tests/testthat/test_classifyErrorHandling.R

In NBLDA: Negative Binomial Linear Discriminant Analysis

# context("classify_error_handling")
# test_that("classify produces error/warning messages properly.", {
#   data(cervical)
# #
# #   # a subset of cervical data with first 150 features.
# #   data <- cervical[c(1:150), ]
# #
# #   # defining sample classes.
# #   class <- data.frame(condition = factor(rep(c("N","T"), c(29, 29))))
# #
# #   n <- ncol(data)  # number of samples
# #   p <- nrow(data)  # number of features
# #
# #   # train set
# #   data.train <- data
# #   data.train <- as.matrix(data.train + 1)
# #   classtr <- data.frame(condition = class)
# #
# #   # train set in S4 class
# #   data.trainS4 <- DESeqDataSetFromMatrix(countData = data.train,
# #                                          colData = classtr, formula(~ condition))
# #   data.trainS4 <- DESeq(data.trainS4, fitType = "local")
# #
# #
# #   # Unmatched method error
# #   expect_error(classify(data = data.trainS4, method = "unkown", normalize = "deseq",
# #                         transformation = "vst", ref = "T",
# #                         control = trainControl(method = "repeatedcv", classProbs = TRUE)))
# #
# #   # method can not be NULL
# #   expect_error(classify(data = data.trainS4, method = NULL, normalize = "deseq",
# #                         transformation = "vst", ref = "T",
# #                         control = trainControl(method = "repeatedcv", classProbs = TRUE)))
# #
# #   # Reference is not defined as "character".
# #   expect_error(classify(data = data.trainS4, method = "rpart", normalize = "deseq",
# #                         transformation = "vst", ref = 2,
# #                         control = trainControl(method = "repeatedcv", classProbs = TRUE)))
# #
# #   # Class of "data" should be "DESeqDataSet
# #   expect_error(classify(data = data.train, method = "rpart", normalize = "deseq",
# #                         transformation = "vst", ref = "T",
# #                         control = trainControl(method = "repeatedcv", classProbs = TRUE)))
# #
# #   # warning:
# #   expect_warning(classify(data = data.trainS4, method = "rpart", normalize = "tmm",
# #                           transformation = "vst", ref = "T",
# #                           control = trainControl(method = "repeatedcv", number = 2, repeats = 2, classProbs = TRUE)))
# #
# #   expect_warning(classify(data = data.trainS4, method = "rpart", normalize = "none",
# #                           transformation = "vst", ref = "T",
# #                           control = trainControl(method = "repeatedcv", number = 2, repeats = 2, classProbs = TRUE)))
# })