Nothing
test_that("anytime-valid p-values are more conservative than standard p-values for ANOVA models", {
# Test with different precision parameters
for (g_val in c(1, 2, 5)) {
# Fit standard and anytime-valid ANOVA models
std_fit <- aov(Sepal.Length ~ Species, data = iris)
std_summary <- summary(std_fit)
std_pvals <- std_summary[[1]]$`Pr(>F)`[1] # Extract the p-value for Species
av_fit <- av(std_fit, g = g_val)
av_summary <- summary(av_fit)
av_pvals <- av_summary[[1]]$`Pr(>F)`[1]
# Check if the anytime-valid p-value is greater than or equal to the standard p-value
expect_gte(av_pvals, std_pvals)
}
})
test_that("anytime-valid methods work with increasingly complex ANOVA models", {
# Test using a more complex ANOVA model
complex_aov <- aov(Sepal.Length ~ Species * Petal.Width, data = iris)
av_complex_aov <- av(complex_aov)
expect_no_error(summary(av_complex_aov))
})
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