tests/testthat/test-smallExactCLR.R

library("testthat")
library("survival")

#
# Small exact, conditional logistic regression
#

test_that("Small exact, conditional logistic regression with no ties", {
    skip_on_cran()

    gold <- clogit(case ~ spontaneous + induced + strata(stratum), data=infert)

    dataPtrNoTies <- createCyclopsData(case ~ spontaneous + induced + strata(stratum),
                                            data = infert,
                                            modelType = "clr")

    cyclopsFitNoTies <- fitCyclopsModel(dataPtrNoTies, prior = createPrior("none"))

    dataPtrExact <- createCyclopsData(case ~ spontaneous + induced + strata(stratum),
                                           data = infert,
                                           modelType = "clr_exact")

    cyclopsFitExact <- fitCyclopsModel(dataPtrExact, prior = createPrior("none"))

    tolerance <- 1E-4

    expect_equal(coef(cyclopsFitNoTies), coef(gold), tolerance = tolerance)
    expect_equal(coef(cyclopsFitExact), coef(gold), tolerance = tolerance)
})

 test_that("Small, exact conditinal logistic regression with ties" , {
    skip_on_cran()

    withTies <- read.table(system.file("extdata/test1-clr.txt", package="Cyclops"), sep=",")
    names(withTies) <- c("stratum", "y",paste("x", 1:10, sep=""))

    goldWithTies <- clogit(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + strata(stratum),
                           data = withTies, method="exact")

    goldWithTiesBreslow <- clogit(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + strata(stratum),
                                  data = withTies, method="breslow")

    dataPtrWithTies <-createCyclopsData(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + strata(stratum),
                                             data = withTies,
                                             modelType = "clr_exact")

    cyclopsFitWithTies <- fitCyclopsModel(dataPtrWithTies, prior = createPrior("none"))

    dataPtrWithTiesBreslow <-createCyclopsData(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + strata(stratum),
                                                    data = withTies,
                                                    modelType = "clr")

    cyclopsFitWithTiesBreslow <- fitCyclopsModel(dataPtrWithTiesBreslow, prior = createPrior("none"))

    tolerance <- 1E-4

    expect_equal(coef(cyclopsFitWithTies), coef(goldWithTies), tolerance = tolerance)
    expect_equal(coef(cyclopsFitWithTiesBreslow), coef(goldWithTiesBreslow), tolerance = tolerance)
})

# test_that("Evaluate speed of exact method without ties (should be same as Breslow)", {
#     gold <- clogit(case ~ spontaneous + induced + strata(stratum), data=infert)
#
#     dataPtrBreslow <- createCyclopsData(case ~ spontaneous + induced + strata(stratum),
#                                              data = infert,
#                                              modelType = "clr")
#
#     cyclopsFitBreslow <- fitCyclopsModel(dataPtrBreslow, prior = createPrior("none"), forceNewObject = TRUE)
#
#     dataPtrExact <- createCyclopsData(case ~ strata(stratum), sparseFormula = ~spontaneous + induced,
#                                            data = infert,
#                                            modelType = "clr_exact")
#
#     cyclopsFitExact <- fitCyclopsModel(dataPtrExact, prior = createPrior("none"), forceNewObject = TRUE)
#
#     library(microbenchmark)
#     microbenchmark(
#         clogit(case ~ spontaneous + induced + strata(stratum), data=infert),
#         {
#             dataPtrBreslow <- createCyclopsData(case ~ spontaneous + induced + strata(stratum),
#                                                      data = infert,
#                                                      modelType = "clr")
#             fitCyclopsModel(dataPtrBreslow, prior = createPrior("none"), forceNewObject = TRUE)
#         },
#         fitCyclopsModel(dataPtrExact, prior = createPrior("none"), forceNewObject = TRUE),
#         times = 100L
#     )
# })

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Cyclops documentation built on Nov. 2, 2023, 6:03 p.m.