DEPRECATED - Add SHAP values to (multi-)flashlight

Description

The function calls light_breakdown() for n_shap observations and adds the resulting (approximate) SHAP decompositions as static element "shap" to the (multi)-flashlight for further analyses.

Usage

add_shap(x, ...)

## Default S3 method:
add_shap(x, ...)

## S3 method for class 'flashlight'
add_shap(
  x,
  v = NULL,
  visit_strategy = c("permutation", "importance", "v"),
  n_shap = 200,
  n_max = Inf,
  n_perm = 12,
  seed = NULL,
  use_linkinv = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'multiflashlight'
add_shap(x, ...)

Arguments

Details

We offer two approximations to SHAP: For visit_strategy = "importance", the breakdown algorithm (see reference) is used with importance based visit order. Use the default visit_strategy = "permutation" to run breakdown for multiple random permutations, averaging the results. This approximation will be closer to exact SHAP values, but very slow. Most available arguments can be chosen to reduce computation time.

Value

An object of class "flashlight" or "multiflashlight" with additional element "shap" of class "shap" (and "list").

Methods (by class)

• add_shap(default): Default method not implemented yet.

• add_shap(flashlight): Variable attribution to single observation for a flashlight.

• add_shap(multiflashlight): Add SHAP to multiflashlight.

References

A. Gosiewska and P. Biecek (2019). IBREAKDOWN: Uncertainty of model explanations for non-additive predictive models. ArXiv <arxiv.org/abs/1903.11420>.

Examples

## Not run: 
fit <- lm(Sepal.Length ~ . + Petal.Length:Species, data = iris)
x <- flashlight(model = fit, label = "lm", data = iris, y = "Sepal.Length")
x <- add_shap(x)
is.shap(x$shap)
plot(light_importance(x, type = "shap"))
plot(light_scatter(x, type = "shap", v = "Petal.Length"))
plot(light_scatter(x, type = "shap", v = "Petal.Length", by = "Species"))

## End(Not run)

all_identical

Description

Checks if an aspect is identical for all elements in a nested list. The aspect is specified by fun, e.g., [[, followed by the element name to compare.

Usage

all_identical(x, fun, ...)

Arguments

Value

A logical vector of length one.

Examples

x <- list(a = 1, b = 2)
y <- list(a = 1, b = 3)
all_identical(list(x, y), `[[`, "a")
all_identical(list(x, y), `[[`, "b")

Discretizes a Vector

Description

This function takes a vector x and returns a list with information on disretized version of x. The construction of level names can be controlled by passing ... arguments to formatC().

Usage

auto_cut(
  x,
  breaks = NULL,
  n_bins = 27L,
  cut_type = c("equal", "quantile"),
  x_name = "value",
  level_name = "level",
  ...
)

Arguments

Value

A list with the following elements:

• data: A data.frame with colums x_name and level_name each with the same length as x. The column x_name has values in output bin_means while the column level_name has values in bin_labels.

• breaks: A vector of increasing and unique breaks used to cut a numeric x with too many distinct levels. NULL otherwise.

• bin_means: The midpoints of subsequent breaks, or if there are no breaks in the output, factor levels or distinct values of x.

• bin_labels: Break labels of the form "(low, high]" if there are breaks in the output, otherwise the same as bin_means. Same order as bin_means.

Examples

auto_cut(1:10, n_bins = 3)
auto_cut(c(NA, 1:10), n_bins = 3)
auto_cut(1:10, breaks = 3:4, n_bins = 3)
auto_cut(1:10, n_bins = 3, cut_type = "quantile")
auto_cut(LETTERS[4:1], n_bins = 2)
auto_cut(factor(LETTERS[1:4], LETTERS[4:1]), n_bins = 2)
auto_cut(990:1100, n_bins = 3, big.mark = "'", format = "fg")
auto_cut(c(0.0001, 0.0002, 0.0003, 0.005), n_bins = 3, format = "fg")

Modified cut

Description

Slightly modified version of cut.default(). Both modifications refer to the construction of break labels. Firstly, ... arguments are passed to formatC() in formatting the numbers in the labels. Secondly, a separator between the two numbers can be specified with default ", ".

Usage

cut3(
  x,
  breaks,
  labels = NULL,
  include.lowest = FALSE,
  right = TRUE,
  dig.lab = 3L,
  ordered_result = FALSE,
  sep = ", ",
  ...
)

Arguments

Value

Vector of the same length as x.

