Nothing
local_binary_preds <- function(env = parent.frame()) {
data("traindata", package = "CalibrationCurves", envir = env)
data("testdata", package = "CalibrationCurves", envir = env)
fit <- glm(y ~ ., data = env$traindata, family = binomial)
p <- predict(fit, newdata = env$testdata, type = "response")
y <- env$testdata$y
list(p = unname(p), y = y)
}
test_that("print.CalibrationCurve works and returns invisibly", {
d <- local_binary_preds()
res <- val.prob.ci.2(d$p, d$y, pl = FALSE)
out <- capture.output(ret <- print(res))
expect_identical(ret, res)
expect_true(any(grepl("Call:", out)))
expect_true(any(grepl("confidence interval", out)))
})
test_that("print.ggplotCalibrationCurve works and returns invisibly", {
d <- local_binary_preds()
res <- valProbggplot(d$p, d$y, pl = TRUE)
out <- capture.output(ret <- print(res))
expect_identical(ret, res)
expect_true(any(grepl("Call:", out)))
})
test_that("print.GeneralizedCalibrationCurve works and returns invisibly", {
data("poissontraindata", package = "CalibrationCurves")
data("poissontestdata", package = "CalibrationCurves")
fit <- glm(Y ~ ., data = poissontraindata, family = poisson)
yHat <- predict(fit, newdata = poissontestdata, type = "response")
res <- genCalCurve(poissontestdata$Y, yHat, family = "poisson", plot = FALSE)
out <- capture.output(ret <- print(res))
expect_identical(ret, res)
expect_true(any(grepl("Call:", out)))
expect_true(any(grepl("confidence interval", out)))
})
test_that("print.SurvivalCalibrationCurve works and returns invisibly", {
data("trainDataSurvival", package = "CalibrationCurves")
data("testDataSurvival", package = "CalibrationCurves")
sFit <- coxph(Surv(ryear, rfs) ~ csize + cnode + grade3,
data = trainDataSurvival, x = TRUE, y = TRUE)
res <- valProbSurvival(sFit, testDataSurvival, plotCal = "none")
out <- capture.output(ret <- print(res))
expect_identical(ret, res)
expect_true(any(grepl("Call:", out)))
expect_true(any(grepl("Calibration performance", out)))
})
test_that("print.ClusteredCalibrationCurve works without error", {
skip_on_cran()
data("clustertraindata", package = "CalibrationCurves")
data("clustertestdata", package = "CalibrationCurves")
mFit <- lme4::glmer(y ~ x1 + x2 + x3 + x5 + (1 | cluster),
data = clustertraindata, family = "binomial")
preds <- predict(mFit, clustertestdata, type = "response", re.form = NA)
res <- suppressWarnings(
valProbCluster(p = preds, y = clustertestdata$y,
cluster = clustertestdata$cluster,
plot = TRUE, approach = "MIXC", grid_l = 50)
)
out <- capture.output(ret <- print(res))
expect_true(any(grepl("Call:", out)))
})
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