I'm running a large number of JAGS models in R using the jags function of the R2jags package (which uses the rjags package to run JAGS).
I get a lot of warnings printed in the console:
value out of range in 'lgamma'
Printing these warnings seems to heavily impinge on computing time. How do I suppress this?
The warnings are printed as output, rather than an R warning.
Thing's I've tried that don't work include:
Wrapping my call in
try(..., silent = TRUE),suppressWarnings,invisible, orcapture.output.Altering the
jags.modelcall withinjagstojags.model(..., quiet = TRUE).
The phenomenon is also noted elsewhere, I just want to shut it up to reduce computation load from squillions of unnecessary prints to console.
Any suggestions?
Here's a long but reproducible example based on an example of the same issue on sourceforge. Apologies for the length of this but I couldn't replicate it in any smaller toy models. I couldn't care less about this particular model, but it replicates the problem reasonably simply:
Model
cat('
model {
K <- 1.1
K.mvhypgeom <- exp( logfact(sum(n[])) - logfact(nMissing) - logfact( sum(n[]) - nMissing))
p ~ dunif(0,1)
for (t in 1:N) {
X.missing[t] ~ dpois( missRate )
}
nMissing ~ dsum(X.missing[1],X.missing[2],X.missing[3],X.missing[4],X.missing[5],X.missing[6],X.missing[7],X.missing[8],X.missing[9],X.missing[10])
for (t in 1:N) {
pX.missing[t] <- exp(logfact(n[t]) - logfact( X.missing[t]) - logfact( n[t] - X.missing[t]))
ones2[t] ~ dbern(pX.missing[t]/K.mvhypgeom)
}
for (t in 1:N) {
X[t] <- X.obs[t] + X.missing[t]
likX[t] <- dbin( X[t], p, n[t])
ones1[t] ~ dbern( likX[t] / K)
}
}
',
file = {example.model <- tempfile()},
sep = ''
)
Data
simBinTS <- function(n, p , nMissing) {
X.full <- X <- rbinom(N, size = n, prob = p)
for (i in seq_len(nMissing)) {
idx <- sample(1:N, size = 1, prob = X)
X[idx] <- X[idx] - 1
}
return(data.frame(n = n, X = X, X.full = X.full))
}
N <- 10
p <- 0.3
set.seed(123)
n <- rpois(N, lambda = 30)
nMissing <- 10
missRate <- 1/10
ts <- simBinTS(p = p, n = n, nMissing = nMissing)
X.obs <- ts$X
n <- ts$n
X.full <- ts$X.full
ones1 <- rep(1,N)
ones2 <- rep(1,N)
jags.inits <- function(){
list(X.missing = X.full-X.obs)
}
Call
library("R2jags")
jags(data = list("X.obs", "n", "N", "nMissing", "ones1", "ones2", "missRate"),
inits = jags.inits,
parameters.to.save = "p",
model.file = example.model,
n.chains = 3,
n.iter = 1000,
n.burnin = 500,
n.thin = 1,
progress.bar = "none")
Output (large number of repeats of warning trimmed - again these are printed as function output rather than as warning messages)
value out of range in 'lgamma'
value out of range in 'lgamma'
value out of range in 'lgamma'
value out of range in 'lgamma'
value out of range in 'lgamma'
value out of range in 'lgamma'
Inference for Bugs model at "D:\Users\fish\AppData\Local\Temp\RtmpWufTIC\file1614244456e1", fit using jags,
3 chains, each with 1000 iterations (first 500 discarded)
n.sims = 1500 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat
p 0.331 0.027 0.280 0.312 0.330 0.348 0.388 1.006
deviance 812.379 2.761 808.165 810.345 811.941 814.103 818.729 1.007
n.eff
p 1300
deviance 670
For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
DIC info (using the rule, pD = var(deviance)/2)
pD = 3.8 and DIC = 816.2
DIC is an estimate of expected predictive error (lower deviance is better).
