CODEWITHSUNDEEP
rbayesianjags
Paul
Paul

Reputation: 11

'Invalid parent values' error when running JAGS from R

I am running a simple generalized linear model, calling JAGS from R. The model is negatively binomially distributed. The model is being fitted to data on counts of fish, with the majority of individual counts ('C' in the data set below) being zeros.

I initially ran the model with one covariate, temperature ('Temp'). About half of the time the model ran and the other half of the time the model gave me the error, 'Error in node C[###] Invalid parent values.' The value for C[###] in the error message changes with each successive attempt to run the model.

Since my success at running the model was inconsistent, I tried adding another covariate, salinity ('Salt'). Then the model would not run at all, with the same error message as above.

Any ideas or suggestions on the source of the error are greatly appreciated.
I am suspecting that the initial values for the dispersion parameter, r, may be the issue. Ideally I add several more covariates into model fitting if this error can be addressed.

The data set and code are immediately below. For sake of getting the data to load properly on this website, I have omitted 662 of the 672 total values; even with the reduced data set (n = 10 instead of n = 672) the problem remains.

Thank you.

setwd("C:/Users/John/Desktop")

library('coda')
library('rjags')
library('R2jags')
set.seed(1000000000)
#data
n=10
C=c(0,0,0,0,0,1,0,0,0,1)
Temp=c(0,29.3,25.3,28.7,28.7,24.4,25.1,25.1,24.2,23.3)
Salt=c(6,6,0,6,6,0,12,12,6,12)
sink("My Model.txt")
cat("
model {
r~dunif(0,10)
beta0~dunif (-20,20)
beta1~dunif (-20,20)
beta2~dunif (-20,20)
for (i in 1:n) {
C[i] ~ dnegbin(p[i], r)
p[i] <- r/(r+lambda[i])
log(lambda[i]) <- mu[i]
mu[i] <- beta0 + beta1*Temp[i]  + beta2*Salt[i]
}
}
 ", fill=TRUE)
sink()
n=n
C=C
Temp=Temp
Salt=Salt
#bundle data
bugs.data = list(
"n",
"C",
"Temp",
"Salt")    
#parameters to monitor
params<-c(
"r",
"beta0",
"beta1",
"beta2")  
#initial values
inits <- function(){list(
r=floor(runif(1,0,5)),
beta0=runif(1,-5,5),
beta1=runif(1,-5,5),
beta2=runif(1,-5,5))}

model.file <- 'My Model.txt'
jagsfit <- jags(data=bugs.data, inits=inits, params, n.iter=1000, n.thin=10, n.burnin=100, model.file)
print(jagsfit, digits=5)

Upvotes: 1

Views: 4361

Answers (1)

Matt Denwood
Matt Denwood

Reputation: 2583

This works fine for me most of the time, but it would fail with the error you describe if the inits function samples a value of r of 0 - which you have made more likely by using floor() in the inits function (not sure why you did that - r is not restricted to integers but is strictly positive). Also, every time you run the model you will get different initial values (unless setting a random seed in R) which is making your life more complicated that it needs to be. I generally recommend picking fixed (and probably over dispersed) initial values, such as r=0.01 and r=10 for the two chains in your example.

However, JAGS picks usable initial values for this model as you can see by not providing your own inits e.g.:

library('runjags')
listdata <- lapply(bugs.data, get)
names(listdata) <- unlist(bugs.data)
run.jags(model.file, params, listdata)

I would also have a think about the prior you are using for r - it could well be that this will have a bigger effect on your posterior than intended. Another (not necessarily better) option is something like a gamma prior.

Matt

Upvotes: 1

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