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Data-simulation_SR.R
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Data-simulation_SR.R
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## Author: A.D. Wright
## Project: NCRN Amphibians - Monitoring Optimization
## Code: Data simulation and analysis for the Random Stratified case study
#rm(list = ls())
#options(max.print = 1000)
## TABLE OF CONTENTS
## Packages, working directory, and data 15
## Global parameters for simulation 29
## General f()'s for simulation 103
## Run and analyze simulations 279
#########
## Part - Packages, working directory, and data
#########
##
#### Install Packages
##
#tidyverse
if(!require(tidyverse)) {install.packages('tidyverse');require(tidyverse)}
#jagsUI
if(!require(jagsUI)) {install.packages('jagsUI');require(jagsUI)}
#########
## Part - Global parameters for simulation
#########
set.seed(25)
##
#### Sampling dimensions
##
#Years
Y <- 10
#Sampling Occassions per Year
K <- 6
Kmed <- 4
Klow <- 2
#Units
R <- 10
#Sites per unit
JMax <- 100
JMin <- 10
Jr <- as.integer(runif(n = R, min = JMin, max = JMax))
#Species Total (will vary by park)
I <- 25
M <- 25
#Datasets per scenario
#5 sampling scenarios:
scenarios <- c('stratified','indicator','rotating','split','weighted')
#5 sampling efforts: #(10%, 20%, 30%, 40%, 50%)
effort <- c(0.1, 0.2, 0.3, 0.4, 0.5)
##
#### Global, regional, and species parameters
##
#Global
#Unit Occupancy
mean.c0 <- 0.4
sd.c0 <- 0.25
#Site Occupancy
#Intercept
mean.a0.global <- 0.3
sd.a0.global <- 0.5
sd.a0 <- 0.5
#Site effect
mu.a1.global <- 0.8
sd.a1.global <- 0.5
sd.a1 <- 0.5
#Year effect
mu.a2.global <- -0.4
sd.a2.global <- 0.5
sd.a2 <- 0.5
#Autologistic effect
mu.a3.global <- 0.4
sd.a3.global <- 0.5
sd.a3 <- 0.5
#Detection
mean.b0.global <- 0.3
sd.b0.global <- 0.5
sd.b0 <- 0.5
##
#### Generate covaraite data
##
Site_effect_a1 <- array(rnorm(JMax*R, 0, 1), dim = c(100,10))
Year <- 1:10
Year_effect_a2 <- (Year - mean(Year))/sd(Year)
#########
## Part - General f()'s for simulation
#########
##
#### Simulation f() - A blend of Sutherland et al. 2016 S2 & simDCM
##
#Basic Function
sim_community <- function(R. = R, # of spatial units
I. = I, # of species
M. = M, # of augmented species
Y. = Y, # of years
K. = K, # of sampling occassions per year
JMax = max(Jr), # sites max per unit
Jr. = Jr, # of sites per spatial unit
mean.c0. = mean.c0, sd.c0. = sd.c0, #unit occupancy
mean.a0.global. = mean.a0.global, sd.a0.global. = sd.a0.global, sd.a0. = sd.a0, #mu.a0. = mu.a0, a0. = a0, #site occupancy
mu.a1.global. = mu.a1.global, sd.a1.global. = sd.a1.global, sd.a1. = sd.a1, #mu.a1. = mu.a1, a1. = a1, #site effect on occupancy
mu.a2.global. = mu.a2.global, sd.a2.global. = sd.a2.global, sd.a2. = sd.a2, #mu.a2. = mu.a2, a2. = a2, #year effect on occupancy