Examples

x <- 998:1001
cut3(x, breaks = 2)
cut3(x, breaks = 2, big.mark = "'", sep = ":")

Create or Update a flashlight

Description

Creates or updates a "flashlight" object. If a flashlight is to be created, all arguments are optional except label. If a flashlight is to be updated, all arguments are optional up to x (the flashlight to be updated).

Usage

flashlight(x, ...)

## Default S3 method:
flashlight(
  x,
  model = NULL,
  data = NULL,
  y = NULL,
  predict_function = stats::predict,
  linkinv = function(z) z,
  w = NULL,
  by = NULL,
  metrics = list(rmse = MetricsWeighted::rmse),
  label = NULL,
  shap = NULL,
  ...
)

## S3 method for class 'flashlight'
flashlight(x, check = TRUE, ...)

Arguments

Value

An object of class "flashlight" (and list) containing each input (except x) as element.

Methods (by class)

• flashlight(default): Used to create a flashlight object. No x has to be passed in this case.

• flashlight(flashlight): Used to update an existing flashlight object.

See Also

multiflashlight()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
(fl <- flashlight(model = fit, data = iris, y = "Sepal.Length", label = "ols"))
(fl_updated <- flashlight(fl, linkinv = exp))

Grouped, weighted mean centering

Description

Centers a numeric variable within optional groups and optional weights. The order of values is unchanged.

Usage

grouped_center(data, x, w = NULL, by = NULL, ...)

Arguments

Value

A numeric vector with centered values in column x.

Examples

ir <- data.frame(iris, w = 1)
mean(grouped_center(ir, "Sepal.Width"))
rowsum(grouped_center(ir, "Sepal.Width", by = "Species"), ir$Species)
mean(grouped_center(ir, "Sepal.Width", w = "w"))
rowsum(grouped_center(ir, "Sepal.Width", by = "Species", w = "w"), ir$Species)

Grouped count

Description

Calculates weighted counts grouped by optional columns.

Usage

grouped_counts(data, by = NULL, w = NULL, value_name = "n", ...)

Arguments

Value

A data.frame with columns by and value_name.

Examples

grouped_counts(iris)
grouped_counts(iris, by = "Species")
grouped_counts(iris, w = "Petal.Length")
grouped_counts(iris, by = "Species", w = "Petal.Length")

Grouped Weighted Means, Quartiles, or Variances

Description

Calculates weighted means, quartiles, or variances (and counts) of a variable grouped by optional columns. By default, counts are not weighted, even if there is a weighting variable.

Usage

grouped_stats(
  data,
  x,
  w = NULL,
  by = NULL,
  stats = c("mean", "quartiles", "variance"),
  counts = TRUE,
  counts_weighted = FALSE,
  counts_name = "counts",
  value_name = x,
  q1_name = "q1",
  q3_name = "q3",
  ...
)

Arguments

Value

A data.frame with columns by, x, and optionally counts_name.

Examples

grouped_stats(iris, "Sepal.Width")
grouped_stats(iris, "Sepal.Width", stats = "quartiles")
grouped_stats(iris, "Sepal.Width", stats = "variance")
grouped_stats(iris, "Sepal.Width", w = "Petal.Width", counts_weighted = TRUE)
grouped_stats(iris, "Sepal.Width", by = "Species")

Fast Grouped Weighted Mean

Description

Fast version of grouped_stats(..., counts = FALSE). Works if there is at most one "by" variable.

Usage

grouped_weighted_mean(
  data,
  x,
  w = NULL,
  by = NULL,
  na.rm = TRUE,
  value_name = x
)

Arguments

Value

A data.frame with grouped weighted means.

Examples

n <- 100
data <- data.frame(
  x = rnorm(n),
  w = runif(n),
  group = factor(sample(1:3, n, TRUE))
)
grouped_weighted_mean(data, x = "x", w = "w", by = "group")

Check functions for flashlight Classes

Description

Checks if an object inherits specific class relevant for the flashlight package.

Usage

is.flashlight(x)

is.multiflashlight(x)

is.light(x)

is.light_performance(x)

is.light_performance_multi(x)

is.light_importance(x)

is.light_importance_multi(x)

is.light_breakdown(x)

is.light_breakdown_multi(x)

is.light_ice(x)

is.light_ice_multi(x)

is.light_profile(x)

is.light_profile_multi(x)

is.light_profile2d(x)

is.light_profile2d_multi(x)

is.light_effects(x)

is.light_effects_multi(x)

is.shap(x)

is.light_scatter(x)

is.light_scatter_multi(x)

is.light_global_surrogate(x)

is.light_global_surrogate_multi(x)

Arguments

Value

A logical vector of length one.

