# #
######################################################################
# Parameters monitored
params <- c("psi.pred","int.p","int.psi","beta.site.type","beta.year","beta.year2","beta.site.type.year","beta.site.type.year2")
Rmodel1 <- nimbleModel(code = occ1, constants = constants, data = data,
check = FALSE, calculate = FALSE, inits = inits)
conf1 <- configureMCMC(Rmodel1, monitors = params, thin = nt, useConjugacy = FALSE, enableWAIC=TRUE)
Rmcmc1 <- buildMCMC(conf1)
Cmodel1 <- compileNimble(Rmodel1, showCompilerOutput = FALSE)
Cmcmc1 <- compileNimble(Rmcmc1, project = Rmodel1)
## Run MCMC ####
out <- runMCMC(Cmcmc1, niter = ni, nburnin = nb , nchains = nc, inits = inits,
setSeed = FALSE, progressBar = TRUE, samplesAsCodaMCMC = TRUE, WAIC=TRUE)
params <- c("psi.pred[1, 1]","psi.pred[1, 2]","psi.pred[1, 3]","psi.pred[1, 4]","psi.pred[1, 5]","psi.pred[1, 6]","psi.pred[1, 7]","psi.pred[1, 8]","psi.pred[1, 9]","psi.pred[1, 10]","psi.pred[1, 11]","psi.pred[1, 12]",
"psi.pred[2, 1]","psi.pred[2, 2]","psi.pred[2, 3]","psi.pred[2, 4]","psi.pred[2, 5]","psi.pred[2, 6]","psi.pred[2, 7]","psi.pred[2, 8]","psi.pred[2, 9]","psi.pred[2, 10]","psi.pred[2, 11]","psi.pred[2, 12]",
"int.p","int.psi","beta.site.type[2]","beta.year","beta.year2","beta.site.type.year[2]","beta.site.type.year2[2]")
out.all <- rbind(out$samples$chain1,out$samples$chain2,out$samples$chain3)
out.all <- out.all[,params]
#store results
#int.p, int.psi mean
#psi.pred mean
results.mat[i,1:24] <- apply(out.all[,1:24], 2, mean)
#psi.pred sd
results.mat[i,25:48] <- apply(out.all[,1:24], 2, sd)
#psi.pred LCI
results.mat[i,49:72] <- apply(out.all[,1:24], 2, function(x)quantile(x,probs=0.025))
#psi.pred LCI
results.mat[i,73:96] <- apply(out.all[,1:24], 2, function(x)quantile(x,probs=0.975))
#coefficients mean
results.mat[i,97:103] <- apply(out.all[,25:31], 2, mean)
#coefficients sd
results.mat[i,104:110] <- apply(out.all[,25:31], 2, sd)
#coefficients LCI
results.mat[i,111:117] <- apply(out.all[,25:31], 2, function(x)quantile(x,probs=0.025))
#coefficients UCI
results.mat[i,118:124] <- apply(out.all[,25:31], 2, function(x)quantile(x,probs=0.975))
#WAIC
results.mat[i,125] <- out$WAIC$WAIC
#R-hat
results.mat[i,126] <- gelman.diag(out$samples[,c(2,4,6,7,8,9,10)],multivariate=TRUE)$mpsrf
for(s in 1:nrow(out.all)){
for(j in 1:24){
for(k in 1:10){
z.pred[i,s,j,k] <- rbinom(1,1,out.all[s,j])
}
}
}
print(i)
}
colnames(results.mat) <- c(paste(params[1:24],".mean",sep=""),paste(params[1:24],".sd",sep=""),paste(params[1:24],".LCI",sep=""),paste(params[1:24],".UCI",sep=""),paste(params[25:31],".mean",sep=""),paste(params[25:31],".sd",sep=""),paste(params[25:31],".LCI",sep=""),paste(params[25:31],".UCI",sep=""),"WAIC","R.hat")
all.species <- apply(z.pred,c(2,3,4),sum)
write.csv(results.mat,"results/occ_model8_results.csv")
write.csv(all.species,"results/occ_model8_allspp.csv")
end.time <- Sys.time()
elapsed <- end.time - start.time
elapsed