knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(riskclustr)
After using eh_test_subtype()
to obtain a model fit, if factor variables are involved in the analysis it will be of interest to obtain overall p-values testing for differences across subtypes across all levels of the factor variable.
The posthoc_factor_test()
function allows for post-hoc testing of a factor variable.
# Load needed packages library(riskclustr) library(dplyr)
# create a new example dataset that contains a factor variable factor_data <- subtype_data %>% mutate( x4 = cut( x1, breaks = c(-3.4, -0.4, 0.3, 1.1, 3.8), include.lowest = T, labels = c("1st quart", "2nd quart", "3rd quart", "4th quart") ) )
# Fit the model using x4 in place of x1 mod1 <- eh_test_subtype( label = "subtype", M = 4, factors = list("x4", "x2", "x3"), data = factor_data, digits = 2 )
After we have the model fit, we can obtain the p-value testing all levels of x4
simulaneously.
mypval <- posthoc_factor_test( fit = mod1, factor = "x4", nlevels = 4 )
The function returns both a formatted and unformatted p-value. The formatted p-value can be accessed as pval
:
mypval$pval
The unformatted p-value can be accessed as pval_raw
:
mypval$pval_raw
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