Bayesian in R: Dimension mismatch in values supplied for betaA - r

Question

I am working with a matrix that is 35 rows and 16 columns. I am trying to run a Bayesian Multistate Model but something in my model code prevents it from working. When I run the code in R, I get the error message:
`
Error in checkForRemoteErrors(val) :
3 nodes produced errors; first error: RUNTIME ERROR:
Dimension mismatch in values supplied for betaA
`
Any help is appreciated and my code is below:
# psi = movement probability
# phi = apparent survival
# p = detection probability
# o = occurrence probability
# load libraries
library(jagsUI)
library(lattice)
library(coda)
library("R2WinBUGS")
library("R2jags")
library(zoo)
devtools::install_github("bstaton1/postpack")`
# initializing functions####
known.state.ms <- function(ms, notseen){
#notseen: label for 'not seen'
state <- ms
state[state==notseen] <- NA
for (i in 1:dim(ms)[1]){
m <- min(which(!is.na(state[i,])))
state[i,m] <- NA
}
return(state)
`}`
#i = 1
#ch = CHY[i,]
#first = f[i]`
z_inits = function(ch, first) {
nt = length(ch)
to_fill = which(ch == 4 & 1:nt >= first)
to_keep = which(ch != 4 & 1:nt >= first)
known = ch; known[to_fill] = NA
unknown = rep(NA, nt)
known_alive = rep(NA, nt)
unknown[to_fill] = 2
for (t in 1:nt) {
known_alive[t] = ifelse(any(!is.na(known[t:nt])), 1, 0)
}
last_known_alive = max(which(known_alive == 1))
if (last_known_alive < 16) {
dead = rep(0, nt)
for (t in (last_known_alive + 1):nt) {
dead[t] = sample(c(0,1), size = 1, prob = matrix(c(0.9, 0.1, 0, 1), 2,
2, byrow = T)[dead[t-1] + 1,])
}
unknown[dead == 1] = 4
}
unknown
}
`
# import data
dat <- read.csv("bass_encounter_history_0.csv")
covs <- read.csv("depth.csv")
depth = covs[,1]
histories <- unlist(lapply(dat$history, function(x) strsplit(x,split="")))
CH <- t(matrix(histories,nrow=16,ncol=35))
CH <- gsub("0",4,CH)
CH <- gsub("A",1,CH)
CH <- gsub("B",2,CH)
CH <- gsub("C",3,CH)
CH <- matrix(as.numeric(CH),nrow=35,ncol=16)
# Built the model####
nind= nrow(CH)
n.occasions = ncol(CH)
f=c(1,1,1,2,2,2,2,2,3,3,3,4,4,4,4,4,4,1,1,1,3,1,1,1,3,3,3,3,5,3,5,6,6,6,6) # initial tagging week
jags_model = function() {
# -------------------------------------------------
# Parameters:
# phiA: survival probability at site A
# phiB: survival probability at site B
# phiC: survival probability at site C
# psiA[1,t]: probability of staying in site A
# psiA[2,t]: movement probability from site A to site B
# psiB[1,t]: movement probability from site B to site A
# psiB[2,t]: probability of staying in site B
# psiB[3,t]: movement probability from site B to site C
# psiC[1,t]: probability of staying in site C
# psiC[2,t]: movement probability from site C to site B
# betaA[i]: the effect of standardized flow on movement probabilities at site A
# betaB[i]: the effect of standardized flow on movement probabilities at site B
# betaC[i]: the effect of standardized flow on movement probabilities at site C
# wA,B,C: the variable weight of the betas, 1 = essential, 0 = insignificant
# pA: recapture probability at site A
# pB: recapture probability at site B
# pC: recapture probability at site C
# -------------------------------------------------
# States (S):
# 1 alive at A
# 2 alive at B
# 3 alive at C
# 4 dead
# Observations (O):
# 1 seen at A
# 2 seen at B
# 3 seen at C
# 4 not seen
# -------------------------------------------------
# Priors and constraints
# Survival and recapture: uniform
phiA ~ dunif(0, 1)
phiB ~ dunif(0, 1)
phiC ~ dunif(0, 1)
pA ~ dunif(0, 1)
pB ~ dunif(0, 1)
pC ~ dunif(0, 1)
wA ~ dbern(.5)
for(i in 1:3){
wB[i] ~ dbern(.5)
}
wC ~ dbern(.5)
for(t in 1:(n.occasions-1)){
logit(psiA[1,t]) <- muA + wA*betaA*x[t]
psiA[2,t] <- 1 - psiA[1,t]
logit(psiC[1,t]) <- muC + wC*betaC*x[t]
psiC[2,t] <- 1 - psiC[1,t]
for(i in 1:3){
b[i,t] <- exp(muB[i] + wB[i]*betaB[i]*x[t])
psiB[i,t] <- b[i,t]/sum(b[,t])
}
}
muA ~ dt(0, 1/1.566^2, 7.763)
muC ~ dt(0, 1/1.566^2, 7.763)
mean.psiA <- 1/(1+exp(-muA))
