Suppress JAGS "value out of range" warnings in R

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, or capture.output.
Altering the jags.model call within jags to jags.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).

Solution

The issue is with the jags package that R2Jags relies upon.

jags uses printf not fprintf to display warnings. Jags doesn't send the warning to stderr, it sends the warning to console not stderr. Hence, the R console cannot filter the warnings.

R2Jags relies upon the jags application. I downloaded the jags source code from Sourceforge for JAGS-4.3.0, compiled and installed the library. This allowed me to trace through the code and identify that jags throws a warning via:

src/jrmath/lgamma.c:74 via ML_ERROR(ME_RANGE, "lgamma");

this resolves through to

src/jrmath/nmath.h:138 via MATHLIB_WARNING(msg, s);

which resolves to

src/jrmath/nmath.h:81
#define MATHLIB_WARNING(fmt,x) printf(fmt,x)

the issue here is that printf is used not fprint(stderr,...), this can be patched thus:

Quick Solution:

If you wish to resolve quickly you can download the source and apply the following fix:

$ diff nmath.h.orig nmath.h
81c81
< #define MATHLIB_WARNING(fmt,x)        printf(fmt,x)
---
> #define MATHLIB_WARNING(fmt,x)        fprintf(stderr,fmt,x)

now you can compile and install the jags library:

>./configure
>sudo make uninstall && sudo make install

with this done we can uninstall the R2jags library, reinstall it and repress stderr using R CMD with stderr redirect...

R CMD ./50635735.R 2> /dev/null

Code Example

#!/usr/bin/env Rscript

library("R2jags")

source("./model.R") # Source Model
source("./simbits.R") # Source simBinTS code...

jags.data <- list("X.obs", "n", "N", "nMissing", "ones1", "ones2", "missRate")

model <- jags(data = jags.data,
              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")

model

Console Output:

Unmodified jags

$ R CMD ./50635735.R 2> /dev/null
1 checking for pkg-config... /usr/local/bin/pkg-config
2 configure: Setting compile and link flags according to pkg-config
3 configure: Compile flags are -I/usr/local/include/JAGS
4 configure: Link flags are -L/usr/local/lib -ljags
5 checking for gcc... ccache clang
6 checking whether we are using the GNU C compiler... no
7 checking whether ccache clang accepts -g... no
8 checking for ccache clang option to accept ISO C89... unsupported
9 checking for jags_version in -ljags... yes
10 checking version of JAGS library... OK
11 configure: creating ./config.status
12 config.status: creating src/Makevars
13 configure: creating ./config.status
14 config.status: creating src/Makevars
15 config.status: creating R/unix/zzz.R
16 ccache clang++ -I"/usr/local/Cellar/r/3.5.0_1/lib/R/include" -DNDEBUG -I/usr/local/include/JAGS   -I/usr/local/opt/gettext/include -I/usr/
17 ccache clang++ -I"/usr/local/Cellar/r/3.5.0_1/lib/R/include" -DNDEBUG -I/usr/local/include/JAGS   -I/usr/local/opt/gettext/include -I/usr/
18 ccache clang++ -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/usr/loc
19 Compiling model graph
20    Resolving undeclared variables
21    Allocating nodes
22 Graph information:
23    Observed stochastic nodes: 21
24    Unobserved stochastic nodes: 11
25    Total graph size: 174
26
27 Initializing model
28
29 value out of range in 'lgamma'
30 value out of range in 'lgamma'
31 value out of range in 'lgamma'
32 value out of range in 'lgamma'
...
...
...
10089 value out of range in 'lgamma'
10090 Inference for Bugs model at "/var/folders/md/03gdc4c14z18kbqwpfh4jdfc0000gp/T//Rtmp3P3FrI/file868156b0697", fit using jags,
10091  3 chains, each with 1000 iterations (first 500 discarded)
10092  n.sims = 1500 iterations saved
10093          mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
10094 p          0.333   0.027   0.281   0.315   0.332   0.350   0.391 1.003   590
10095 deviance 812.168   2.720 808.036 810.199 811.778 813.737 818.236 1.036    66
10096
10097 For each parameter, n.eff is a crude measure of effective sample size,
10098 and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
10099
10100 DIC info (using the rule, pD = var(deviance)/2)
10101 pD = 3.6 and DIC = 815.8
10102
10103
10104
10105
10106
10107
10108
10109 BDIC is an estimate of expected predictive error (lower deviance is better).

Modified Jags framework

$ R CMD ./50635735.R 2> /dev/null
checking for pkg-config... /usr/local/bin/pkg-config
configure: Setting compile and link flags according to pkg-config
configure: Compile flags are -I/usr/local/include/JAGS
configure: Link flags are -L/usr/local/lib -ljags
checking for gcc... ccache clang
checking whether we are using the GNU C compiler... no
checking whether ccache clang accepts -g... no
checking for ccache clang option to accept ISO C89... unsupported
checking for jags_version in -ljags... yes
checking version of JAGS library... OK
configure: creating ./config.status
config.status: creating src/Makevars
configure: creating ./config.status
config.status: creating src/Makevars
config.status: creating R/unix/zzz.R
ccache clang++ -I"/usr/local/Cellar/r/3.5.0_1/lib/R/include" -DNDEBUG -I/usr/local/include/JAGS   -I/usr/local/opt/gettext/include -I/usr/local/opt/readline/include -I/usr/local/include   -fPIC  -g -O2  -c jags.cc -o jags.o
ccache clang++ -I"/usr/local/Cellar/r/3.5.0_1/lib/R/include" -DNDEBUG -I/usr/local/include/JAGS   -I/usr/local/opt/gettext/include -I/usr/local/opt/readline/include -I/usr/local/include   -fPIC  -g -O2  -c parallel.cc -o parallel.o
ccache clang++ -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/usr/local/opt/gettext/lib -L/usr/local/opt/readline/lib -L/usr/local/lib -L/usr/local/Cellar/r/3.5.0_1/lib/R/lib -L/usr/local/opt/gettext/lib -L/usr/local/opt/readline/lib -L/usr/local/lib -o rjags.so jags.o parallel.o -L/usr/local/lib -ljags -L/usr/local/opt/icu4c/lib -L/usr/local/lib -L/usr/local/Cellar/r/3.5.0_1/lib/R/lib -lR -lintl -Wl,-framework -Wl,CoreFoundation
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 21
   Unobserved stochastic nodes: 11
   Total graph size: 174

Initializing model

Inference for Bugs model at "/var/folders/md/03gdc4c14z18kbqwpfh4jdfc0000gp/T//RtmpI80TnH/file8e70516d6f34", 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 n.eff
p          0.333   0.027   0.281   0.315   0.332   0.350   0.391 1.003   590
deviance 812.168   2.720 808.036 810.199 811.778 813.737 818.236 1.036    66

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 = 3.6 and DIC = 815.8
DIC is an estimate of expected predictive error (lower deviance is better).

Long-Term Solution

Submit a bug and propose fix via SourceForge.