Functions

• is.multiflashlight(): Check for multiflashlight object.

• is.light(): Check for light object.

• is.light_performance(): Check for light_performance object.

• is.light_performance_multi(): Check for light_performance_multi object.

• is.light_importance(): Check for light_importance object.

• is.light_importance_multi(): Check for light_importance_multi object.

• is.light_breakdown(): Check for light_breakdown object.

• is.light_breakdown_multi(): Check for light_breakdown_multi object.

• is.light_ice(): Check for light_ice object.

• is.light_ice_multi(): Check for light_ice_multi object.

• is.light_profile(): Check for light_profile object.

• is.light_profile_multi(): Check for light_profile_multi object.

• is.light_profile2d(): Check for light_profile2d object.

• is.light_profile2d_multi(): Check for light_profile2d_multi object.

• is.light_effects(): Check for light_effects object.

• is.light_effects_multi(): Check for light_effects_multi object.

• is.shap(): Check for shap object.

• is.light_scatter(): Check for light_scatter object.

• is.light_scatter_multi(): Check for light_scatter_multi object.

• is.light_global_surrogate(): Check for light_global_surrogate object.

• is.light_global_surrogate_multi(): Check for light_global_surrogate_multi object.

Examples

a <- flashlight(label = "a")
is.flashlight(a)
is.flashlight("a")

Variable Contribution Breakdown for Single Observation

Description

Calculates sequential additive variable contributions (approximate SHAP) to the prediction of a single observation, see Gosiewska and Biecek (see reference) and the details below.

Usage

light_breakdown(x, ...)

## Default S3 method:
light_breakdown(x, ...)

## S3 method for class 'flashlight'
light_breakdown(
  x,
  new_obs,
  data = x$data,
  by = x$by,
  v = NULL,
  visit_strategy = c("importance", "permutation", "v"),
  n_max = Inf,
  n_perm = 20,
  seed = NULL,
  use_linkinv = FALSE,
  description = TRUE,
  digits = 2,
  ...
)

## S3 method for class 'multiflashlight'
light_breakdown(x, ...)

Arguments

Details

The breakdown algorithm works as follows: First, the visit order (x_1, ..., x_m) of the variables v is specified. Then, in the query data, the column x_1 is set to the value of x_1 of the single observation new_obs to be explained. The change in the (weighted) average prediction on data measures the contribution of x_1 on the prediction of new_obs. This procedure is iterated over all x_i until eventually, all rows in data are identical to new_obs.

A complication with this approach is that the visit order is relevant, at least for non-additive models. Ideally, the algorithm could be repeated for all possible permutations of v and its results averaged per variable. This is basically what SHAP values do, see the reference below for an explanation. Unfortunately, there is no efficient way to do this in a model agnostic way.

We offer two visit strategies to approximate SHAP:

1. "importance": Using the short-cut described in the reference below: The variables are sorted by the size of their contribution in the same way as the breakdown algorithm but without iteration, i.e., starting from the original query data for each variable x_i.

2. "permutation": Averages contributions from a small number of random permutations of v.

Note that the minimum required elements in the (multi-)flashlight are a "predict_function", "model", and "data". The latter can also directly be passed to light_breakdown(). Note that by default, no retransformation function is applied.

Value

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

• data A tibble with results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

Methods (by class)

• light_breakdown(default): Default method not implemented yet.

• light_breakdown(flashlight): Variable attribution to single observation for a flashlight.

• light_breakdown(multiflashlight): Variable attribution to single observation for a multiflashlight.

References

A. Gosiewska and P. Biecek (2019). IBREAKDOWN: Uncertainty of model explanations for non-additive predictive models. ArXiv.

See Also

plot.light_breakdown()

Examples

fit <- lm(Sepal.Length ~ . + Petal.Length:Species, data = iris)
fl <- flashlight(model = fit, label = "lm", data = iris, y = "Sepal.Length")
light_breakdown(fl, new_obs = iris[1, ])

Check flashlight

Description

Checks if an object of class "flashlight" or "multiflashlight" is consistently defined.

Usage

light_check(x, ...)

## Default S3 method:
light_check(x, ...)

## S3 method for class 'flashlight'
light_check(x, ...)

## S3 method for class 'multiflashlight'
light_check(x, ...)

Arguments

Value

The input x or an error message.

