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Calculate ALE data

Source: R/calc_ale.R
calc_ale.Rd

This function is not exported. It is a complete reimplementation of the ALE algorithm relative to the reference in ALEPlot::ALEPlot(). In addition to adding bootstrapping and handling of categorical y variables, it reimplements categorical x interactions.

Usage

calc_ale(
  data,
  model,
  x_cols,
  y_col,
  y_cats,
  pred_fun,
  pred_type,
  max_num_bins,
  boot_it,
  seed,
  boot_alpha,
  boot_centre,
  boot_ale_y = FALSE,
  .bins = NULL,
  ale_y_norm_funs = NULL,
  p_dist = NULL
)

Arguments

data

See documentation for ALE()

model

See documentation for ALE()

x_cols

character(1 or 2). Names of columns in X for which ALE data is to be calculated. Length 1 for 1D ALE and length 2 for 2D ALE.

y_col

character(1). Name of the target y column.

y_cats

character. The categories of y. For most cases with non-categorical y, y_cats == y_col.

pred_fun

See documentation for ALE()

pred_type

See documentation for ALE()

max_num_bins

See documentation for ALE()

boot_it

See documentation for ALE()

seed

See documentation for ALE()

boot_alpha

See documentation for ALE()

boot_centre

See documentation for ALE()

boot_ale_y

logical(1). If TRUE, return the bootstrap matrix of ALE y values. If FALSE (default) return NULL for the boot_ale_y element of the return value.

.bins

See documentation for ALE()

ale_y_norm_funs

list of functions. Custom functions for normalizing ALE y for statistics. It is usually a list(1), but for categorical y, there is a distinct function for each y category. If provided, ale_y_norm_funs saves some time since it is usually the same for all all variables throughout one call to ALE(). For now, used as a flag to determine whether statistics will be calculated or not; if NULL, statistics will not be calculated.

p_dist

See documentation for p_values in ALE()

Details

For details about arguments not documented here, see ALE().

References

Apley, Daniel W., and Jingyu Zhu. "Visualizing the effects of predictor variables in black box supervised learning models." Journal of the Royal Statistical Society Series B: Statistical Methodology 82.4 (2020): 1059-1086.

Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. doi:10.48550/arXiv.2310.09877.