Calculates response- prediction-, partial dependence, and ALE profiles of a (multi-)flashlight with respect to a covariable v
.
light_effects(x, ...)# S3 method for default
light_effects(x, ...)
# S3 method for flashlight
light_effects(
x,
v,
data = NULL,
by = x$by,
stats = c("mean", "quartiles"),
breaks = NULL,
n_bins = 11,
cut_type = c("equal", "quantile"),
use_linkinv = TRUE,
value_name = "value",
q1_name = "q1",
q3_name = "q3",
label_name = "label",
type_name = "type",
counts_name = "counts",
counts_weighted = FALSE,
v_labels = TRUE,
pred = NULL,
pd_indices = NULL,
pd_n_max = 1000,
pd_seed = NULL,
ale_two_sided = TRUE,
...
)
# S3 method for multiflashlight
light_effects(
x,
v,
data = NULL,
breaks = NULL,
n_bins = 11,
cut_type = c("equal", "quantile"),
...
)
An object of class flashlight
or multiflashlight
.
Further arguments passed to cut3
resp. formatC
in forming the cut breaks of the v
variable.
The variable to be profiled.
An optional data.frame
.
An optional vector of column names used to additionally group the results.
Statistic to calculate for the response profile: "mean" or "quartiles".
Cut breaks for a numeric v
.
Maxmium number of unique values to evaluate for numeric v
.
For the default "equal", bins of equal width are created for v
by pretty
. Choose "quantile" to create quantile bins (recommended if interested in ALE).
Should retransformation function be applied? Default is TRUE.
Column name in resulting data objects containing the profile value. Defaults to "value".
Name of the resulting column with first quartile values. Only relevant for stats
"quartiles".
Name of the resulting column with third quartile values. Only relevant for stats
"quartiles".
Column name in resulting data
containing the label of the flashlight. Defaults to "label".
Name of the column in data
containing type
.
Name of the column containing counts.
Should counts be weighted by the case weights? If TRUE, the sum of w
is returned by group.
If FALSE, return group centers of v
instead of labels. Only relevant if v
is numeric with many distinct values. In that case useful if e.g. different flashlights use different data sets.
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.
A vector of row numbers to consider in calculating partial dependence and ALE profiles. Useful to force all flashlights to use the same basis for calculations of partial dependence and ALE.
Maximum number of ICE profiles to consider for partial depencence and ALE calculation (will be randomly picked from data
).
An integer random seed used to sample ICE profiles for partial dependence and ALE.
If TRUE
, v
is continuous and breaks
are passed or being calculated, then two-sided derivatives are calculated for ALE instead of left derivatives. This aligns the results better with the x labels. More specifically: Usually, local effects at value x are calculated using points between x-e and x. Set ale_two_sided = TRUE
to use points between x-e/2 and x+e/2.
An object of classes light_effects
, light
(and a list) with the following elements.
response
A tibble containing the response profiles.
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.
stats
Same as input stats
.
value_name
Same as input value_name
.
q1_name
Same as input q1_name
.
q3_name
Same as input q3_name
.
label_name
Same as input label_name
.
type_name
Same as input type
.
counts_name
Same as input counts_name
.
default
: Default method.
flashlight
: Profiles for a flashlight object.
multiflashlight
: Effect profiles for a multiflashlight object.
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.
# NOT RUN {
fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
light_effects(fl, v = "Species")
# }
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