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
199
200
201
202
203
204
205
206
207
208
209
210
|
context("stan_pcount function and methods")
on_mac <- tolower(Sys.info()[["sysname"]]) == "darwin"
on_cran <- !identical(Sys.getenv("NOT_CRAN"), "true")
skip_if(on_mac & on_cran, "On CRAN mac")
#Simulate dataset
set.seed(123)
M <- 50
J <- 5
beta <- c(1, 0.5, 0.2, -0.4)
sc <- data.frame(x1=rnorm(M), x2=sample(letters[1:26],M,replace=T),
stringsAsFactors=TRUE)
oc <- data.frame(x3=rnorm(M*J))
lambda <- exp(beta[1] + beta[2]*sc$x1) #+ rx2[rx_idx])
N <- rpois(M, lambda)
p <- plogis(beta[3] + beta[4]*oc$x3)
y <- matrix(NA, M, J)
idx <- 1
for (i in 1:M){
y[i,] <- rbinom(J, N[i], p[idx:(idx+J-1)])
idx <- idx + J
}
umf <- unmarkedFramePCount(y=y,siteCovs=sc, obsCovs=oc)
umf2 <- umf
umf2@y[1,] <- NA
umf2@y[2,1] <- NA
good_fit <- TRUE
tryCatch({
fit <- suppressWarnings(stan_pcount(~x3~x1, umf[1:10,], K=15,
chains=2, iter=100, refresh=0))
fit_na <- suppressWarnings(stan_pcount(~x3~x1, umf2[1:10,], K=15,
chains=2, iter=100, refresh=0))
}, error=function(e){
good_fit <<- FALSE
})
skip_if(!good_fit, "Test setup failed")
test_that("stan_pcount output structure is correct",{
expect_is(fit, "ubmsFitPcount")
expect_is(fit, "ubmsFitAbun")
expect_equal(nsamples(fit), 100)
})
test_that("stan_pcount produces accurate results",{
skip_on_cran()
skip_on_ci()
skip_on_covr()
set.seed(123)
fit_long <- suppressWarnings(stan_pcount(~x3~x1, umf, K=15, chains=2,
iter=300, refresh=0))
fit_unm <- pcount(~x3~x1, umf, K=15)
#similar to truth
expect_RMSE(coef(fit_long), beta, 0.2)
#similar to unmarked
expect_RMSE(coef(fit_long), coef(fit_unm), 0.05)
#similar to previous known values
expect_RMSE(coef(fit_long), c(0.96637,0.54445,0.02651,-0.3631), 0.05)
})
test_that("offsets work with stan_pcount",{
skip_on_cran()
skip_on_ci()
skip_on_covr()
set.seed(123)
umf@siteCovs$area <- runif(numSites(umf), 0, 1)
fit_long <- suppressWarnings(stan_pcount(~x3~x1+offset(log(area)), umf, K=15, chains=2,
iter=300, refresh=0))
fit_unm <- pcount(~x3~x1+offset(log(area)), umf, K=15)
expect_RMSE(coef(fit_long), coef(fit_unm), 0.05)
pr_stan <- predict(fit_long, "state")
pr_unm <- predict(fit_unm, "state")
expect_RMSE(pr_stan$Predicted, pr_unm$Predicted, 0.05)
})
test_that("stan_pcount handles NA values",{
expect_true(is.numeric(coef(fit_na)))
})
test_that("ubmsFitPcount gof method works",{
set.seed(123)
g <- gof(fit, draws=5, quiet=TRUE)
expect_between(g@estimate, 30, 100)
gof_plot_method <- methods::getMethod("plot", "ubmsGOF")
pdf(NULL)
pg <- gof_plot_method(g)
dev.off()
expect_is(pg, "gg")
})
test_that("ubmsFitPcount gof method works with missing values",{
set.seed(123)
g <- gof(fit_na, draws=5, quiet=TRUE)
expect_is(g, "ubmsGOF")
})
test_that("ubmsFitPcount predict method works",{
pr <- predict(fit_na, "state")
expect_is(pr, "data.frame")
expect_equal(dim(pr), c(10, 4))
expect_between(pr[1,1], 0, 15)
pr <- predict(fit_na, "det")
expect_equal(dim(pr), c(10*obsNum(umf2),4))
expect_between(pr[1,1], 0, 1)
#with newdata
nd <- data.frame(x1=c(0,1))
