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Summary table of CHAID terminal nodes

Source: R/11_inspect.R
chaid_table.Rd

Builds a segment summary of the terminal nodes. The first row is the root node, which serves as the baseline (index 100). For categorical responses the table contains the predicted class and the class shares, plus, when target is given, the response rate and the index value (response rate relative to the root, times 100). For continuous responses it contains the node mean, standard deviation and the index of the mean.

Usage

chaid_table(fit, target = NULL)

Arguments

fit

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

target

Optional response level of interest (categorical responses only). Adds response_rate and index columns.

Value

A data frame with one row for the root followed by one row per terminal node, including the reaching rule in the rule column.

Details

Note that n is the sum of frequency weights while pct_n and the class shares are computed with case weights included; the two scales coincide unless case weights are used.

See also

chaid_rules(), chaid_gains(), chaid_importance()

Examples

fit <- chaid(Species ~ ., data = iris,
             control = chaid_control(min_parent = 30, min_child = 10))
chaid_table(fit)
#>   node depth   n pct_n prediction p_setosa p_versicolor p_virginica
#> 1    1     0 150 100.0     setosa   0.3333       0.3333      0.3333
#> 2    2     1  44  29.3     setosa   1.0000       0.0000      0.0000
#> 3    3     1  14   9.3 versicolor   0.4286       0.5714      0.0000
#> 4    4     1  32  21.3 versicolor   0.0000       0.9688      0.0312
#> 5    5     1  14   9.3 versicolor   0.0000       0.6429      0.3571
#> 6    6     1  16  10.7  virginica   0.0000       0.1250      0.8750
#> 7    7     1  30  20.0  virginica   0.0000       0.0000      1.0000
#>                         rule
#> 1                     (root)
#> 2        Petal.Length <= 1.6
#> 3 Petal.Length in (1.6, 3.8]
#> 4 Petal.Length in (3.8, 4.6]
#> 5 Petal.Length in (4.6, 4.9]
#> 6 Petal.Length in (4.9, 5.3]
#> 7         Petal.Length > 5.3
chaid_table(fit, target = "virginica")
#>   node depth   n pct_n prediction p_setosa p_versicolor p_virginica
#> 1    1     0 150 100.0     setosa   0.3333       0.3333      0.3333
#> 2    2     1  44  29.3     setosa   1.0000       0.0000      0.0000
#> 3    3     1  14   9.3 versicolor   0.4286       0.5714      0.0000
#> 4    4     1  32  21.3 versicolor   0.0000       0.9688      0.0312
#> 5    5     1  14   9.3 versicolor   0.0000       0.6429      0.3571
#> 6    6     1  16  10.7  virginica   0.0000       0.1250      0.8750
#> 7    7     1  30  20.0  virginica   0.0000       0.0000      1.0000
#>   response_rate index                       rule
#> 1        0.3333 100.0                     (root)
#> 2        0.0000   0.0        Petal.Length <= 1.6
#> 3        0.0000   0.0 Petal.Length in (1.6, 3.8]
#> 4        0.0312   9.4 Petal.Length in (3.8, 4.6]
#> 5        0.3571 107.1 Petal.Length in (4.6, 4.9]
#> 6        0.8750 262.5 Petal.Length in (4.9, 5.3]
#> 7        1.0000 300.0         Petal.Length > 5.3