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test_that("residual calculation works", {
set.seed(1)
X <- cbind(1, matrix(rnorm(4 * 2) , nrow = 4, ncol = 2))
Y <- matrix(rnbinom(n = 2 * 4, mu = 30, size = 0.7), nrow = 2, ncol = 4)
fit <- glm_gp(Y, X, size_factors = FALSE, overdispersion = 1/0.7)
r_fit1 <- glm(Y[1,] ~ X - 1, family = MASS::negative.binomial(theta = 0.7))
r_fit2 <- glm(Y[2,] ~ X - 1, family = MASS::negative.binomial(theta = 0.7))
expect_equal(unname(fit$Beta[1,]), unname(coef(r_fit1)), tolerance = 1e-4)
expect_equal(unname(fit$Beta[2,]), unname(coef(r_fit2)), tolerance = 1e-4)
expect_equal(c(t(residuals(fit, "response"))),
unname(c(residuals.glm(r_fit1, "response"), residuals.glm(r_fit2, "response"))),
tolerance = 1e-4)
expect_equal(c(t(residuals(fit, "working"))),
unname(c(residuals.glm(r_fit1, "working"), residuals.glm(r_fit2, "working"))),
tolerance = 1e-5)
expect_equal(c(t(residuals(fit, "pearson"))),
unname(c(residuals.glm(r_fit1, "pearson"), residuals.glm(r_fit2, "pearson"))),
tolerance = 1e-5)
expect_equal(c(t(residuals(fit, "deviance"))),
unname(c(residuals.glm(r_fit1, "deviance"), residuals.glm(r_fit2, "deviance"))),
tolerance = 1e-5)
# Randomized Quantiles are by definition not equal
r_qs <- c(statmod::qresiduals(r_fit1), statmod::qresiduals(r_fit2))
res <- c(t(residuals(fit, "randomized_quantile")))
expect_gt(cor(r_qs, res), 0.99)
})
test_that("residual calculation works with Delayed Matrix", {
set.seed(1)
X <- cbind(1, matrix(rnorm(4 * 2) , nrow = 4, ncol = 2))
Y <- matrix(rnbinom(n = 2 * 4, mu = 30, size = 0.7), nrow = 2, ncol = 4)
Y_hdf5 <- HDF5Array::writeHDF5Array(Y)
fit <- glm_gp(Y_hdf5, X, size_factors = FALSE, overdispersion = 1/0.7)
r_fit1 <- glm(Y[1,] ~ X - 1, family = MASS::negative.binomial(theta = 0.7))
r_fit2 <- glm(Y[2,] ~ X - 1, family = MASS::negative.binomial(theta = 0.7))
expect_equal(unname(fit$Beta[1,]), unname(coef(r_fit1)), tolerance = 1e-4)
expect_equal(unname(fit$Beta[2,]), unname(coef(r_fit2)), tolerance = 1e-4)
expect_s4_class(residuals(fit, "response"), "DelayedMatrix")
expect_s4_class(residuals(fit, "working"), "DelayedMatrix")
expect_s4_class(residuals(fit, "pearson"), "DelayedMatrix")
expect_s4_class(residuals(fit, "deviance"), "DelayedMatrix")
expect_equal(c(t(residuals(fit, "response"))),
unname(c(residuals.glm(r_fit1, "response"), residuals.glm(r_fit2, "response"))),
tolerance = 1e-4)
expect_equal(c(t(residuals(fit, "working"))),
unname(c(residuals.glm(r_fit1, "working"), residuals.glm(r_fit2, "working"))),
tolerance = 1e-5)
expect_equal(c(t(residuals(fit, "pearson"))),
unname(c(residuals.glm(r_fit1, "pearson"), residuals.glm(r_fit2, "pearson"))),
tolerance = 1e-5)
expect_equal(c(t(residuals(fit, "deviance"))),
unname(c(residuals.glm(r_fit1, "deviance"), residuals.glm(r_fit2, "deviance"))),
tolerance = 1e-5)
# Randomized Quantiles are by definition not equal
r_qs <- c(statmod::qresiduals(r_fit1), statmod::qresiduals(r_fit2))
res <- c(t(residuals(fit, "randomized_quantile")))
expect_gt(cor(r_qs, res), 0.99)
})
test_that("qres.gampoi can handle extreme value", {
Y <- matrix(27)
Mu <- matrix(2)
overdispersion <- 0
res <- qres.gampoi(Y, Mu, overdispersion)
expect_false(is.infinite(res))
Y <- matrix(2700)
res <- qres.gampoi(Y, Mu, overdispersion)
expect_false(is.infinite(res))
Y <- matrix(2)
Mu <- matrix(270)
res <- qres.gampoi(Y, Mu, overdispersion)
expect_false(is.infinite(res))
Y <- matrix(c(2, 2), ncol = 1)
Mu <- matrix(c(270, 270), ncol = 1)
overdispersion <- c(0, 500)
res <- qres.gampoi(Y, Mu, overdispersion)
expect_false(is.infinite(res[1,1]), is.infinite(res[2,1]))
expect_false(res[1,1] == res[2,1])
})
test_that("qres.gampoi can handle other weird values", {
if(R.version$arch == "i686") {
skip("i686 does not exhibit this weird issue.")
}
# This specific combination of parameters caused NA's
Y <- matrix(27)
Mu <- matrix(0.435023)
overdispersion <- 0
a <- ppois(Y - 1, lambda = Mu)
b <- ppois(Y, lambda = Mu)
# This really shouldn't happen
# Nonetheless, it does for this combination of parameters
# at least on Linux and MacOS
if(! is_windows()){
expect_gt(a, b)
}
# However, now qres.gampoi handles this edge case
res <- qres.gampoi(Y, Mu, overdispersion)
expect_false(is.nan(res))
})
test_that("compute_gp_deviance can handle weird values", {
# This specific combination of parameters caused negative deviance
y <- 1
mu <- 0.99999999999994
theta <- 1e-7
# However, now qres.gampoi handles this edge case
res <- compute_gp_deviance(y, mu, theta)
expect_gte(res, 0)
})
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