Classification Tree with Adjusted p-values

This function builds a classification tree based on raw or adjusted p-values using the results of a regression model.

Usage

pima_tree(res, p.values = "adjusted", method = "class", alpha = 0.05, ...)

res: A list object with a specific structure containing regression model results. It should contain `mods`, a list of models, and `summary_table`, a data frame with the summary of results, including estimates and p-values.
p.values: A char string indicating which type of p-values to use. Options are `"raw"` or `"adjusted"` (default). When `"raw"`, the function uses the p-values from the `summary_table`.
method: used in function `rpart::rpart` if `method="class"` it will classify significant p-values at level `alpha`
alpha: used only when `method="class"`
...: any other parameter of `rpart::rpart`.

A plot of the classification tree using `rpart.plot()`.

# Example usage (assuming `res` is a pre-computed result object)
# pvalue_tree(res, p.values = "raw")