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.

```
# 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.

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

:

The unformatted p-value can be accessed as `pval_raw`

: