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Last data update: 2014.03.03 |
Run ‘JAGS’ from RDescriptionThe Usage
jags(data, inits, parameters.to.save, model.file="model.bug",
n.chains=3, n.iter=2000, n.burnin=floor(n.iter/2),
n.thin=max(1, floor((n.iter - n.burnin) / 1000)),
DIC=TRUE, working.directory=NULL, jags.seed = 123,
refresh = n.iter/50, progress.bar = "text", digits=5,
RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry",
"Super-Duper", "Mersenne-Twister"),
jags.module = c("glm","dic")
)
jags.parallel(data, inits, parameters.to.save, model.file = "model.bug",
n.chains = 2, n.iter = 2000, n.burnin = floor(n.iter/2),
n.thin = max(1, floor((n.iter - n.burnin)/1000)),
n.cluster= n.chains, DIC = TRUE,
working.directory = NULL, jags.seed = 123, digits=5,
RNGname = c("Wichmann-Hill", "Marsaglia-Multicarry",
"Super-Duper", "Mersenne-Twister"),
jags.module = c("glm","dic"),
export_obj_names=NULL,
envir = .GlobalEnv
)
jags2(data, inits, parameters.to.save, model.file="model.bug",
n.chains=3, n.iter=2000, n.burnin=floor(n.iter/2),
n.thin=max(1, floor((n.iter - n.burnin) / 1000)),
DIC=TRUE, jags.path="",
working.directory=NULL, clearWD=TRUE,
refresh = n.iter/50)
Arguments
.
DetailsTo run:
BUGS version support:
Author(s)Yu-Sung Su suyusung@tsinghua.edu.cn, Masanao Yajima yajima@stat.columbia.edu ReferencesPlummer, Martyn (2003) “JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling.” http://citeseer.ist.psu.edu/plummer03jags.html. Gelman, A., Carlin, J. B., Stern, H.S., Rubin, D.B. (2003) Bayesian Data Analysis, 2nd edition, CRC Press. Sibylle Sturtz and Uwe Ligges and Andrew Gelman. (2005). “R2WinBUGS: A Package for Running WinBUGS from R.” Journal of Statistical Software 3 (12): 1–6. Examples
# An example model file is given in:
model.file <- system.file(package="R2jags", "model", "schools.txt")
# Let's take a look:
file.show(model.file)
# you can also write BUGS model as a R function, see below:
#=================#
# initialization #
#=================#
# data
J <- 8.0
y <- c(28.4,7.9,-2.8,6.8,-0.6,0.6,18.0,12.2)
sd <- c(14.9,10.2,16.3,11.0,9.4,11.4,10.4,17.6)
jags.data <- list("y","sd","J")
jags.params <- c("mu","sigma","theta")
jags.inits <- function(){
list("mu"=rnorm(1),"sigma"=runif(1),"theta"=rnorm(J))
}
## You can input data in 4 ways
## 1) data as list of character
jagsfit <- jags(data=list("y","sd","J"), inits=jags.inits, jags.params,
n.iter=10, model.file=model.file)
## 2) data as character vector of names
jagsfit <- jags(data=c("y","sd","J"), inits=jags.inits, jags.params,
n.iter=10, model.file=model.file)
## 3) data as named list
jagsfit <- jags(data=list(y=y,sd=sd,J=J), inits=jags.inits, jags.params,
n.iter=10, model.file=model.file)
## 4) data as a file
fn <- "tmpbugsdata.txt"
dump(c("y","sd","J"), file=fn)
jagsfit <- jags(data=fn, inits=jags.inits, jags.params,
n.iter=10, model.file=model.file)
unlink("tmpbugsdata.txt")
## You can write bugs model in R as a function
schoolsmodel <- function() {
for (j in 1:J){ # J=8, the number of schools
y[j] ~ dnorm (theta[j], tau.y[j]) # data model: the likelihood
tau.y[j] <- pow(sd[j], -2) # tau = 1/sigma^2
}
for (j in 1:J){
theta[j] ~ dnorm (mu, tau) # hierarchical model for theta
}
tau <- pow(sigma, -2) # tau = 1/sigma^2
mu ~ dnorm (0.0, 1.0E-6) # noninformative prior on mu
sigma ~ dunif (0, 1000) # noninformative prior on sigma
}
jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params,
n.iter=10, model.file=schoolsmodel)
#===============================#
# RUN jags and postprocessing #
#===============================#
jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params,
n.iter=5000, model.file=model.file)
# Run jags parallely, no progress bar. R may be frozen for a while,
# Be patient. Currenlty update afterward does not run parallelly
#
jagsfit.p <- jags.parallel(data=jags.data, inits=jags.inits, jags.params,
n.iter=5000, model.file=model.file)
# display the output
print(jagsfit)
plot(jagsfit)
# traceplot
traceplot(jagsfit.p)
traceplot(jagsfit)
# or to use some plots in coda
# use as.mcmmc to convert rjags object into mcmc.list
jagsfit.mcmc <- as.mcmc(jagsfit.p)
jagsfit.mcmc <- as.mcmc(jagsfit)
## now we can use the plotting methods from coda
#require(lattice)
#xyplot(jagsfit.mcmc)
#densityplot(jagsfit.mcmc)
# if the model does not converge, update it!
jagsfit.upd <- update(jagsfit, n.iter=100)
print(jagsfit.upd)
print(jagsfit.upd, intervals=c(0.025, 0.5, 0.975))
plot(jagsfit.upd)
# before update parallel jags object, do recompile it
recompile(jagsfit.p)
jagsfit.upd <- update(jagsfit.p, n.iter=100)
# or auto update it until it converges! see ?autojags for details
# recompile(jagsfit.p)
jagsfit.upd <- autojags(jagsfit.p)
jagsfit.upd <- autojags(jagsfit)
# to get DIC or specify DIC=TRUE in jags() or do the following#
dic.samples(jagsfit.upd$model, n.iter=1000, type="pD")
# attach jags object into search path see "attach.bugs" for details
attach.jags(jagsfit.upd)
# this will show a 3-way array of the bugs.sim object, for example:
mu
# detach jags object into search path see "attach.bugs" for details
detach.jags()
# to pick up the last save session
# for example, load("RWorkspace.Rdata")
recompile(jagsfit)
jagsfit.upd <- update(jagsfit, n.iter=100)
recompile(jagsfit.p)
jagsfit.upd <- update(jagsfit, n.iter=100)
#=============#
# using jags2 #
#=============#
## jags can be run and produces coda files, but cannot be updated once it's done
## You may need to edit "jags.path" to make this work,
## also you need a write access in the working directory:
## e.g. setwd("d:/")
## NOT RUN HERE
## Not run:
jagsfit <- jags2(data=jags.data, inits=jags.inits, jags.params,
n.iter=5000, model.file=model.file)
print(jagsfit)
plot(jagsfit)
# or to use some plots in coda
# use as.mcmmc to convert rjags object into mcmc.list
jagsfit.mcmc <- as.mcmc.list(jagsfit)
traceplot(jagsfit.mcmc)
#require(lattice)
#xyplot(jagsfit.mcmc)
#densityplot(jagsfit.mcmc)
## End(Not run)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(R2jags)
Loading required package: rjags
Loading required package: coda
Linked to JAGS 4.1.0
Loaded modules: basemod,bugs
Attaching package: 'R2jags'
The following object is masked from 'package:coda':
traceplot
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/R2jags/jags.Rd_%03d_medium.png", width=480, height=480)
> ### Name: jags
> ### Title: Run 'JAGS' from R
> ### Aliases: rjags-class rjags.parallel-class jags jags2 jags.parallel
> ### Keywords: interface models
>
> ### ** Examples
>
> # An example model file is given in:
> model.file <- system.file(package="R2jags", "model", "schools.txt")
> # Let's take a look:
> file.show(model.file)
# Bugs model file for 8 schools analysis from Section 5.5 of "Bayesian Data
# Analysis". Save this into the file "schools.bug" in your R working directory.
model {
for (j in 1:J){ # J=8, the number of schools
y[j] ~ dnorm (theta[j], tau.y[j]) # data model: the likelihood
tau.y[j] <- pow(sd[j], -2) # tau = 1/sigma^2
}
for (j in 1:J){
theta[j] ~ dnorm (mu, tau) # hierarchical model for theta
}
tau <- pow(sigma, -2) # tau = 1/sigma^2
mu ~ dnorm (0.0, 1.0E-6) # noninformative prior on mu
sigma ~ dunif (0, 1000) # noninformative prior on sigma
}
> # you can also write BUGS model as a R function, see below:
>
> #=================#
> # initialization #
> #=================#
>
> # data
> J <- 8.0
> y <- c(28.4,7.9,-2.8,6.8,-0.6,0.6,18.0,12.2)
> sd <- c(14.9,10.2,16.3,11.0,9.4,11.4,10.4,17.6)
>
>
> jags.data <- list("y","sd","J")
> jags.params <- c("mu","sigma","theta")
> jags.inits <- function(){
+ list("mu"=rnorm(1),"sigma"=runif(1),"theta"=rnorm(J))
+ }
>
> ## You can input data in 4 ways
> ## 1) data as list of character
> jagsfit <- jags(data=list("y","sd","J"), inits=jags.inits, jags.params,
+ n.iter=10, model.file=model.file)
module glm loaded
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
>
> ## 2) data as character vector of names
> jagsfit <- jags(data=c("y","sd","J"), inits=jags.inits, jags.params,
+ n.iter=10, model.file=model.file)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
>
> ## 3) data as named list
> jagsfit <- jags(data=list(y=y,sd=sd,J=J), inits=jags.inits, jags.params,
+ n.iter=10, model.file=model.file)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
>
> ## 4) data as a file
> fn <- "tmpbugsdata.txt"
> dump(c("y","sd","J"), file=fn)
> jagsfit <- jags(data=fn, inits=jags.inits, jags.params,
+ n.iter=10, model.file=model.file)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
> unlink("tmpbugsdata.txt")
>
> ## You can write bugs model in R as a function
>
> schoolsmodel <- function() {
+ for (j in 1:J){ # J=8, the number of schools
+ y[j] ~ dnorm (theta[j], tau.y[j]) # data model: the likelihood
+ tau.y[j] <- pow(sd[j], -2) # tau = 1/sigma^2
+ }
+ for (j in 1:J){
+ theta[j] ~ dnorm (mu, tau) # hierarchical model for theta
+ }
+ tau <- pow(sigma, -2) # tau = 1/sigma^2
+ mu ~ dnorm (0.0, 1.0E-6) # noninformative prior on mu
+ sigma ~ dunif (0, 1000) # noninformative prior on sigma
+ }
>
> jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params,
+ n.iter=10, model.file=schoolsmodel)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
>
>
> #===============================#
> # RUN jags and postprocessing #
> #===============================#
> jagsfit <- jags(data=jags.data, inits=jags.inits, jags.params,
+ n.iter=5000, model.file=model.file)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
>
> # Run jags parallely, no progress bar. R may be frozen for a while,
> # Be patient. Currenlty update afterward does not run parallelly
> #
> jagsfit.p <- jags.parallel(data=jags.data, inits=jags.inits, jags.params,
+ n.iter=5000, model.file=model.file)
>
> # display the output
> print(jagsfit)
Inference for Bugs model at "/home/ddbj/local/lib64/R/library/R2jags/model/schools.txt", fit using jags,
3 chains, each with 5000 iterations (first 2500 discarded), n.thin = 2
n.sims = 3750 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
mu 8.283 5.250 -2.546 5.090 8.402 11.748 18.141 1.002 1100
sigma 7.265 5.940 0.262 2.874 6.028 10.141 22.009 1.003 760
theta[1] 12.292 8.516 -2.159 6.889 11.255 16.444 32.125 1.003 1000
theta[2] 8.271 6.535 -4.883 4.294 8.490 12.464 21.533 1.003 960
theta[3] 6.198 8.339 -12.728 1.775 7.022 11.637 20.903 1.001 3700
theta[4] 7.877 6.719 -6.389 3.825 8.143 12.145 20.983 1.001 3800
theta[5] 5.284 6.928 -10.283 1.103 6.012 10.189 16.532 1.001 3800
theta[6] 6.245 7.168 -9.519 2.031 6.987 11.191 18.653 1.001 3100
theta[7] 11.189 6.958 -1.956 6.511 10.845 15.111 26.792 1.005 430
theta[8] 9.017 8.180 -7.128 4.358 9.013 13.489 27.227 1.001 3800
deviance 60.453 2.221 56.907 59.069 60.085 61.471 66.066 1.002 1700
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 = 2.5 and DIC = 62.9
DIC is an estimate of expected predictive error (lower deviance is better).
> plot(jagsfit)
>
> # traceplot
> traceplot(jagsfit.p)
> traceplot(jagsfit)
>
> # or to use some plots in coda
> # use as.mcmmc to convert rjags object into mcmc.list
> jagsfit.mcmc <- as.mcmc(jagsfit.p)
> jagsfit.mcmc <- as.mcmc(jagsfit)
> ## now we can use the plotting methods from coda
> #require(lattice)
> #xyplot(jagsfit.mcmc)
> #densityplot(jagsfit.mcmc)
>
> # if the model does not converge, update it!
> jagsfit.upd <- update(jagsfit, n.iter=100)
> print(jagsfit.upd)
Inference for Bugs model at "/home/ddbj/local/lib64/R/library/R2jags/model/schools.txt", fit using jags,
3 chains, each with 100 iterations (first 0 discarded)
n.sims = 300 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
mu 7.846 5.229 -1.867 3.812 8.146 10.870 17.368 1.052 100
sigma 4.953 5.859 0.181 0.975 2.714 6.147 20.279 1.242 12
theta[1] 10.395 7.811 -1.199 4.954 8.794 15.013 33.524 1.052 54
theta[2] 7.453 6.173 -3.307 3.412 7.256 10.825 18.544 1.039 140
theta[3] 6.636 7.380 -11.524 3.385 7.334 11.051 18.837 1.049 140
theta[4] 8.025 5.771 -2.394 3.919 7.788 11.092 18.337 1.048 200
theta[5] 6.524 6.097 -5.623 3.271 6.864 9.808 17.291 1.032 120
theta[6] 6.926 6.655 -9.884 3.466 7.571 10.718 17.758 1.068 57
theta[7] 10.093 6.240 0.104 5.866 8.882 14.311 24.388 1.048 44
theta[8] 8.559 7.353 -6.273 3.846 8.056 11.874 26.072 1.018 300
deviance 60.619 1.913 57.453 59.395 60.246 61.544 64.660 1.135 19
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 = 1.6 and DIC = 62.3
DIC is an estimate of expected predictive error (lower deviance is better).
> print(jagsfit.upd, intervals=c(0.025, 0.5, 0.975))
Inference for Bugs model at "/home/ddbj/local/lib64/R/library/R2jags/model/schools.txt", fit using jags,
3 chains, each with 100 iterations (first 0 discarded)
n.sims = 300 iterations saved
mu.vect sd.vect 2.5% 50% 97.5% Rhat n.eff
mu 7.846 5.229 -1.867 8.146 17.368 1.052 100
sigma 4.953 5.859 0.181 2.714 20.279 1.242 12
theta[1] 10.395 7.811 -1.199 8.794 33.524 1.052 54
theta[2] 7.453 6.173 -3.307 7.256 18.544 1.039 140
theta[3] 6.636 7.380 -11.524 7.334 18.837 1.049 140
theta[4] 8.025 5.771 -2.394 7.788 18.337 1.048 200
theta[5] 6.524 6.097 -5.623 6.864 17.291 1.032 120
theta[6] 6.926 6.655 -9.884 7.571 17.758 1.068 57
theta[7] 10.093 6.240 0.104 8.882 24.388 1.048 44
theta[8] 8.559 7.353 -6.273 8.056 26.072 1.018 300
deviance 60.619 1.913 57.453 60.246 64.660 1.135 19
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 = 1.6 and DIC = 62.3
DIC is an estimate of expected predictive error (lower deviance is better).
