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Heuristic variable importance for a CHAID tree

Source: R/11_inspect.R
chaid_importance.Rd

SPSS defines no official importance measure for CHAID, and the chi-squared and F statistics of different splits are not directly comparable because their degrees of freedom differ. This function therefore uses a p-value based heuristic: for each predictor, importance is the sum over its splits of (node size / root size) * (-log10(adjusted p-value)), i.e. a variable is important when it splits large nodes with strong significance.

Usage

chaid_importance(fit)

Arguments

fit

A fitted "chaid" object returned by chaid().

Value

A data frame with one row per predictor actually used for a split, sorted by decreasing importance: variable, n_splits, min_p_adj, importance and importance_pct (share of the total importance in percent).

See also

chaid(), chaid_table()

Examples

fit <- chaid(Species ~ ., data = iris,
             control = chaid_control(min_parent = 30, min_child = 10))
chaid_importance(fit)
#>       variable n_splits    min_p_adj importance importance_pct
#> 1 Petal.Length        1 1.354149e-44   43.86833            100