在 R 中抑制 JAGS "value out of range" 警告
Suppress JAGS "value out of range" warnings in R
我正在 运行使用 R2jags 包的 jags 函数（它使用 rjags 包来 运行 JAGS）在 R 中建立大量的 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。我该如何抑制它？
警告打印为输出，而不是 R 警告。
我试过但不起作用的方法包括：
在 try(..., silent = TRUE)、suppressWarnings 中结束我的通话
invisible，或 capture.output。
将 jags 中的 jags.model 调用更改为 jags.model(...,
quiet = TRUE)。
这种现象也是 noted elsewhere，我只是想关闭它以减少从大量不必要的打印到控制台的计算负载。
有什么建议吗？
这是一个基于 an example of the same issue on sourceforge 的长但可重现的示例。很抱歉这么长，但我无法在任何较小的玩具模型中复制它。我不太关心这个特定的模型，但它 合理地 简单地复制了问题：
型号
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)
}
通话
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 - 这些再次打印为函数输出而不是警告消息）
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).
问题出在 jags package that R2Jags 依赖上。
- jags 使用
printf 而不是 fprintf 来显示警告。 Jags 不会将警告发送到 stderr，而是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 relies upon the jags申请。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)
这里的问题是 printf 没有被使用 fprint(stderr,...)，可以这样修补：
快速解决方案：
如果您希望快速解决问题，您可以下载源代码并应用以下修复：
$ diff nmath.h.orig nmath.h
81c81
< #define MATHLIB_WARNING(fmt,x) printf(fmt,x)
---
> #define MATHLIB_WARNING(fmt,x) fprintf(stderr,fmt,x)
现在可以编译安装jags库了：
>./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 抑制 stderr...
R CMD ./50635735.R 2> /dev/null
代码示例
#!/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
控制台输出：
未修改的锯齿
$ 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).
修改后的 Jags 框架
$ 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).
长期解决方案
通过 SourceForge 提交错误并提出修复建议。
我正在 运行使用 R2jags 包的 jags 函数（它使用 rjags 包来 运行 JAGS）在 R 中建立大量的 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。我该如何抑制它？
警告打印为输出，而不是 R 警告。
我试过但不起作用的方法包括：
在
try(..., silent = TRUE)、suppressWarnings中结束我的通话invisible，或capture.output。将
jags中的jags.model调用更改为jags.model(..., quiet = TRUE)。
这种现象也是 noted elsewhere，我只是想关闭它以减少从大量不必要的打印到控制台的计算负载。
有什么建议吗？
这是一个基于 an example of the same issue on sourceforge 的长但可重现的示例。很抱歉这么长，但我无法在任何较小的玩具模型中复制它。我不太关心这个特定的模型，但它 合理地 简单地复制了问题：
型号
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)
}
通话
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 - 这些再次打印为函数输出而不是警告消息）
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).
问题出在 jags package that R2Jags 依赖上。
- jags 使用
printf而不是fprintf来显示警告。 Jags 不会将警告发送到stderr，而是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 relies upon the jags申请。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)
这里的问题是 printf 没有被使用 fprint(stderr,...)，可以这样修补：
快速解决方案：
如果您希望快速解决问题，您可以下载源代码并应用以下修复：
$ diff nmath.h.orig nmath.h
81c81
< #define MATHLIB_WARNING(fmt,x) printf(fmt,x)
---
> #define MATHLIB_WARNING(fmt,x) fprintf(stderr,fmt,x)
现在可以编译安装jags库了：
>./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 抑制 stderr...
R CMD ./50635735.R 2> /dev/null
代码示例
#!/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
控制台输出：
未修改的锯齿
$ 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).
修改后的 Jags 框架
$ 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).
长期解决方案
通过 SourceForge 提交错误并提出修复建议。