flashlight (version 1.0.0)

light_effects: Combination of Response, Predicted, Partial Dependence, and ALE profiles.

Description

Calculates response- prediction-, partial dependence, and ALE profiles of a (multi-)flashlight with respect to a covariable v.

Usage

light_effects(x, ...)
# S3 method for default
light_effects(x, ...)
# S3 method for flashlight
light_effects(
  x,
  v,
  data = NULL,
  by = x$by,
  stats = "mean",
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  use_linkinv = TRUE,
  counts_weighted = FALSE,
  v_labels = TRUE,
  pred = NULL,
  pd_indices = NULL,
  pd_n_max = 1000L,
  pd_seed = NULL,
  ale_two_sided = TRUE,
  ...
)
# S3 method for multiflashlight
light_effects(
  x,
  v,
  data = NULL,
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  ...
)

Value

An object of class "light_effects" with the following elements:

• response: A tibble containing the response profiles. Column names can be controlled by options(flashlight.column_name).

• predicted: A tibble containing the prediction profiles.

• pd: A tibble containing the partial dependence profiles.

• ale: A tibble containing the ALE profiles.

• by: Same as input by.

• v: The variable(s) evaluated.

Arguments

x

An object of class "flashlight" or "multiflashlight".

...

Further arguments passed to formatC() in forming the cut breaks of the v variable.

v

The variable name to be profiled.

data

An optional data.frame.

by

An optional vector of column names used to additionally group the results.

stats

Deprecated. Will be removed in version 1.1.0.

breaks

Cut breaks for a numeric v. Used to overwrite automatic binning via n_bins and cut_type. Ignored if v is not numeric.

n_bins

Approximate number of unique values to evaluate for numeric v. Ignored if v is not numeric or if breaks is specified.

cut_type

Should a numeric v be cut into "equal" or "quantile" bins? Ignored if v is not numeric or if breaks is specified.

use_linkinv

Should retransformation function be applied? Default is TRUE.

counts_weighted

Should counts be weighted by the case weights? If TRUE, the sum of w is returned by group.

v_labels

If FALSE, return group centers of v instead of labels. Only relevant if v is numeric with many distinct values. In that case useful for instance when different flashlights use different data sets.

pred

Optional vector with predictions (after application of inverse link). Can be used to avoid recalculation of predictions over and over if the functions is to be repeatedly called for different v and predictions are computationally expensive to make. Not implemented for multiflashlight.

pd_indices

A vector of row numbers to consider in calculating partial dependence profiles and "ale".

pd_n_max

Maximum number of ICE profiles to calculate (will be randomly picked from data) for partial dependence and ALE.

pd_seed

Integer random seed used to select ICE profiles for partial dependence and ALE.

ale_two_sided

If TRUE, v is continuous and breaks are passed or being calculated, then two-sided derivatives are calculated for ALE instead of left derivatives. More specifically: Usually, local effects at value x are calculated using points in \([x-e, x]\). Set ale_two_sided = TRUE to use points in \([x-e/2, x+e/2]\).

Methods (by class)

• light_effects(default): Default method.

• light_effects(flashlight): Profiles for a flashlight object.

• light_effects(multiflashlight): Effect profiles for a multiflashlight object.

Details

Note that ALE profiles are being calibrated by (weighted) average predictions. The resulting level might be quite different from the one of the partial dependence profiles.

See Also

light_profile(), plot.light_effects()

Examples

fit_lin <- lm(Sepal.Length ~ ., data = iris)
fl_lin <- flashlight(model = fit_lin, label = "lin", data = iris, y = "Sepal.Length")

# PDP, average response, average predicted by Species
eff <- light_effects(fl_lin, v = "Petal.Length")
plot(eff)

# PDP and ALE
plot(eff, use = c("pd", "ale"), recode_labels = c(ale = "ALE"))

# Second model with non-linear Petal.Length effect
fit_nonlin <- lm(Sepal.Length ~ . + I(Petal.Length^2), data = iris)
fl_nonlin <- flashlight(
  model = fit_nonlin, label = "nonlin", data = iris, y = "Sepal.Length"
)
fls <- multiflashlight(list(fl_lin, fl_nonlin))

# PDP and ALE
plot(light_effects(fls, v = "Petal.Length"), use = c("pd", "ale"))