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# Project: gspcr
# Objective: Test cp_BIC function
# Author: Edoardo Costantini
# Created: 2023-04-18
# Modified: 2023-04-18
# Notes:
# Define tolerance for differences
tol <- 1e-5
# Test: output class -----------------------------------------------------------
# Fit some model
lm_out <- lm(mpg ~ cyl + disp, data = mtcars)
# Compute BIC with your function
BIC_M <- cp_BIC(
ll = logLik(lm_out),
n = nobs(lm_out),
k = length(coef(lm_out)) + 1 # intercept + reg coefs + error variance
)
# Atomic numeric vector
testthat::expect_true(is.numeric(BIC_M))
# Length 1
testthat::expect_true(length(BIC_M) == 1)
# Test: manual computation = stats::BIC output ---------------------------------
# Compute BIC with R function
BIC_R <- stats::BIC(lm_out)
# R equal to manual
testthat::expect_true(BIC_R - BIC_M < tol)
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