mu.a3.global. = mu.a3.global, sd.a3.global. = sd.a3.global, sd.a3. = sd.a3, #mu.a3. = mu.a3, a3. = a3, #auto effect on occupancy
mean.b0.global. = mean.b0.global, sd.b0.global. = sd.b0.global, sd.bo. = sd.b0, #mu.b0. = mu.b0, b0. = b0,#detection
Site_effect_a1. = Site_effect_a1, Year_effect_a2. = Year_effect_a2 #covariates
){
#Create empty objects for loops
#Region (and species - in 2nd loop)
mu.a0 <- mu.a1 <- mu.a2 <- mu.a3 <- mu.b0 <- omega <- N_unit <- array(NA, dim = R)
a0 <- a1 <- a2 <- a3 <- b0 <- array(NA, dim = c(I+M,R))
W_mat <- array(NA, c(I+M,R))
Z_mat <- logit_psi <- array(NA, c(JMax, Y, I+M, R))
X_mat <- array(NA, c(JMax, K, Y, I+M, R))
#Generate park and species parameters
for(r in 1:R){
omega[r] <- plogis(rnorm(1,qlogis(mean.c0),sd.c0))
#Occupancy
mu.a0[r] <- rnorm(1, mean = qlogis(mean.a0.global), sd = sd.a0.global)
mu.a1[r] <- rnorm(1, mean = mu.a1.global, sd = sd.a1.global)
mu.a2[r] <- rnorm(1, mean = mu.a2.global, sd = sd.a2.global)
mu.a3[r] <- rnorm(1, mean = mu.a3.global, sd = sd.a3.global)
#Detection
mu.b0[r] <- rnorm(1, mean = qlogis(mean.b0.global), sd = sd.b0.global)
for(i in 1:(I+M)){
W_mat[i,r] <- rbinom(1, 1, omega[r])
#Species
#Occupancy
a0[i,r] <- rnorm(1, mean = mu.a0[r], sd = sd.a0)
a1[i,r] <- rnorm(1, mean = mu.a1[r], sd = sd.a1)
a2[i,r] <- rnorm(1, mean = mu.a2[r], sd = sd.a2)
a3[i,r] <- rnorm(1, mean = mu.a3[r], sd = sd.a3)
#Detection
b0[i,r] <- rnorm(1, mean = mu.b0[r], sd = sd.b0)
}
N_unit[r] <- sum(W_mat[,r])
}
#Generate data
for(r in 1:R){
for(i in 1:(I+M)){
for(j in 1:Jr[r]) {
logit_psi[j,1,i,r] <- a0[i,r] + a1[i,r]*Site_effect_a1[j,r] + a2[i,r]*Year_effect_a2[1]
Z_mat[j,1,i,r] <- rbinom(1, 1, plogis(logit_psi[j,1,i,r])*W_mat[i,r])
for(y in 2:Y){
logit_psi[j,y,i,r] <- a0[i,r] + a1[i,r]*Site_effect_a1[j,r] + a2[i,r]*Year_effect_a2[y] + a3[i,r]*Z_mat[j,y-1,i,r]
Z_mat[j,y,i,r] <- rbinom(1, 1, plogis(logit_psi[j,y,i,r])*W_mat[i,r])
} #y
for(y in 1:Y){
for(k in 1:K){
X_mat[j,k,y,i,r] <- rbinom(1, 1, Z_mat[j,y,i,r]*plogis(b0[i,r]))
} #k
} #y
} #j
} #i
} #r
return(list(X_mat = X_mat, logit_psi = logit_psi, Z_mat = Z_mat, #the simulated data
R = R, I = I, M = M, Y = Y, K = K, JMax = JMax, Jr = Jr, #the dimensions used to simulate the data
Site_effect_a1 = Site_effect_a1, Year_effect_a2 = Year_effect_a2, #the covariates used to simulate the data
mean.c0 = mean.c0, sd.c0 = sd.c0, omega = omega, W_mat = W_mat, N_unit = N_unit, #the parameters used to simulate data - unit occupancy
mean.a0.global = mean.a0.global, sd.a0.global = sd.a0.global, sd.a0 = sd.a0, mu.a0 = mu.a0, a0 = a0, #the parameters used to simulate data - site occupancy (intercept)
mu.a1.global = mu.a1.global, sd.a1.global = sd.a1.global, sd.a1 = sd.a1, mu.a1 = mu.a1, a1 = a1, #the parameters used to simulate data - site occupancy (slope - site)