Methods (by class)

• light_check(default): Default check method not implemented yet.

• light_check(flashlight): Checks if a flashlight object is consistently defined.

• light_check(multiflashlight): Checks if a multiflashlight object is consistently defined.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fit_log <- lm(log(Sepal.Length) ~ ., data = iris)
fl <- flashlight(fit, data = iris, y = "Sepal.Length", label = "ols")
fl_log <- flashlight(fit_log,  y = "Sepal.Length", label = "ols", linkinv = exp)
light_check(fl)
light_check(fl_log)

Combine Objects

Description

Combines a list of similar objects each of class "light" by row binding data.frame slots and retaining the other slots from the first list element.

Usage

light_combine(x, ...)

## Default S3 method:
light_combine(x, ...)

## S3 method for class 'light'
light_combine(x, new_class = NULL, ...)

## S3 method for class 'list'
light_combine(x, new_class = NULL, ...)

Arguments

Value

If x is a list, an object like each element but with unioned rows in data slots.

Methods (by class)

• light_combine(default): Default method not implemented yet.

• light_combine(light): Since there is nothing to combine, the input is returned except for additional classes.

• light_combine(list): Combine a list of similar light objects.

Examples

fit_lm <- lm(Sepal.Length ~ ., data = iris)
fit_glm <- glm(Sepal.Length ~ ., family = Gamma(link = "log"), data = iris)
mod_lm <- flashlight(model = fit_lm, label = "lm", data = iris, y = "Sepal.Length")
mod_glm <- flashlight(
  model = fit_glm,
  label = "glm",
  data = iris,
  y = "Sepal.Length",
  predict_function = function(object, newdata)
    predict(object, newdata, type = "response")
)
mods <- multiflashlight(list(mod_lm, mod_glm))
perf_lm <- light_performance(mod_lm)
perf_glm <- light_performance(mod_glm)
manual_comb <- light_combine(
  list(perf_lm, perf_glm),
  new_class = "light_performance_multi"
)
auto_comb <- light_performance(mods)
all.equal(manual_comb, auto_comb)

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, ...)

## Default S3 method:
light_effects(x, ...)

## S3 method for class 'flashlight'
light_effects(
  x,
  v,
  data = NULL,
  by = x$by,
  stats = c("mean", "quartiles"),
  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 class 'multiflashlight'
light_effects(
  x,
  v,
  data = NULL,
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  ...
)

Arguments

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.

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.

• stats: Same as input stats.

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.

See Also

light_profile(), plot.light_effects()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
light_effects(fl, v = "Species")

Global Surrogate Tree

Description

Model predictions are modelled by a single decision tree, serving as an easy to interprete surrogate to the original model. As suggested in Molnar (see reference below), the quality of the surrogate tree can be measured by its R-squared. The size of the tree can be modified by passing ... arguments to rpart::rpart().

Usage

light_global_surrogate(x, ...)

## Default S3 method:
light_global_surrogate(x, ...)

## S3 method for class 'flashlight'
light_global_surrogate(
  x,
  data = x$data,
  by = x$by,
  v = NULL,
  use_linkinv = TRUE,
  n_max = Inf,
  seed = NULL,
  keep_max_levels = 4L,
  ...
)

## S3 method for class 'multiflashlight'
light_global_surrogate(x, ...)

Arguments

Value

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

• data A tibble with results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

Methods (by class)

• light_global_surrogate(default): Default method not implemented yet.

• light_global_surrogate(flashlight): Surrogate model for a flashlight.

• light_global_surrogate(multiflashlight): Surrogate model for a multiflashlight.

References

Molnar C. (2019). Interpretable Machine Learning.

See Also

plot.light_global_surrogate()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
x <- flashlight(model = fit, label = "lm", data = iris)
light_global_surrogate(x)

Individual Conditional Expectation (ICE)

Description

Generates Individual Conditional Expectation (ICE) profiles. An ICE profile shows how the prediction of an observation changes if one or multiple variables are systematically changed across its ranges, holding all other values fixed (see the reference below for details). The curves can be centered in order to increase visibility of interaction effects.

Usage

light_ice(x, ...)

## Default S3 method:
light_ice(x, ...)

## S3 method for class 'flashlight'
light_ice(
  x,
  v = NULL,
  data = x$data,
  by = x$by,
  evaluate_at = NULL,
  breaks = NULL,
  grid = NULL,
  n_bins = 27L,
  cut_type = c("equal", "quantile"),
  indices = NULL,
  n_max = 20L,
  seed = NULL,
  use_linkinv = TRUE,
  center = c("no", "first", "middle", "last", "mean", "0"),
  ...
)

## S3 method for class 'multiflashlight'
light_ice(x, ...)

Arguments

Details

There are two ways to specify the variable(s) to be profiled.

