Calculate MID Importance

mid.importance() calculates the MID importance of a fitted MID model. This is a measure of feature importance that quantifies the average contribution of each component function across a dataset.

Usage

mid.importance(
  object,
  data = NULL,
  weights = NULL,
  sort = TRUE,
  measure = 1L,
  max.nrow = 10000
)

Arguments

object

a "mid" object.

data

a data frame containing the observations to calculate the importance. If NULL, the fitted.matrix from the "mid" object is used.

weights

an optional numeric vector of sample weights.

sort

logical. If TRUE, the output data frame is sorted by importance in descending order.

measure

an integer specifying the measure of importance. Possible alternatives are 1 for the mean absolute effect, 2 for the root mean square effect, and 3 for the median absolute effect.

max.nrow

an integer specifying the maximum number of observations to retain in the predictions component of the returned object. If the number of observations exceeds this value, a weighted random sample is taken.

Value

mid.importance() returns an object of class "mid.importance". This is a list containing the following components:

importance

a data frame with the calculated importance values, sorted by default.

predictions

the matrix of the fitted or predicted MID values. If the number of observations exceeds max.nrow, this matrix contains a sampled subset.

measure

a character string describing the type of the importance measure used.

Details

The MID importance of a component function (e.g., a main effect or an interaction) is defined as the mean absolute effect on the predictions within the given data. Terms with higher importance have a larger average impact on the model's overall predictions.

See also

interpret, plot.mid.importance, ggmid.mid.importance

Examples

data(airquality, package = "datasets")
mid <- interpret(Ozone ~ .^2, data = airquality, lambda = 1)
#> 'model' not passed: response variable in 'data' is used

# Calculate MID importance using median absolute contribution
imp <- mid.importance(mid, data = airquality)
print(imp)
#> 
#> MID Importance based on 153 Observations
#> 
#> Measure: Mean Absolute Contribution
#> 
#> Importance:
#>             term importance order
#> 1           Temp   13.87962     1
#> 2           Wind   10.33981     1
#> 3        Solar.R    5.02973     1
#> 4            Day    4.89328     1
#> 5          Month    2.15851     1
#> 6   Solar.R:Wind    0.45096     2
#> 7       Temp:Day    0.40189     2
#> 8       Wind:Day    0.38731     2
#> 9  Solar.R:Month    0.38573     2
#> 10    Wind:Month    0.38048     2
#> 11     Month:Day    0.31373     2
#> 12     Wind:Temp    0.29742     2
#> 13   Solar.R:Day    0.28369     2
#> 14  Solar.R:Temp    0.21675     2
#> 15    Temp:Month    0.14530     2

# Calculate MID importance using root mean square contribution
imp <- mid.importance(mid, measure = 2)
print(imp)
#> 
#> MID Importance based on 111 Observations
#> 
#> Measure: Root Mean Square Contribution
#> 
#> Importance:
#>             term importance order
#> 1           Temp   16.23314     1
#> 2           Wind   14.77143     1
#> 3        Solar.R    7.25784     1
#> 4            Day    5.69605     1
#> 5          Month    2.66107     1
#> 6     Wind:Month    0.56396     2
#> 7   Solar.R:Wind    0.55439     2
#> 8  Solar.R:Month    0.51836     2
#> 9       Wind:Day    0.51705     2
#> 10      Temp:Day    0.48822     2
#> 11     Month:Day    0.47417     2
#> 12     Wind:Temp    0.41892     2
#> 13   Solar.R:Day    0.32831     2
#> 14  Solar.R:Temp    0.30592     2
#> 15    Temp:Month    0.20070     2