Nothing
at_home <- identical( Sys.getenv("AT_HOME"), "TRUE" )
set.seed(123)
data(longley)
data <- list(gnp=longley$GNP, employed=longley$Employed, n=nrow(longley))
modfile <- tempfile()
writeLines("
model{
for (i in 1:n){
employed[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta*gnp[i]
}
alpha ~ dnorm(0, 0.00001)
beta ~ dnorm(0, 0.00001)
sigma ~ dunif(0,1000)
tau <- pow(sigma,-2)
}", con=modfile)
inits <- function(){
list(alpha=rnorm(1,0,1),beta=rnorm(1,0,1),sigma=runif(1,0,3))
}
params <- c('alpha','beta','sigma', 'mu')
nul <- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE))
# Remove time/date based elements
if(at_home){
ref <- readRDS("autojags_ref.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_equal(out[-c(17,18,21)], ref[-c(17,18,21)])
}
# codaOnly---------------------------------------------------------------------
nul<- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE, codaOnly=c("mu")))
# Remove time/date based elements
if(at_home){
ref <- readRDS("autojags_ref_codaonly.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_equal(out[-c(17,18,21)], ref[-c(17,18,21)])
}
# Check recovery after process_output errors-----------------------------------
# Setting DIC to -999 forces process_output to error for testing
expect_message(nul<- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE, codaOnly=c("mu"), DIC=-999)))
expect_inherits(out, "jagsUIbasic")
expect_equal(coda::varnames(out$samples),
c("alpha","beta", "sigma", paste0("mu[",1:16,"]"),"deviance"))
expect_equal(names(out), c("samples", "model"))
# Save all iterations----------------------------------------------------------
set.seed(123)
params <- c('alpha','beta','sigma', 'mu')
nul <- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 1, verbose=TRUE, save.all.iter=TRUE))
# Runs three updates of 10 iterations each
expect_true(grepl("Note: ALL iterations", nul[6]))
expect_true(grepl("Update 3", nul[10]))
expect_true(nul[11] == "")
# All are combined to yield 30 total iterations in each chain
expect_equal(coda::niter(out$samples), 30)
if(at_home){
ref <- readRDS("autojags_ref_alliter.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_equal(out[-c(17,18,21)], ref[-c(17,18,21)])
}
# Parallel----------------------------------------------------------
if(at_home){
set.seed(123)
nul <- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=TRUE, parallel=TRUE, save.all.iter=FALSE))
# Runs two updates of 40 iterations each
expect_true(grepl("Update 13", nul[18]))
expect_equal(nul[19], "")
ref <- readRDS("autojags_ref.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_equal(out[-c(17,18,20:22)], ref[-c(17,18,20:22)])
# Save all iter
set.seed(123)
params <- c('alpha','beta','sigma', 'mu')
nul <- capture.output(
out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 1, verbose=TRUE, parallel=TRUE, save.all.iter=TRUE))
expect_true(grepl("Update 3", nul[10]))
expect_equal(nul[11], "")
expect_equal(coda::niter(out$samples), 30)
ref <- readRDS("autojags_ref_alliter.Rds")
expect_equal(out[-c(17,18,20:22)], ref[-c(17,18,20:22)])
}
# test.Rhat--------------------------------------------------------------------
samples <- readRDS('coda_samples.Rds')
rhats <- sapply(1:coda::nvar(samples), function(i) coda::gelman.diag(samples[,i],
autoburnin=FALSE)$psrf[1])
# None above 1.1
expect_false(any(rhats > 1.1))
expect_false(jagsUI:::test.Rhat(samples, 1.1, params.omit=NULL, verbose=FALSE))
# Sigma is above 1.05
expect_true(any(rhats > 1.05))
expect_true(jagsUI:::test.Rhat(samples, 1.05, params.omit=NULL, verbose=FALSE))
nul <- capture.output(jagsUI:::test.Rhat(samples, 1.05, params.omit=NULL, verbose=TRUE))
expect_true(grepl("sigma", nul[1]))
# Exclude sigma
expect_false(jagsUI:::test.Rhat(samples, 1.05, params.omit="sigma", verbose=FALSE))
# Test input errors------------------------------------------------------------
expect_error(out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE, seed=123))
expect_error(out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 1, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE))
expect_warning(out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=50,
iter.increment=10, n.thin = 2, verbose=FALSE, max.iter=40))
nul <- capture.output(out <- autojags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.burnin=0,
iter.increment=10, n.thin = 2, verbose=TRUE, max.iter=5))
expect_equal(nul[8], "Maximum iterations reached.")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.