39 lines
2.1 KiB
R
39 lines
2.1 KiB
R
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context("Calculate integrated Brier score without error")
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# This code is more concerned that the code runs without error. The tests in the
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# Java code check that the results it returns are accurate.
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test_that("Can calculate Integrated Brier Score", {
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sampleData <- data.frame(x=rnorm(100))
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sampleData$T <- sample(0:4, size=100, replace = TRUE) # the censor distribution we provide needs to conform to the data or we can get NaNs
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sampleData$delta <- sample(0:2, size = 100, replace = TRUE)
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testData <- sampleData[1:5,]
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trainingData <- sampleData[6:100,]
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forest <- train(CR_Response(delta, T) ~ x, trainingData, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, cores=2, displayProgress=FALSE)
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predictions <- predict(forest, testData)
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scores_test <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
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censoringDistribution = NULL)
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# Check that we don't get a crash if we calculate the error for only one observation
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scores_one <- integratedBrierScore(CR_Response(testData$delta, testData$T)[1], predictions[1], event = 1, time = 4,
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censoringDistribution = NULL)
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# Make sure our error didn't somehow change
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expect_equal(scores_one, scores_test[1])
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# Provide a censoring distribution via censor times
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scores_censoring1 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
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censoringDistribution = c(0,1,1,2,3,4))
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scores_censoring2 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
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censoringDistribution = list(x = 0:4, y = 1 - c(1/6, 3/6, 4/6, 5/6, 6/6)))
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scores_censoring3 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
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censoringDistribution = stepfun(x=0:4, y=1 - c(0, 1/6, 3/6, 4/6, 5/6, 6/6)))
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expect_equal(scores_censoring1, scores_censoring2)
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expect_equal(scores_censoring1, scores_censoring3)
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})
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