Nothing
### normal_pi() is implecitely covered, because it generates the output of
# the lmer_pi_... functions
test_that("check class and output", {
fit <- lmer(y_ijk~(1|a)+(1|b)+(1|a:b), c2_dat1)
pred_int <- lmer_pi_unstruc(model=fit,
newdat=c2_dat2,
alternative="both",
traceplot=FALSE,
nboot=100)
# Test classes
expect_s3_class(pred_int,
class=c("predint", "normalPI"))
names_pi <- names(pred_int)
expect_equal(names_pi,
c("prediction",
"newdat",
"futmat_list",
"futvec",
"histdat",
"y_star_hat",
"pred_se",
"alternative",
"q",
"mu",
"pred_var",
"m",
"algorithm"))
# No. of slots of the output list
expect_equal(length(pred_int), 13)
# $prediction has to be a data.frame
expect_true(is.data.frame(pred_int$prediction))
# histdat has to be a data.frame
expect_true(is.data.frame(pred_int$histdat))
# Algorithm ha to be MS22 by default
expect_equal(pred_int$algorithm, "MS22")
})
test_that("model, newdat and m must be specified correctly", {
# newdat and m are not specified
expect_error(lmer_pi_unstruc(model=c2_dat1))
# newdat and m are both specified
expect_error(lmer_pi_unstruc(model=lme4::lmer(y_ijk~(1|a)+(1|b)+(1|a:b), c2_dat1),
newdat=c2_dat2,
m=10))
# newdat is not a data frame
expect_error(lmer_pi_unstruc(model=lme4::lmer(y_ijk~(1|a)+(1|b)+(1|a:b), c2_dat1),
newdat=c(1,2,3)))
# random effects must be specified as (1|rf)
expect_error(lmer_pi_unstruc(model=lme4::lmer(y_ijk~(b|a), c2_dat1),
newdat=c2_dat2))
# must be of length 1
expect_error(lmer_pi_unstruc(model=lme4::lmer(y_ijk~(1|a)+(1|b)+(1|a:b), c2_dat1),
m=c(10, 11)))
})
test_that("alternative", {
# alternative
expect_error(lmer_pi_unstruc(model=lme4::lmer(y_ijk~(1|a)+(1|b)+(1|a:b), c2_dat1),
m=3,
alternative="opper"))
})
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