Nothing
test_that("output from unregularized poisson model matches glm", {
set.seed(654)
for (intercept in c(TRUE, FALSE)) {
xy <- SLOPE:::randomProblem(100, 10, response = "poisson", amplitude = 1)
x <- xy$x
y <- xy$y
x <- scale(x)
SLOPE_fit <- SLOPE(
x,
y,
family = "poisson",
alpha = 1e-8,
intercept = intercept,
center = FALSE,
scale = "none"
)
glm_fit <- if (intercept) {
glm(y ~ 1 + ., data = data.frame(y = y, x), family = "poisson")
} else {
glm(y ~ 0 + ., data = data.frame(y = y, x), family = "poisson")
}
expect_equivalent(coef(glm_fit), coef(SLOPE_fit), tol = 1e-5)
}
})
test_that("SLOPE reproduces lasso fit when all lambda are equal", {
set.seed(0978213)
xy <- SLOPE:::randomProblem(100, 10, response = "poisson", amplitude = 1)
x <- xy$x
y <- xy$y
x <- scale(x)
n <- nrow(x)
p <- ncol(x)
alpha <- 1
gnt_fit <- glmnet::glmnet(x, y,
family = "poisson",
lambda = alpha,
standardize = FALSE
)
gnt_coef <- as.vector(coef(gnt_fit))
SLOPE_fit <- SLOPE(x, y,
family = "poisson",
alpha = alpha,
lambda = rep(1, p),
scale = "none",
center = FALSE
)
expect_equivalent(gnt_coef, coef(SLOPE_fit), tol = 1e-3)
})
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