Context

After using eh_test_subtype() to obtain a model fit, if factor variables are involved in the analysis it will be of interest to obtain overall p-values testing for differences across subtypes across all levels of the factor variable.

The posthoc_factor_test() function allows for post-hoc testing of a factor variable.

Example

# Load needed packages
library(riskclustr)
library(dplyr)
# create a new example dataset that contains a factor variable
factor_data <-
subtype_data %>%
mutate(
x4 = cut(
x1,
breaks = c(-3.4, -0.4, 0.3, 1.1, 3.8),
include.lowest = T,
labels = c("1st quart",
"2nd quart",
"3rd quart",
"4th quart")
)
)
# Fit the model using x4 in place of x1
mod1 <- eh_test_subtype(
label = "subtype",
M = 4,
factors = list("x4", "x2", "x3"),
data = factor_data,
digits = 2
)

After we have the model fit, we can obtain the p-value testing all levels of x4 simulaneously.

mypval <- posthoc_factor_test(
fit = mod1,
factor = "x4",
nlevels = 4
)

The function returns both a formatted and unformatted p-value. The formatted p-value can be accessed as pval:

mypval$pval #> P #> "<.001" The unformatted p-value can be accessed as pval_raw: mypval$pval_raw
#> P
#> 0