我使用R2jags包的jags函数（使用rjags包运行JAGS）在R中运行大量JAGS模型。
我在控制台中打印了很多警告：
value out of range in 'lgamma'
打印这些警告似乎会严重影响计算时间。我该如何压制这个？
警告打印为输出，而不是R警告。
我尝试过的东西不起作用包括：
在try(..., silent = TRUE)，suppressWarnings中收听我的电话，
invisible或capture.output。
将jags.model内的jags电话改为jags.model(...,
quiet = TRUE)。
这种现象也是noted elsewhere，我只是想把它关闭，以减少从不必要的打印到控制台的计算量。
有什么建议吗？
这是一个基于an example of the same issue on sourceforge的长而可重复的示例。对此长度表示歉意但我无法在任何较小的玩具模型中复制它。我不在乎这个特定的模型，但它简单地复制了 的问题：
模型
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 = ''
)
数据
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)
}
呼叫
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")
输出 （大量重复的警告被修剪 - 再次打印为功能输出而不是警告信息）
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).
答案 0 :(得分：3)
printf而非fprintf来显示警告。 Jags不会向stderr发送警告，它会将警告发送到控制台而不是stderr。因此，R控制台无法过滤警告。 R2Jags依赖于jags应用程序。我从Sourceforge下载了JAGS-4.3.0的jags源代码，编译并安装了库。这使我能够跟踪代码并确定jags通过以下方式发出警告：
src/jrmath/lgamma.c:74通过ML_ERROR(ME_RANGE, "lgamma"); 这解决了
src/jrmath/nmath.h:138通过MATHLIB_WARNING(msg, s); 解析为
src/jrmath/nmath.h:81 #define MATHLIB_WARNING(fmt,x) printf(fmt,x) 此处的问题是使用的printf不是fprint(stderr,...)，因此可以修补：
如果您希望快速解决，可以下载源代码并应用以下修复程序：
$ diff nmath.h.orig nmath.h
81c81
< #define MATHLIB_WARNING(fmt,x) printf(fmt,x)
---
> #define MATHLIB_WARNING(fmt,x) fprintf(stderr,fmt,x)
现在您可以编译并安装jags库：
>./configure
>sudo make uninstall && sudo make install
完成此操作后，我们可以卸载R2jags库，重新安装它并使用带有stderr重定向的R CMD来压制stderr ...
R CMD ./50635735.R 2> /dev/null
#!/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
$ 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).
$ 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).
提交错误并通过SourceForge提出修复建议。