1. Pass the variable name via v and an optional vector with evaluation points evaluate_at (or breaks). This works for dependence on a single variable.

2. More general: Specify any grid as a data.frame with one or more columns. For instance, it can be generated by a call to expand.grid().

The minimum required elements in the (multi-)flashlight are "predict_function", "model", "linkinv" and "data", where the latest can be passed on the fly.

Which rows in data are profiled? This is specified by indices. If not given and n_max is smaller than the number of rows in data, then row indices will be sampled randomly from data. If the same rows should be used for all flashlights in a multiflashlight, there are two options: Either pass a seed or a vector of indices used to select rows. In both cases, data should be the same for all flashlights considered.

Value

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

• data A tibble containing the results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

• v The variable(s) evaluated.

• center How centering was done.

Methods (by class)

• light_ice(default): Default method not implemented yet.

• light_ice(flashlight): ICE profiles for a flashlight object.

• light_ice(multiflashlight): ICE profiles for a multiflashlight object.

References

Goldstein, A. et al. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24:1 <doi.org/10.1080/10618600.2014.907095>.

See Also

light_profile(), plot.light_ice()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "lm", data = iris)
light_ice(fl, v = "Species")

Variable Importance

Description

Two algorithms to calculate variable importance are available:

1. Permutation importance, and

2. SHAP importance

Algorithm 1 measures importance of variable v as the drop in performance by permuting the values of v, see Fisher et al. 2018 (reference below). Algorithm 2 measures variable importance by averaging absolute SHAP values.

Usage

light_importance(x, ...)

## Default S3 method:
light_importance(x, ...)

## S3 method for class 'flashlight'
light_importance(
  x,
  data = x$data,
  by = x$by,
  type = c("permutation", "shap"),
  v = NULL,
  n_max = Inf,
  seed = NULL,
  m_repetitions = 1L,
  metric = x$metrics[1L],
  lower_is_better = TRUE,
  use_linkinv = FALSE,
  ...
)

## S3 method for class 'multiflashlight'
light_importance(x, ...)

Arguments

Details

For Algorithm 1, the minimum required elements in the (multi-)flashlight are "y", "predict_function", "model", "data" and "metrics". For Algorithm 2, the only required element is "shap". Call add_shap() once to add such object.

Note: The values of the permutation Algorithm 1. are on the scale of the selected metric. For SHAP Algorithm 2, the values are on the scale of absolute values of the predictions.

Value

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

• data A tibble with results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

• type Same as input type. For information only.

Methods (by class)

• light_importance(default): Default method not implemented yet.

• light_importance(flashlight): Variable importance for a flashlight.

• light_importance(multiflashlight): Variable importance for a multiflashlight.

References

Fisher A., Rudin C., Dominici F. (2018). All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance. Arxiv.

See Also

most_important(), plot.light_importance()

Examples

fit <- lm(Sepal.Length ~ Petal.Length, data = iris)
fl <- flashlight(model = fit, label = "full", data = iris, y = "Sepal.Length")
light_importance(fl)

Interaction Strength

Description

This function provides Friedman's H statistic for overall interaction strength per covariable as well as its version for pairwise interactions, see the reference below.

Usage

light_interaction(x, ...)

## Default S3 method:
light_interaction(x, ...)

## S3 method for class 'flashlight'
light_interaction(
  x,
  data = x$data,
  by = x$by,
  v = NULL,
  pairwise = FALSE,
  type = c("H", "ice"),
  normalize = TRUE,
  take_sqrt = TRUE,
  grid_size = 200L,
  n_max = 1000L,
  seed = NULL,
  use_linkinv = FALSE,
  ...
)

## S3 method for class 'multiflashlight'
light_interaction(x, ...)

Arguments

Details

As a fast alternative to assess overall interaction strength, with type = "ice", the function offers a method based on centered ICE curves: The corresponding H* statistic measures how much of the variability of a c-ICE curve is unexplained by the main effect. As for Friedman's H statistic, it can be useful to consider unnormalized or squared values (see Details below).

