Nothing
## library(growcurves, quietly = TRUE)
context("trtplot returns valid plot object and associated data.frame")
test_that("growplot returns plot object", {
##
## Load simulation dataset without nuisance covariates
## (Two treatment levels, {0,1}, and no nuisance covariates)
##
data(datsim)
##
## function to run either dp or lgm options under dpgrow function
##
mod <- function(x){
dpgrow(y = datsim$y, subject = datsim$subject, trt = datsim$trt, time = datsim$time,
n.random = datsim$n.random, n.fix_degree = 2, n.iter = 12, n.burn = 2,
n.thin = 1, shape.dp = 1, plot.out = TRUE, option = x)
}
DP <- mod("dp")
LGM <- mod("lgm")
subjects.plot = sample(datsim$subject,10,replace = FALSE)
gp <- growplot(object = DP, compare.objects = list(LGM = LGM), subjects.plot = subjects.plot, main.label = "DP")
## determine how many repeated subject measures associated with subjects in subjects.plot
tmp <- data.frame(datsim$y,datsim$subject)
names(tmp) <- c("y","subject")
tmp <- subset(tmp, subject %in% subjects.plot)
num.y <- length(tmp$y)
rm(tmp)
expect_that(length(gp), equals(5))
expect_that(gp$p.gctrt, is_a("ggplot"))
expect_that(names(gp$dat.gc)[1], matches("fit"))
expect_that(nrow(gp$dat.data),is_equivalent_to(num.y)) ## plot data.frame properly dimensioned
})
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