f_v_rf_categorical {collinear} | R Documentation |
Association Between a Categorical Response and a Categorical or Numeric Predictor
Description
Computes the Cramer's V between a categorical response (of class "character" or "factor") and the prediction of a Random Forest model with a categorical or numeric predictor and weighted cases.
Usage
f_v_rf_categorical(df)
Arguments
df |
(required, data frame) with columns:
|
Value
numeric: Cramer's V
See Also
Other preference_order_functions:
f_auc
,
f_r2
,
f_r2_counts
,
f_v()
Examples
#load example data
data(vi)
#reduce size to speed-up example
vi <- vi[1:1000, ]
#categorical response and predictor
#to data frame without NAs
df <- data.frame(
y = vi[["vi_factor"]],
x = vi[["soil_type"]]
) |>
na.omit()
#Cramer's V of a Random Forest model
f_v_rf_categorical(df = df)
#categorical response and numeric predictor
df <- data.frame(
y = vi[["vi_factor"]],
x = vi[["swi_mean"]]
) |>
na.omit()
f_v_rf_categorical(df = df)
[Package collinear version 2.0.0 Index]