Friedman's H statistic relates the interaction strength of a variable (pair) to the total effect strength of that variable (pair) based on partial dependence curves. Due to this normalization step, even variables with low importance can have high values for H. The function light_interaction() offers the option to skip normalization in order to have a more direct comparison of the interaction effects across variable (pairs). The values of such unnormalized H statistics are on the scale of the response variable. Use take_sqrt = FALSE to return squared values of H. Note that in general, for each variable (pair), predictions are done on a data set with grid_size * n_max, so be cautious with increasing the defaults too much. Still, even with larger grid_size and n_max, there might be considerable variation across different runs, thus, setting a seed is recommended.

The minimum required elements in the (multi-) flashlight are a "predict_function", "model", and "data".

Value

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

• data A tibble containing the results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

• type Same as input type. For information only.

Methods (by class)

• light_interaction(default): Default method not implemented yet.

• light_interaction(flashlight): Interaction strengths for a flashlight object.

• light_interaction(multiflashlight): for a multiflashlight object.

References

Friedman, J. H. and Popescu, B. E. (2008). "Predictive learning via rule ensembles." The Annals of Applied Statistics. JSTOR, 916–54.

See Also

light_ice()

Examples

v <- c("Petal.Length", "Petal.Width")
fit_add <- stats::lm(Sepal.Length ~ Petal.Length + Petal.Width, data = iris)
fit_nonadd <- stats::lm(Sepal.Length ~ Petal.Length * Petal.Width, data = iris)
fl_add <- flashlight(model = fit_add, label = "additive")
fl_nonadd <- flashlight(model = fit_nonadd, label = "nonadditive")
fls <- multiflashlight(list(fl_add, fl_nonadd), data = iris)
plot(st <- light_interaction(fls, v = v), fill = "darkgreen")
plot(light_interaction(fls, v = v, pairwise = TRUE), fill = "darkgreen")
plot(st <- light_interaction(fls, v = v, by = "Species"), fill = "darkgreen")

Model Performance of Flashlight

Description

Calculates performance of a flashlight with respect to one or more performance measure.

Usage

light_performance(x, ...)

## Default S3 method:
light_performance(x, ...)

## S3 method for class 'flashlight'
light_performance(
  x,
  data = x$data,
  by = x$by,
  metrics = x$metrics,
  use_linkinv = FALSE,
  ...
)

## S3 method for class 'multiflashlight'
light_performance(x, ...)

Arguments

Details

The minimal required elements in the (multi-) flashlight are "y", "predict_function", "model", "data" and "metrics". The latter two can also directly be passed to light_performance(). Note that by default, no retransformation function is applied.

Value

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

• data: A tibble containing the results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Same as input by.

Methods (by class)

• light_performance(default): Default method not implemented yet.

• light_performance(flashlight): Model performance of flashlight object.

• light_performance(multiflashlight): Model performance of multiflashlight object.

See Also

plot.light_performance()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "lm", data = iris, y = "Sepal.Length")
light_performance(fl)
light_performance(fl, by = "Species")

Partial Dependence and other Profiles

Description

Calculates different types of profiles across covariable values. By default, partial dependence profiles are calculated (see Friedman). Other options are profiles of ALE (accumulated local effects, see Apley), response, predicted values ("M plots" or "marginal plots", see Apley), residuals, and shap. The results are aggregated either by (weighted) means or by (weighted) quartiles.

Note that ALE profiles are calibrated by (weighted) average predictions. In contrast to the suggestions in Apley, we calculate ALE profiles of factors in the same order as the factor levels. They are not being reordered based on similiarity of other variables.

Usage

light_profile(x, ...)

## Default S3 method:
light_profile(x, ...)

## S3 method for class 'flashlight'
light_profile(
  x,
  v = NULL,
  data = NULL,
  by = x$by,
  type = c("partial dependence", "ale", "predicted", "response", "residual", "shap"),
  stats = c("mean", "quartiles"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  use_linkinv = TRUE,
  counts = TRUE,
  counts_weighted = FALSE,
  v_labels = TRUE,
  pred = NULL,
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  pd_indices = NULL,
  pd_n_max = 1000L,
  pd_seed = NULL,
  pd_center = c("no", "first", "middle", "last", "mean", "0"),
  ale_two_sided = FALSE,
  ...
)

## S3 method for class 'multiflashlight'
light_profile(
  x,
  v = NULL,
  data = NULL,
  type = c("partial dependence", "ale", "predicted", "response", "residual", "shap"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = c("equal", "quantile"),
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  ...
)

Arguments

Details

Numeric covariables v with more than n_bins disjoint values are binned into n_bins bins. Alternatively, breaks can be provided to specify the binning. For partial dependence profiles (and partly also ALE profiles), this behaviour can be overwritten either by providing a vector of evaluation points (pd_evaluate_at) or an evaluation pd_grid. By the latter we mean a data frame with column name(s) with a (multi-)variate evaluation grid.