#it's not really the mean - it's the probability of staying in A at mean value of x (only b/c x is z- transformed)
mean.psiC <- 1/(1+exp(-muC))
betaA ~ dt(0, 1/1.566^2, 7.763)
betaC ~ dt(0, 1/1.566^2, 7.763)
for(i in 1:2){
muB[i] ~ dt(0, 1/1.566^2, 7.763)
betaB[i] ~ dt(0, 1/1.566^2, 7.763)
}
muB[3] <- 0
betaB[3] <- 0
# PREDICTED TRANSITION PROBS FOR PLOTTING
for(r in 1:n.depth){
for(i in 1:3){
pred.b[i,r] <- exp(muB[i] + wB[i]*betaB[i]*depthseq[r])
pred.psiB[i,r] <- pred.b[i,r]/sum(pred.b[,r])
}
logit(pred.psiA[1,r]) <- muA + wA*betaA*depthseq[r]
pred.psiA[2,r] <- 1 - pred.psiA[1,r]
logit(pred.psiC[1,r]) <- muC + wC*betaC*depthseq[r]
pred.psiC[2,r] <- 1 - pred.psiC[1,r]
}
# Define probabilities of state S(t+1) given S(t)
for (t in 1:(n.occasions-1)){
ps[1,t,1] <- phiA * psiA[1,t]
ps[1,t,2] <- phiA * psiA[2,t]
ps[1,t,3] <- 0
ps[1,t,4] <- 1-phiA
ps[2,t,1] <- phiB * psiB[1,t]
ps[2,t,2] <- phiB * psiB[2,t]
ps[2,t,3] <- phiB * psiB[3,t]
ps[2,t,4] <- 1-phiB
ps[3,t,1] <- 0
ps[3,t,2] <- phiC * psiC[2,t]
ps[3,t,3] <- phiC * psiC[1,t] # switch these so the coefs talk about prob(stay in C)
ps[3,t,4] <- 1-phiC
ps[4,t,1] <- 0
ps[4,t,2] <- 0
ps[4,t,3] <- 0
ps[4,t,4] <- 1
# Define probabilities of O(t) given S(t)
po[1,t,1] <- pA
po[1,t,2] <- 0
po[1,t,3] <- 0
po[1,t,4] <- 1-pA
po[2,t,1] <- 0
po[2,t,2] <- pB
po[2,t,3] <- 0
po[2,t,4] <- 1-pB
po[3,t,1] <- 0
po[3,t,2] <- 0
po[3,t,3] <- pC
po[3,t,4] <- 1-pC
po[4,t,1] <- 0
po[4,t,2] <- 0
po[4,t,3] <- 0
po[4,t,4] <- 1
} #t
# Likelihood
for (i in 1:nind){
# Define latent state at first capture
z[i,f[i]] <- y[i,f[i]]
for (t in (f[i]+1):n.occasions){
# State process: draw S(t) given S(t-1)
z[i,t] ~ dcat(ps[z[i,t-1], t-1,])
# Observation process: draw O(t) given S(t)
y[i,t] ~ dcat(po[z[i,t], t-1,])
} #t
} #i
}
jags_file = "invasiondepthmodel.txt"
postpack::write_model(jags_model, jags_file)
# Configure the model settings and initial values ####
depthseq = seq(min(depth),max(depth),length.out=100)
n.depth=length(depthseq)
#compile jags data object
jags_data <- list(y = CH, x= depth, depthseq=depthseq, n.depth=n.depth, f = f,
n.occasions = n.occasions, nind = nind, z = known.state.ms(CH, 4))
#specify initial values
jags_inits <- function(i){list(
muA = runif(1,-1,1),
muB = c(runif(2,-1,1),NA),
muC = runif(1,-1,1),
wA= rbinom(3, 1, 0.5),
wB= rbinom(3, 1, 0.5),
wC= rbinom(3, 1, 0.5),
betaA = runif(2,-1,1),
betaB = c(runif(2,-1,1),NA),
betaC = runif(2,-1,1),
phiA = runif(1, 0.5, 1),
phiB = runif(1, 0.5, 1),
phiC = runif(1, 0.5, 1),
pA = runif(1, 0.5, 1),
pB = runif(1, 0.5, 1),
pC = runif(1, 0.5, 1),
z = t(sapply(1:nind, function(i) z_inits(CH[i,], f[i])))
)
}
# Parameters monitored
jags_params <- c("phiA","phiB","phiC",
"psiA","psiB","psiC",
"wA","wB","wC",
"muA","muB","muC",
"betaA","betaB","betaC",
"pA","pB","pC",
"pred.psiA","pred.psiB","pred.psiC")
jags_dims = c(
na = 10000, # number of samples in adapting phase
ni = 40000, # number of post-burn-in samples per chain
nb = 40000, # number of burn-in samples
nt = 20, # thinning rate
nc = 3, # number of chains,
parallel = T # run chains in parallel?
); with(as.list(jags_dims), ni/nt * nc)
inits = lapply(1:jags_dims["nc"], jags_inits)
# Run the model in JAGS #####
starttime = Sys.time()
cat("MCMC Started: ", format(starttime), "\n")
post = jagsUI::jags.basic(
data = jags_data,
model.file = jags_file,
inits = inits,
parameters.to.save = jags_params,
n.adapt = jags_dims["na"],
n.iter = sum(jags_dims[c("ni", "nb")]),
n.thin = jags_dims["nt"],
n.burnin = jags_dims["nb"],
n.chains = jags_dims["nc"],
parallel = jags_dims["parallel"],
verbose = F
)
I was expecting the dimensions between the matrix and betaA values to match up. However, it seems like they are not.

Answer 1

In the model, betaA is a scalar. In the model code, you have betaA*x[t] and in the prior: betaA ~ dt(0, 1/1.566^2, 7.763) both indicating a single value. However, in the initial values, it is a vector of length 2: betaA = runif(2,-1,1). You either need to define it as a vector in the model or pass a single value in the inits.