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context("dpgrowmm returns correct objects under multivariate MM effects")
##
## Load simulation dataset without nuisance covariates
## (Two treatment levels, {0,1}, and no nuisance covariates)
##
data(datsim)
##
## function to run either mmcar, mmigrp or mmi options under dpgrow function
##
mod <- function(x, niter, nburn, nthin){
dpgrowmm(y = datsim$y, subject = datsim$subject, trt = datsim$trt, time = datsim$time,
n.random = datsim$n.random, n.fix_degree = 2, Omega = datsim$Omega, group = datsim$group,
subj.aff = datsim$subj.aff, W.subj.aff = datsim$W.subj.aff, multi = TRUE, n.iter = niter, n.burn = nburn,
n.thin = nthin, shape.dp = 4, strength.mm = 0.1, plot.out = TRUE, option = x)
}
test_that("mmi option of dpgrowmm returns expected objects under multivariate MM effects", {
niter <- 8
nburn <- 2
nthin <- 2
MMI <- mod("mmi",niter,nburn,nthin)
srm <- summary(MMI)$summary.results
parms <- samples(MMI)
pr <- MMI$plot.results
num.subj <- length(unique(datsim$subject))
nrandom <- ncol(srm$Z)
nsessions <- ncol(datsim$W.subj.aff)
Nmv <- MMI$summary.results$Nmv
## evaluating class
expect_that(MMI,is_a("dpgrowmm"))
## evaluating summary output
expect_that(length(names(srm)), equals(26))
expect_that(nrow(srm$u.summary), is_equivalent_to((Nmv*nsessions)))
expect_that(srm$bmat.summary, is_a("list"))
expect_that(names(srm)[17], matches("lpml"))
expect_that(ncol(srm$X),equals(5))
expect_match(colnames(srm$X),"time")
## evaluating MCMC sample results
expect_that(nrow(parms$M),is_equivalent_to((niter-nburn)/nthin))
expect_that(ncol(parms$B),is_equivalent_to(num.subj*nrandom))
expect_that(length(residuals(MMI)),equals(length(datsim$y)))
## checking plot output
expect_that(length(names(pr)),equals(11))
expect_that(names(pr)[11],matches("p.gcsel"))
expect_that(pr$p.gcsel,is_a("ggplot"))
})
test_that("mmcar option of dpgrowmm returns expected objects under multivariate MM effects", {
niter <- 8
nburn <- 2
nthin <- 2
MMCAR <- mod("mmcar",niter,nburn,nthin)
srm <- summary(MMCAR)$summary.results
parms <- samples(MMCAR)
pr <- MMCAR$plot.results
num.subj <- length(unique(datsim$subject))
nrandom <- ncol(srm$Z)
nsessions <- ncol(datsim$W.subj.aff)
Nmv <- MMCAR$summary.results$Nmv
## evaluating class
expect_that(MMCAR,is_a("dpgrowmm"))
## evaluating summary output
expect_that(length(names(srm)), equals(26))
expect_that(nrow(srm$u.summary), is_equivalent_to((Nmv*nsessions)))
expect_that(srm$bmat.summary, is_a("list"))
expect_that(names(srm)[17], matches("lpml"))
expect_that(ncol(srm$X),equals(5))
expect_match(colnames(srm$X),"time")
## evaluating MCMC sample results
expect_that(nrow(parms$M),is_equivalent_to((niter-nburn)/nthin))
expect_that(ncol(parms$B),is_equivalent_to(num.subj*nrandom))
expect_that(length(residuals(MMCAR)),equals(length(datsim$y)))
## checking plot output
expect_that(length(names(pr)),equals(11))
expect_that(names(pr)[11],matches("p.gcsel"))
expect_that(pr$p.gcsel,is_a("ggplot"))
})
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