```
context("test-plnpcafamily")
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
test_that("PLNPCAfamily: main function, field access and methods", {
## does not work because of future evaluating in random order the models
# output <- "\n Initialization...\n\n Adjusting 5 PLN models for PCA analysis.\n Rank approximation = 1\n\t conservative convex separable approximation for gradient descent Rank approximation = 2\n\t conservative convex separable approximation for gradient descent Rank approximation = 3\n\t conservative convex separable approximation for gradient descent Rank approximation = 4\n\t conservative convex separable approximation for gradient descent Rank approximation = 5\n\t conservative convex separable approximation for gradient descent\n Post-treatments\n DONE!"
#
models <- PLNPCA(Abundance ~ 1, data = trichoptera,
ranks = 1:5, control = PLNPCA_param(trace = 0))
expect_is(models, "PLNPCAfamily")
expect_is(plot(models), "ggplot")
expect_is(plot(models, reverse = TRUE), "ggplot")
expect_is(plot(models, map="individual"), "ggplot")
expect_is(plot(models, map="variable"), "ggplot")
expect_is(getBestModel(models), "PLNPCAfit")
expect_is(getModel(models, models$ranks[1]), "PLNPCAfit")
X <- model.matrix(Abundance ~ 1, data = trichoptera)
Y <- as.matrix(trichoptera$Abundance)
O <- matrix(0, nrow = nrow(Y), ncol = ncol(Y))
w <- rep(1, nrow(Y))
## extract the data matrices and weights
ctrl <-PLNPCA_param()
## instantiate
myPLN <- PLNmodels:::PLNPCAfamily$new(1:5, Y, X, O, w, Abundance ~ 1, ctrl)
## optimize
myPLN$optimize(ctrl$config_optim)
## post-treatment
config <- PLNmodels:::config_post_default_PLNPCA
config$trace <- 0
myPLN$postTreatment(config)
## S3 methods
expect_true(PLNmodels:::isPLNPCAfamily(myPLN))
expect_is(plot(myPLN), "ggplot")
expect_is(plot(myPLN, map="individual"), "ggplot")
expect_is(plot(myPLN, map="variable"), "ggplot")
expect_is(getBestModel(myPLN), "PLNPCAfit")
expect_is(getModel(myPLN, myPLN$ranks[1]), "PLNPCAfit")
## test fail on Mac OS : fit is slightly different between OS X and Ubuntu
## probably due to the version of optimization libraries...
## Show method
# expect_output(models$show(),
# "--------------------------------------------------------
# COLLECTION OF 5 POISSON LOGNORMAL MODELS
# --------------------------------------------------------
# Task: Principal Component Analysis
# ========================================================
# - Ranks considered: from 1 to 5
# - Best model (greater BIC): rank = 5
# - Best model (greater ICL): rank = 4",
# fixed = TRUE)
})
# test_that("PLNPCA is fast on low ranks", {
#
# n <- 100
# p <- 1000
# lambda <- exp(rnorm(n * p))
# Y <- matrix(rpois(n * p, lambda), n, p)
#
# models <- PLNPCA(Y ~ 1, ranks = 1:3)
# expect_is(models, "PLNPCAfamily")
# })
```

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