mu.a2.global = mu.a2.global, sd.a2.global = sd.a2.global, sd.a2 = sd.a2, mu.a2 = mu.a2, a2 = a2, #the parameters used to simulate data - site occupancy (slope - year)
mu.a3.global = mu.a3.global, sd.a3.global = sd.a3.global, sd.a3 = sd.a3, mu.a3 = mu.a3, a3 = a3, #the parameters used to simulate data - site occupancy (slope - auto)
mean.b0.global = mean.b0.global, sd.b0.global = sd.b0.global, sd.b0 = sd.b0, mu.b0 = mu.b0, b0 = b0 #the parameters used to simulate data - detection (intercept)
))
} #f() - sim_community
##
#### Base function to create tables of results for plotting purposes
##
#This should work for both vary vs non-vary scenarios
org_results <- function(jagsOut, td){
nPark <- td$R
nSpp <- dim(td$W_mat)[1]*dim(td$W_mat)[2]
#Global
simTab_g <- data.frame(mean.c0 = NA,
sd.c0 = NA,
mu.a0.global = NA,
sd.a0.global = NA,
sd.a0 = NA,
mu.b0.global = NA,
sd.b0.global = NA,
sd.b0 = NA,
mu.a1.global = NA,
sd.a1.global = NA,
sd.a1 = NA,
mu.a2.global = NA,
sd.a2.global = NA,
sd.a2 = NA,
mu.a3.global = NA,
sd.a3.global = NA,
sd.a3 = NA
)
simTab_g$mean.c0 <- jagsOut$mean$mean.c0 - td$mean.c0
simTab_g$sd.c0 <- jagsOut$mean$sd.c0 - td$sd.c0
simTab_g$mu.a0.global <- jagsOut$mean$mu.a0.global - mean(td$mu.a0)
simTab_g$sd.a0.global <- jagsOut$mean$sd.a0.global - td$sd.a0.global
simTab_g$sd.a0 <- jagsOut$mean$sd.a0 - td$sd.a0
simTab_g$mu.a1.global <- jagsOut$mean$mu.a1.global - mean(td$mu.a1)
simTab_g$sd.a1.global <- jagsOut$mean$sd.a1.global - td$sd.a1.global
simTab_g$sd.a1 <- jagsOut$mean$sd.a1 - td$sd.a1
simTab_g$mu.a2.global <- jagsOut$mean$mu.a2.global - mean(td$mu.a2)
simTab_g$sd.a2.global <- jagsOut$mean$sd.a2.global - td$sd.a2.global
simTab_g$sd.a2 <- jagsOut$mean$sd.a2 - td$sd.a2
simTab_g$mu.a3.global <- jagsOut$mean$mu.a3.global - mean(td$mu.a3)
simTab_g$sd.a3.global <- jagsOut$mean$sd.a3.global - td$sd.a3.global
simTab_g$sd.a3 <- jagsOut$mean$sd.a3 - td$sd.a3
simTab_g$mu.b0.global <- jagsOut$mean$mu.b0.global - mean(td$mu.b0)
simTab_g$sd.b0.global <- jagsOut$mean$sd.b0.global - td$sd.b0.global
simTab_g$sd.b0 <- jagsOut$mean$sd.b0 - td$sd.b0
#Park
simTab_p <- data.frame(mu.a0 = rep(NA,nPark),
mu.b0 = rep(NA,nPark),
mu.a1 = rep(NA,nPark),
mu.a2 = rep(NA,nPark),
mu.a3 = rep(NA,nPark)
)
simTab_p$mu.a0[1:nPark] <- (jagsOut$mean$mu.a0 - apply(td$a0,2,mean))
simTab_p$mu.a1[1:nPark] <- (jagsOut$mean$mu.a1 - apply(td$a1,2,mean))
simTab_p$mu.a2[1:nPark] <- (jagsOut$mean$mu.a2 - apply(td$a2,2,mean))
simTab_p$mu.a3[1:nPark] <- (jagsOut$mean$mu.a3 - apply(td$a3,2,mean))
simTab_p$mu.b0[1:nPark] <- (jagsOut$mean$mu.b0 - apply(td$b0,2,mean))
#Species
simTab_s <- data.frame(a0 = rep(NA,nSpp),
b0 = rep(NA,nSpp),
a1 = rep(NA,nSpp),
a2 = rep(NA,nSpp),
a3 = rep(NA,nSpp)
)
simTab_s$a0[1:nSpp] <- as.vector(jagsOut$mean$a0*na_if(td$W_mat, 0)) - (td$a0*na_if(td$W_mat, 0))
simTab_s$b0[1:nSpp] <- as.vector(jagsOut$mean$b0*na_if(td$W_mat, 0)) - (td$b0*na_if(td$W_mat, 0))
