tests/testthat/test-lincomb_logreg.R

In aorsf: Accelerated Oblique Random Survival Forests

# test_that(
#  desc = "logreg_fit with weights approximately equal to glm()",
#  code = {
#
#   nrows <- 1000
#   ncols <- 20
#
#   X <- matrix(data = rnorm(nrows*ncols), nrow = nrows, ncol = ncols)
#
#   # X <- cbind(1, X)
#
#   colnames(X) <- c(
#    # "intercept",
#    paste0("x", seq(ncols))
#   )
#
#   Y <- matrix(rbinom(nrows, size = 1, prob = 0.7), ncol = 1)
#
#   glm_data <- as.data.frame(cbind(y=as.numeric(Y), X))
#
#   # Fit logistic regression using the custom function
#
#   W <- sample(1:3, nrow(X), replace=TRUE)
#
#   control <- glm.control()
#
#   cpp = logreg_fit_exported(X, Y, W, do_scale = T,
#                             epsilon = control$epsilon,
#                             iter_max = control$maxit)
#
#   R = glm(y ~ .,
#           weights = as.integer(W),
#           control = control,
#           data = glm_data,
#           family = 'binomial')
#
#   R_summary <- summary(R)
#
#   R_beta_est <- as.numeric(R_summary$coefficients[-1, 'Estimate'])
#   R_beta_pvalues <- as.numeric(R_summary$coefficients[-1, 'Pr(>|z|)'])
#
#   expect_equal(cpp[,1], R_beta_est, tolerance = 1e-3)
#   expect_equal(cpp[,2], R_beta_pvalues, tolerance = 1e-3)
#
#
#  }
# )