test_that("IAEISE", {
# case 1 Surv object
library(survival)
library(randomForestSRC)
library(pec)
set.seed(1234)
mydata <- kidney[, -1]
train_index <- sample(1:nrow(mydata), 0.7 * nrow(mydata))
train_data <- mydata[train_index, ]
test_data <- mydata[-train_index, ]
coxfit <- coxph(Surv(time, status) ~ ., data = train_data, x = TRUE)
distime <- sort(unique(as.vector(coxfit$y[coxfit$y[, 2] == 1])))
sp_matrix <- predictSurvProb(coxfit, test_data, distime)
time <- test_data$time
status <- test_data$status
expect_type(IAEISE(Surv(time, status), sp_matrix), "double")
expect_equal(as.numeric(IAEISE(Surv(time, status), sp_matrix)), c(132.0101, 44.3022))
expect_error(
IAEISE(Surv(time[-1], status), sp_matrix),
"Time and status are different lengths"
)
expect_error(
IAEISE(Surv(time, status), sp_matrix, c(2, 1)),
"The interval must increase"
)
expect_error(
IAEISE(Surv(time, status), sp_matrix, c(1, NA)),
"Cannot calculate IAE or ISE in the interval containing NA"
)
expect_error(
IAEISE(Surv(time, status), NA),
"The input probability matrix cannot have NA"
)
expect_error(
IAEISE(Surv(time, status), sp_matrix, 1),
"Can not calculate the integration at a single point"
)
time[1] <- NA
expect_error(
IAEISE(Surv(time, status), sp_matrix),
"The input vector cannot have NA"
)
# case2 fit object
# test coxph
coxfit <- coxph(Surv(time, status) ~ ., data = train_data, x = TRUE)
expect_type(IAEISE(coxfit, test_data), "double")
# test RSF
rsffit <- rfsrc(Surv(time, status) ~ ., data = train_data)
expect_type(IAEISE(rsffit, test_data), "double")
# test survreg
for (dist in c(
"weibull", "exponential", "gaussian",
"logistic", "lognormal", "loglogistic"
)) {
survregfit <- survreg(Surv(time, status) ~ .,
dist = dist, data = train_data
)
expect_type(IAEISE(survregfit, test_data), "double")
}
})
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