How to deal with "Non-conforming parameters with inprod function" in JAGS model

I am trying to model the variance in overall species richness with the habitat covariates of a camera trapping station using R2jags. However, I keep getting the error:

"Error in jags.model(model.file, data = data, inits = init.values, n.chains = n.chains,  : 
  RUNTIME ERROR:
Non-conforming parameters in function inprod"

I used a very similar function in my previous JAGS model (to find the species richness) so I am not sure why it is not working now...

I have already tried formatting the covariates within the inprod function in different ways, as a data frame and a matrix, to no avail.

Variable specification:

J=length(ustations) #number of camera stations

NSite=Global.Model$BUGSoutput$sims.list$Nsite
NS=apply(NSite,2,function(x)c(mean(x)))

###What I think is causing the problem:

COV <- data.frame(as.numeric(station.cov$NDVI), as.numeric(station.cov$TRI), as.numeric(station.cov$dist2edge), as.numeric(station.cov$dogs), as.numeric(station.cov$Leopard_captures))

###but I have also tried:

COV <- cbind(station.cov$NDVI, station.cov$TRI, station.cov$dist2edge, station.cov$dogs, station.cov$Leopard_captures)

JAGS model:

sink("Variance_model.txt")
cat("model {
# Priors
Y ~ dnorm(0,0.001)              #Mean richness
X ~ dnorm(0,0.001)              #Mean variance

for (a in 1:length(COV)){
U[a] ~ dnorm(0,0.001)}      #Variance covariates

# Likelihood
for (i in 1:J) { 
mu[i] <- Y          #Hyper-parameter for station-specific all richness
NS[i] ~ dnorm(mu[i], tau[i])   #Likelihood
tau[i] <- (1/sigma2[i])
log(sigma2[i]) <- X + inprod(U,COV[i,])
}
}
", fill=TRUE)
sink()

var.data <- list(NS = NS, 
                 COV = COV,
                 J=J)

Bundle data:

# Inits function
var.inits <- function(){list(
  Y =rnorm(1), 
  X =rnorm(1), 
  U =rnorm(length(COV)))}

# Parameters to estimate
var.params <- c("Y","X","U")

# MCMC settings
nc <- 3
ni <-20000
nb <- 10000
nthin <- 10

Start Gibbs sampler:

jags(data=var.data,
     inits=var.inits,
     parameters.to.save=var.params,
     model.file="Variance_model.txt", 
     n.chains=nc,n.iter=ni,n.burnin=nb,n.thin=nthin)

Ultimately, I get the error:

Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Deleting model

Error in jags.model(model.file, data = data, inits = init.values, n.chains = n.chains,  : 
  RUNTIME ERROR:
Non-conforming parameters in function inprod

In the end, I would like to calculate the mean and 95% credible interval (BCI) estimates of the habitat covariates hypothesized to influence the variance in station-specific (point-level) species richness.

Any help would be greatly appreciated!

Solution

It looks like you are using length to generate the priors for U. In JAGS this function will return the number of elements in a node array. In this case, that would be the number of rows ins COV multiplied by the number of columns.

Instead, I would supply a scalar to your data list that you supply to jags.model.

var.data <- list(NS = NS, 
                 COV = COV,
                 J=J,
                 ncov = ncol(COV)
)

Following this, you can modify your JAGS code where you are generating your priors for U. The model would then become:

sink("Variance_model.txt")
cat("model {
# Priors
Y ~ dnorm(0,0.001)              #Mean richness
X ~ dnorm(0,0.001)              #Mean variance

for (a in 1:ncov){ # THIS IS THE ONLY LINE OF CODE THAT I MODIFIED
U[a] ~ dnorm(0,0.001)}      #Variance covariates

# Likelihood
for (i in 1:J) { 
mu[i] <- Y          #Hyper-parameter for station-specific all richness
NS[i] ~ dnorm(mu[i], tau[i])   #Likelihood
tau[i] <- (1/sigma2[i])
log(sigma2[i]) <- X + inprod(U,COV[i,])
}
}
", fill=TRUE)
sink()