simTab_s$a1[1:nSpp] <- as.vector(jagsOut$mean$a1*na_if(td$W_mat, 0)) - (td$a1*na_if(td$W_mat, 0))
simTab_s$a2[1:nSpp] <- as.vector(jagsOut$mean$a2*na_if(td$W_mat, 0)) - (td$a2*na_if(td$W_mat, 0))
simTab_s$a3[1:nSpp] <- as.vector(jagsOut$mean$a3*na_if(td$W_mat, 0)) - (td$a3*na_if(td$W_mat, 0))
#Put results all together
x <- list(global = simTab_g, park = simTab_p, species = simTab_s)
return(x)
}
#########
## Part - Run and analyze simulations
#########
## Looping Variables
start <- 1
end <- 10 #Run this script 65 times in HPCC for a total of 650 sims: for i in {1..65}; do sbatch amphibianRS.sb; done
results <- Jr_temp <- maxJr_temp <- K_temp <- list()
converge <- vector()
##Loop
for(i in start:end){
#Remove seed so simulations in parallel are all different
set.seed(NULL)
#Simulate a data set
td <- sim_community()
## Simulaiton specific variable
strategy <- "randomStrat"
effort <- 0.5
Jr_temp[[i]] <- round(td$Jr*effort)
maxJr_temp[[i]] <- max(Jr_temp[[i]])
K_temp[[i]] <- 4
#Need to rewrite Z_mat and W_mat based on effort for initial values
for(r in 1:td$R){
for(m in 1:(td$I+td$M)){
for(j in (Jr_temp[[i]][r]+1):td$JMax) {
for(y in 1:td$Y){
td$Z_mat[j,y,m,r] <- NA
for(k in 1:6){
td$X_mat[j,k,y,m,r] <- NA
} #k
} #y
} #j
} #i
} #r
td$X_mat <- td$X_mat[1:maxJr_temp[[i]],1:K,1:10,1:50,1:10]
td$Z_mat <- td$Z_mat[1:maxJr_temp[[i]],1:10,1:50,1:10]
# Organize data for jags
jagsDat <- list(X = td$X_mat, #Detection data
R = td$R, I = td$I, M = td$M, Y = td$Y, K = K_temp[[i]], Jr = Jr_temp[[i]], #Looping variables
Site_effect_a1 = td$Site_effect_a1, Year_effect_a2 = td$Year_effect_a2 #Covariates
)
# Compile inititial values for jags
jagsIni <- function(){
list(Z=td$Z_mat, W=td$W_mat)
}
# Paramaters to monitor for jags
jagsPar <- c('mean.c0', 'sd.c0', #unit occupancy
'mu.a0.global', 'sd.a0.global', 'sd.a0', 'mu.a0', 'a0', #site occupancy (intercept)
'mu.a1.global', 'sd.a1.global', 'sd.a1', 'mu.a1', 'a1', #site occupancy (slope)
'mu.a2.global', 'sd.a2.global', 'sd.a2', 'mu.a2', 'a2', #site occupancy (slope)
'mu.a3.global', 'sd.a3.global', 'sd.a3', 'mu.a3', 'a3', #site occupancy (slope)
'mu.b0.global', 'sd.b0.global', 'sd.b0', 'mu.b0', 'b0' #detection (intercept)
)
#Run jags()
jagsFit <- autojags(data = jagsDat,
inits = jagsIni,
parameters.to.save = jagsPar,
model.file = "mrcm_jags.txt",
parallel=T,
n.chains=3,
n.adapt=1000,
iter.increment=10000,
max.iter=50000,
n.burnin=5000,
n.thin=10,
Rhat.limit = 1.1
)
# Append this run to one full results object
results[[i]] <- org_results(jagsOut = jagsFit, td = td)
converge[i] <- max(unlist(jagsFit$Rhat))
}# END OF LOOP
##Save results file
date <- gsub(pattern = c(":| "), replacement = "-", x = as.character(Sys.time()))
file_str <- paste("jagsFit_","Simul_", effort*100, strategy,"_",date,".R",sep="")
#Save
save(results, converge, file=file_str)