tests/testthat/test-getCalibration.R

# Copyright 2025 Observational Health Data Sciences and Informatics
#
# This file is part of PatientLevelPrediction
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
test_that("getCalibration binary", {
  pErediction <- data.frame(
    rowId = 1:100,
    evaluation = rep("Test", 100),
    value = runif(100),
    outcomeCount = round(runif(100))
  )
  attr(pErediction, "metaData")$predictionType <- "binary"
  calib <- getCalibrationSummary(
    prediction = pErediction,
    predictionType = "binary",
    typeColumn = "evaluation",
    numberOfStrata = 100,
    truncateFraction = 0.05
  )

  expect_equal(nrow(calib), 100)
  expect_equal(ncol(calib), 12)
  expect_true("evaluation" %in% colnames(calib))


  calibBinary <- getCalibrationSummary_binary(
    prediction = pErediction,
    evalColumn = "evaluation",
    numberOfStrata = 100,
    truncateFraction = 0.05
  )

  expect_equal(calib, calibBinary)
})




test_that("getCalibration survival", {
  pErediction <- data.frame(
    rowId = 1:100,
    evaluation = rep("Test", 100),
    value = runif(100),
    survivalTime = 50 + sample(2 * 365, 100),
    outcomeCount = round(runif(100))
  )

  calib <- getCalibrationSummary_survival(
    prediction = pErediction,
    evalColumn = "evaluation",
    numberOfStrata = 50,
    truncateFraction = 0.05,
    timepoint = 365
  )

  expect_true("evaluation" %in% colnames(calib))
  expect_equal(nrow(calib), 50)
  expect_equal(ncol(calib), 7)
})

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PatientLevelPrediction documentation built on April 3, 2025, 9:58 p.m.