> plot(jagsfit.upd)
>
> # before update parallel jags object, do recompile it
> recompile(jagsfit.p)
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 10
Total graph size: 50
Initializing model
> jagsfit.upd <- update(jagsfit.p, n.iter=100)
>
>
>
> # or auto update it until it converges! see ?autojags for details
> # recompile(jagsfit.p)
> jagsfit.upd <- autojags(jagsfit.p)
> jagsfit.upd <- autojags(jagsfit)
>
> # to get DIC or specify DIC=TRUE in jags() or do the following#
> dic.samples(jagsfit.upd$model, n.iter=1000, type="pD")
Mean deviance: 60.52
penalty 2.855
Penalized deviance: 63.38
>
> # attach jags object into search path see "attach.bugs" for details
> attach.jags(jagsfit.upd)
>
> # this will show a 3-way array of the bugs.sim object, for example:
> mu
[,1]
[1,] 14.881464571
[2,] 3.604098011
[3,] 15.978212680
[4,] 4.959063873
[5,] 3.151922559
[6,] 5.782175675
[7,] 2.047465119
[8,] 8.678511782
[9,] 5.654278389
[10,] 7.344493749
[11,] 5.782657710
[12,] 11.436401939
[13,] 14.090212575
[14,] 14.496716772
[15,] 8.899458286
[16,] 3.960753808
[17,] 7.681111588
[18,] 8.776018331
[19,] 7.230958446
[20,] 15.503152856
[21,] 3.337025812
[22,] 7.441212500
[23,] 11.572551700
[24,] 7.141389653
[25,] 5.970881192
[26,] 11.516674412
[27,] 9.907720219
[28,] 7.638678098
[29,] 7.440112271
[30,] 7.449259317
[31,] 12.551402640
[32,] 20.496298267
[33,] 7.997844720
[34,] 2.528411333
[35,] 4.708914468
[36,] 8.731331171
[37,] 6.501914071
[38,] 5.925110663
[39,] 5.519476276
[40,] 7.626505880
[41,] 7.346637301
[42,] 14.400361473
[43,] 10.868374241
[44,] 4.209502314
[45,] 3.653545690
[46,] 12.820979314
[47,] 5.294160840
[48,] 11.035957712
[49,] 9.532682836
[50,] 17.130237346
[51,] 14.248520245
[52,] 16.732664022
[53,] 3.332926590
[54,] 5.900265166
[55,] 7.939522140
[56,] 15.899177878
[57,] 9.239121149
[58,] 2.319339925
[59,] 11.789126204
[60,] 7.870244087
[61,] 1.479283867
[62,] 8.636186989
[63,] 4.515985092
[64,] 7.775149012
[65,] 7.477989163
[66,] 10.905278571
[67,] 6.386348532
[68,] 10.184845353
[69,] 6.972813128
[70,] -1.204632133
[71,] 12.290723046
[72,] 5.336024790
[73,] 5.629053137
[74,] 12.911307524
[75,] 4.263402388
[76,] 11.756942175
[77,] 6.851328701
[78,] 6.694269463
[79,] 11.231916631
[80,] 11.420441026
[81,] 16.056803464
[82,] 5.439035101
[83,] 10.717188466
[84,] 7.267940890
[85,] 4.094834409
[86,] 5.360866882
[87,] 14.522174998
[88,] 9.857490869
[89,] 4.176488846
[90,] 17.942807242
[91,] 12.066728764
[92,] 4.979643715
[93,] 4.127549412
[94,] 7.703299903
[95,] 4.715101284
[96,] 9.053416477
[97,] -4.999941251
[98,] 10.006114228
[99,] 12.764989490
[100,] 19.122006828
[101,] 2.654536643
[102,] 7.830745126
[103,] 1.973625602
[104,] 16.570034980
[105,] 8.271698087
[106,] 6.816608991
[107,] 5.707964386
[108,] 8.113976474
[109,] 11.769146129
[110,] 0.617720007
[111,] 15.057564291
[112,] 6.323683265
[113,] 0.965988055
[114,] 9.594935048
[115,] 2.160081592
[116,] 2.261093221
[117,] 11.976691215
[118,] 17.222454300
[119,] 10.654743549
[120,] 6.769094417
[121,] 10.092302882
[122,] 10.768852550
[123,] -2.358310837
[124,] 10.573310348
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[830,] 3.737813315
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[843,] 12.697205257
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[846,] 21.5
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and