For partial dependence, ALE, and prediction profiles, "model", "predict_function", "linkinv" and "data" are required. For response profiles its "y", "linkinv" and "data", and for shap profiles it is just "shap". "data" can be passed on the fly.

Value

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

• data A tibble containing results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Names of group by variable.

• v The variable(s) evaluated.

• type Same as input type. For information only.

• stats Same as input stats.

Methods (by class)

• light_profile(default): Default method not implemented yet.

• light_profile(flashlight): Profiles for flashlight.

• light_profile(multiflashlight): Profiles for multiflashlight.

References

• Friedman J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29:1189–1232.

• Apley D. W. (2016). Visualizing the effects of predictor variables in black box supervised learning models.

See Also

light_effects(), plot.light_profile()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
light_profile(fl, v = "Species")
light_profile(fl, v = "Petal.Width", type = "residual")

2D Partial Dependence and other 2D Profiles

Description

Calculates different types of 2D-profiles across two variables. By default, partial dependence profiles are calculated (see Friedman). Other options are response, predicted values, residuals, and shap. The results are aggregated by (weighted) means.

Usage

light_profile2d(x, ...)

## Default S3 method:
light_profile2d(x, ...)

## S3 method for class 'flashlight'
light_profile2d(
  x,
  v = NULL,
  data = NULL,
  by = x$by,
  type = c("partial dependence", "predicted", "response", "residual", "shap"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = "equal",
  use_linkinv = TRUE,
  counts = TRUE,
  counts_weighted = FALSE,
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  pd_indices = NULL,
  pd_n_max = 1000L,
  pd_seed = NULL,
  ...
)

## S3 method for class 'multiflashlight'
light_profile2d(
  x,
  v = NULL,
  data = NULL,
  type = c("partial dependence", "predicted", "response", "residual", "shap"),
  breaks = NULL,
  n_bins = 11L,
  cut_type = "equal",
  pd_evaluate_at = NULL,
  pd_grid = NULL,
  ...
)

Arguments

Details

Different binning options are available, see arguments below. For high resolution partial dependence plots, it might be necessary to specify breaks, pd_evaluate_at or pd_grid in order to avoid empty parts in the plot. A high value of n_bins might not have the desired effect as it internally capped at the number of distinct values of a variable.

For partial dependence and prediction profiles, "model", "predict_function", "linkinv" and "data" are required. For response profiles it is "y", "linkinv" and "data" and for shap profiles it is just "shap". "data" can be passed on the fly.

Value

An object of class "light_profile2d" with the following elements:

• data A tibble containing results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by Names of group by variables.

• v The two variable names evaluated.

• type Same as input type. For information only.

Methods (by class)

• light_profile2d(default): Default method not implemented yet.

• light_profile2d(flashlight): 2D profiles for flashlight.

• light_profile2d(multiflashlight): 2D profiles for multiflashlight.

References

Friedman J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29:1189–1232.

See Also

light_profile(), plot.light_profile2d()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
light_profile2d(fl, v = c("Petal.Length", "Species"))

Recode Factor Columns

Description

Recodes factor levels of columns in data slots of an object of class "light".

Usage

light_recode(x, ...)

## Default S3 method:
light_recode(x, ...)

## S3 method for class 'light'
light_recode(x, what, levels, labels, ...)

Arguments

Value

x with new factor levels of type_name column.

Methods (by class)

• light_recode(default): Default method not implemented yet.

• light_recode(light): Recoding factors in data slots of "light" object.

See Also

plot.light_effects().

Examples

fit_full <- lm(Sepal.Length ~ ., data = iris)
fit_part <- lm(Sepal.Length ~ Petal.Length, data = iris)
mod_full <- flashlight(
  model = fit_full, label = "full", data = iris, y = "Sepal.Length"
)
mod_part <- flashlight(
  model = fit_part, label = "part", data = iris, y = "Sepal.Length"
)
mods <- multiflashlight(list(mod_full, mod_part))
eff <- light_effects(mods, v = "Species")
eff <- light_recode(
  eff,
  what = "type_name",
  levels = c("response", "predicted", "partial dependence", "ale"),
  labels = c("Observed", "Fitted", "PD", "ALE")
)
plot(eff, use = "all")

Scatter

Description

This function prepares values for drawing a scatter plot of predicted values, responses, residuals, or SHAP values against a selected variable.

