Nothing
library(testthat)
library(EmpiricalCalibration)
data(sccs)
negatives <- sccs[sccs$groundTruth == 0, ]
positive <- sccs[sccs$groundTruth == 1, ]
test_that("calibrateLlr normal distribution", {
null <- fitNull(negatives$logRr, negatives$seLogRr)
expect_equal(calibrateLlr(null, positive), 0)
expect_error(calibrateLlr(null, positive, twoSided = TRUE),
regexp = ".*only one-sided upper LLRs are supported.*"
)
expect_error(calibrateLlr(null, positive, upper = FALSE),
regexp = ".*only one-sided upper LLRs are supported.*"
)
})
test_that("calibrateLlr grid distribution", {
set.seed(123)
mu <- 0.2
sigma <- 0.2
data <- simulateControls(n = 50, mean = mu, sd = sigma, trueLogRr = 0, seLogRr = 0.1)
goldStandardNull <- fitNull(logRr = data$logRr, seLogRr = data$seLogRr)
point <- seq(log(0.1), log(10), length.out = 1000)
createGridApproximation <- function(row) {
return(data.frame(
point = point,
value = dnorm(point, mean = row$logRr, sd = row$seLogRr)
))
}
gridApproximations <- lapply(split(data, 1:nrow(data)), createGridApproximation)
null <- fitNullNonNormalLl(gridApproximations)
calibrateLlr(null, gridApproximations)
})
test_that("calibrateLlr non normal distribution", {
null <- fitNull(negatives$logRr, negatives$seLogRr)
colnames(positive) <- c("drugName", "mu", "gamma", "sigma")
expect_equal(calibrateLlr(null, positive), 0.247,
tolerance = 0.1,
check.attributes = FALSE
)
colnames(positive) <- c("drugName", "mu", "alpha", "sigma")
expect_equal(calibrateLlr(null, positive), 0.344,
tolerance = 0.1,
check.attributes = FALSE
)
})
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