1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
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')
out <- jags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 1000,
n.burnin = 500, n.thin = 2, verbose=FALSE)
# Used below
mu2_est <- out$mean$mu[2]
ref <- readRDS("longley_reference_fit.Rds")
# Remove time/date based elements
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_identical(out[-c(17,18,21)], ref[-c(17,18,21)])
# Plots
pdf(NULL)
tp <- traceplot(out, ask=FALSE)
dev.off()
expect_true(is.null(tp))
pdf(NULL)
dp <- densityplot(out, ask=FALSE)
dev.off()
expect_true(is.null(dp))
pdf(NULL)
wp <- whiskerplot(out, "mu")
dev.off()
expect_true(is.null(wp))
pdf(NULL)
pp <- pp.check(out, "alpha", "beta")
dev.off()
expect_equal(pp, 0)
# codaOnly---------------------------------------------------------------------
out <- jags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, codaOnly=c("mu", "sigma"), verbose=FALSE)
ref <- readRDS("reference_codaOnly.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_identical(out[-c(17,18,21)], ref[-c(17,18,21)])
# DIC = FALSE------------------------------------------------------------------
out <- jags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, DIC=FALSE, verbose=FALSE)
expect_false(out$calc.DIC)
ref <- readRDS("reference_noDIC.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_identical(out[-c(15,16,19)], ref[-c(15,16,19)])
# Reordered parameter names----------------------------------------------------
pars_new <- c("mu", "sigma", "alpha", "beta")
out <- jags(data = data, inits = inits, parameters.to.save = pars_new,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, verbose=FALSE)
ref <- readRDS("reference_parsorder.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_identical(out[-c(17,18,21)], ref[-c(17,18,21)])
# Reordered parameter names and no DIC-----------------------------------------
pars_new <- c("mu", "sigma", "alpha", "beta")
out <- jags(data = data, inits = inits, parameters.to.save = pars_new,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, DIC = FALSE, verbose=FALSE)
ref <- readRDS("reference_parsorder_noDIC.Rds")
out$mcmc.info$elapsed.mins <- ref$mcmc.inf$elapsed.mins
expect_identical(out[-c(15,16,19)], ref[-c(15,16,19)])
# Single parameter saved-------------------------------------------------------
pars_new <- c("alpha")
out <- jags(data = data, inits = inits, parameters.to.save = pars_new,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, DIC = FALSE, verbose=FALSE)
expect_equal(nrow(out$summary), 1)
expect_equal(ncol(out$samples[[1]]), 1)
# Another example from Github issues
modfile2 <- tempfile()
writeLines("
model{
for (i in 1:n) {
y[i] ~ dinterval(t[i], c[i, ])
t[i] ~ dweib(v, lambda)
}
v ~ dunif(0, 10)
lambda ~ dunif(0, 10)
}
", con=modfile2)
dataList <- list(n = 7L, c = structure(c(5, 7.5, 8, 9, 8, 8, 8, 15, 15, 15,
15, 10, 10, 10), dim = c(7L, 2L)), y = c(1, 1, 1, 1, 1, 1, 1))
out <- jags(data = dataList, parameters.to.save = c("v", "lambda"),
model.file =modfile2 ,n.chains = 3,n.adapt = 100, n.iter = 100+100,
n.burnin = 100, n.thin = 5, verbose=FALSE)
expect_equal(rownames(out$summary), c("v", "lambda","deviance"))
# Single chain and single iteration--------------------------------------------
out <- jags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 1, n.adapt = 100, n.iter = 100,
n.burnin = 99, n.thin = 1, DIC = FALSE, verbose=FALSE)
expect_true(all(is.na(out$summary[,"sd"])))
expect_true(all(is.na(out$summary[,"Rhat"])))
expect_true(all(is.na(out$summary[,"n.eff"])))
expect_true(all(out$summary["alpha",3:7] == out$summary["alpha",3]))
# Single parameter slice-------------------------------------------------------
set.seed(123)
pars_new <- c("mu[2]")
out <- jags(data = data, inits = inits, parameters.to.save = pars_new,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 1000,
n.burnin = 500, n.thin = 2, DIC = FALSE, verbose=FALSE)
expect_equal(nrow(out$summary), 1)
expect_equal(ncol(out$samples[[1]]), 1)
expect_equal(out$mean$mu, mu2_est)
# Ragged arrays----------------------------------------------------------------
set.seed(123)
# Should trigger creation of a bunch of missing values in mu[,2] in output
modfile <- tempfile()
writeLines("
model{
for (i in 1:n){
employed[i] ~ dnorm(mu[i,1], tau)
mu[i,1] <- alpha + beta*gnp[i]
}
mu[1,2] <- 1
alpha ~ dnorm(0, 0.00001)
beta ~ dnorm(0, 0.00001)
sigma ~ dunif(0,1000)
tau <- pow(sigma,-2)
}", con=modfile)
inits <- function(){
list(alpha=rnorm(1,0,1),beta=rnorm(1,0,1),sigma=runif(1,0,3))
}
params <- c('alpha','beta','sigma', 'mu')
out <- jags(data = data, inits = inits, parameters.to.save = params,
model.file = modfile, n.chains = 3, n.adapt = 100, n.iter = 100,
n.burnin = 50, n.thin = 1, verbose=FALSE)
expect_equal(dim(out$mean$mu), c(16, 2))
expect_true(all(is.na(out$mean$mu[2:16,2])))
expect_equal(dim(out$sims.list$mu), c(150, 16, 2))
expect_equal(nrow(out$summary), 21)
expect_equal(rownames(out$summary)[20], "mu[1,2]")
# When no stochastic nodes (and deviance is not calculated)--------------------
library(jagsUI)
data <- list(a = 1, b = Inf)
modfile <- tempfile()
writeLines("
model{
x <- a * b
} ", con=modfile)
expect_warning(out <- jags(data=data, parameters.to.save="x", model.file=modfile,
n.chains=3, n.iter=10, n.burnin=5, n.adapt=10, DIC=TRUE, verbose=FALSE))
expect_equal(coda::varnames(out$samples), c("x"))
expect_true(is.na(out$Rhat$x))
expect_true(is.null(out$pD))
expect_true(is.null(out$DIC))
|
