The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Tutorial: Obtain an overall p-value for a factor variable

Emily C. Zabor

Last updated: January 18, 2024

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
#>      [,1]   
#> [1,] "<.001"

The unformatted p-value can be accessed as pval_raw:

mypval$pval_raw
#>      [,1]
#> [1,]    0

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.