pr <- predict(fit_na, "state", newdata=nd)
expect_equal(dim(pr), c(2,4))
expect_between(pr[1,1], 0, 15)
})
test_that("ubmsFitPcount sim_z method works",{
set.seed(123)
samples <- get_samples(fit, 5)
zz <- sim_z(fit, samples, re.form=NULL)
expect_is(zz, "matrix")
expect_equal(dim(zz), c(length(samples), 10))
expect_between(mean(zz), 0, 10)
set.seed(123)
pz <- posterior_predict(fit, "z", draws=5)
expect_equivalent(zz, pz)
})
test_that("ubmsFitPcount sim_y method works",{
set.seed(123)
samples <- get_samples(fit, 5)
yy <- sim_y(fit, samples, re.form=NULL)
expect_is(yy, "matrix")
expect_equal(dim(yy), c(length(samples), 10*obsNum(umf)))
set.seed(123)
py <- posterior_predict(fit, "y", draws=5)
expect_equivalent(yy, py)
})
test_that("Posterior sim methods for ubmsFitPcount work with NAs",{
zna <- posterior_predict(fit_na, "z", draws=3)
expect_equal(dim(zna), c(3,10))
expect_true(all(is.na(zna[,1])))
yna <- posterior_predict(fit_na, "y", draws=3)
expect_equal(dim(yna), c(3, 10*obsNum(umf2)))
expect_equal(sum(is.na(yna[1,])), 5)
expect_equal(sum(is.na(yna[2,])), 5)
})
test_that("Posterior linear pred methods work for ubmsFitPcount",{
set.seed(123)
samples <- get_samples(fit, 3)
lp1 <- sim_lp(fit, "state", transform=TRUE, samples=samples,
newdata=NULL, re.form=NULL)
expect_equal(dim(lp1), c(length(samples), 10))
set.seed(123)
pl <- posterior_linpred(fit, draws=3, submodel="state")
})
test_that("Fitted/residual methods work with ubmsFitPcount",{
ubms_fitted <- methods::getMethod("fitted", "ubmsFit")
ubms_residuals <- methods::getMethod("residuals", "ubmsFit")
ubms_plot <- methods::getMethod("plot", "ubmsFit")
ft <- ubms_fitted(fit, "state", draws=5)
ft2 <- ubms_fitted(fit, "det", draws=5)
expect_equal(dim(ft), c(5, 10))
expect_equal(dim(ft2), c(5, 10*obsNum(umf)))
res <- ubms_residuals(fit, "state", draws=5)
res2 <- ubms_residuals(fit, "det", draws=5)
expect_equal(dim(res), c(5, 10))
expect_equal(dim(res2), c(5, 10*obsNum(umf)))
pdf(NULL)
rp <- plot_residuals(fit, "state")
rp2 <- plot_residuals(fit, "det")
rp3 <- ubms_plot(fit)
mp <- plot_marginal(fit, "state")
dev.off()
expect_is(rp, "gg")
expect_is(rp2, "gg")
expect_is(rp3, "gtable")
expect_is(mp, "gtable")
})
test_that("pcount spatial works", {
skip_on_cran()
umf2 <- umf
umf2@siteCovs$x <- runif(numSites(umf2), 0, 10)
umf2@siteCovs$y <- runif(numSites(umf2), 0, 10)
fit_spat <- suppressMessages(suppressWarnings(stan_pcount(~1~x1+RSR(x,y,1),
umf2[1:20,], K=15, chains=2, iter=100, refresh=0)))
expect_is(fit_spat@submodels@submodels$state, "ubmsSubmodelSpatial")
expect_equal(names(coef(fit_spat))[3], "state[RSR [tau]]")
# With offsets
umf2@siteCovs$area <- runif(numSites(umf2), 0, 1)
fit_spat <- suppressMessages(suppressWarnings(stan_pcount(~1~x1+offset(area) + RSR(x,y,1),
umf2[1:20,], K=15, chains=2, iter=100, refresh=0)))
expect_is(fit_spat, "ubmsFit")
fit_spat <- suppressMessages(suppressWarnings(stan_pcount(~offset(area)~x1+ RSR(x,y,1),
umf2[1:20,], K=15, chains=2, iter=100, refresh=0)))
expect_is(fit_spat, "ubmsFit")
})
|