Usage

light_scatter(x, ...)

## Default S3 method:
light_scatter(x, ...)

## S3 method for class 'flashlight'
light_scatter(
  x,
  v,
  data = x$data,
  by = x$by,
  type = c("predicted", "response", "residual", "shap"),
  use_linkinv = TRUE,
  n_max = 400,
  seed = NULL,
  ...
)

## S3 method for class 'multiflashlight'
light_scatter(x, ...)

Arguments

Value

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

• data: A tibble with results. Can be used to build fully customized visualizations. Column names can be controlled by options(flashlight.column_name).

• by: Same as input by.

• v: The variable evaluated.

• type: Same as input type. For information only.

Methods (by class)

• light_scatter(default): Default method not implemented yet.

• light_scatter(flashlight): Variable profile for a flashlight.

• light_scatter(multiflashlight): light_scatter for a multiflashlight.

See Also

plot.light_scatter()

Examples

fit_a <- lm(Sepal.Length ~ . -Petal.Length, data = iris)
fit_b <- lm(Sepal.Length ~ ., data = iris)
fl_a <- flashlight(model = fit_a, label = "without Petal.Length")
fl_b <- flashlight(model = fit_b, label = "all")
fls <- multiflashlight(list(fl_a, fl_b), data = iris, y = "Sepal.Length")
pr <- light_scatter(fls, v = "Petal.Length")
plot(
  light_scatter(fls, "Petal.Length", by = "Species", type = "residual"),
  alpha = 0.2
)

Most Important Variables.

Description

Returns the most important variable names sorted descendingly.

Usage

most_important(x, top_m = Inf)

## Default S3 method:
most_important(x, top_m = Inf)

## S3 method for class 'light_importance'
most_important(x, top_m = Inf)

Arguments

Value

A character vector of variable names sorted in descending order by importance.

Methods (by class)

• most_important(default): Default method not implemented yet.

• most_important(light_importance): Extracts most important variables from an object of class "light_importance".

See Also

light_importance()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "ols", data = iris, y = "Sepal.Length")
(imp <- light_importance(fl, seed = 4))
most_important(imp)
most_important(imp, 2)

Create or Update a multiflashlight

Description

Combines a list of flashlights to an object of class "multiflashlight" and/or updates a multiflashlight.

Usage

multiflashlight(x, ...)

## Default S3 method:
multiflashlight(x, ...)

## S3 method for class 'flashlight'
multiflashlight(x, ...)

## S3 method for class 'list'
multiflashlight(x, ...)

## S3 method for class 'multiflashlight'
multiflashlight(x, ...)

Arguments

Value

An object of class "multiflashlight" (a named list of flashlight objects).

Methods (by class)

• multiflashlight(default): Used to create a flashlight object. No x has to be passed in this case.

• multiflashlight(flashlight): Updates an existing flashlight object and turns into a multiflashlight.

• multiflashlight(list): Creates (and updates) a multiflashlight from a list of flashlights.

• multiflashlight(multiflashlight): Updates an object of class "multiflashlight".

See Also

flashlight()

Examples

fit_lm <- lm(Sepal.Length ~ ., data = iris)
fit_glm <- glm(Sepal.Length ~ ., family = Gamma(link = log), data = iris)
mod_lm <- flashlight(model = fit_lm, label = "lm")
mod_glm <- flashlight(model = fit_glm, label = "glm")
(mods <- multiflashlight(list(mod_lm, mod_glm)))

Visualize Variable Contribution Breakdown for Single Observation

Description

Minimal visualization of an object of class "light_breakdown" as waterfall plot. The object returned is of class "ggplot" and can be further customized.

Usage

## S3 method for class 'light_breakdown'
plot(x, facet_scales = "free", facet_ncol = 1, rotate_x = FALSE, ...)

Arguments

Details

The waterfall plot is to be read from top to bottom. The first line describes the (weighted) average prediction in the query data used to start with. Then, each additional line shows how the prediction changes due to the impact of the corresponding variable. The last line finally shows the original prediction of the selected observation. Multiple flashlights are shown in different facets. Positive and negative impacts are visualized with different colors.

Value

An object of class "ggplot".

See Also

light_breakdown()

Examples

fit <- lm(Sepal.Length ~ . + Petal.Length:Species, data = iris)
fl <- flashlight(model = fit, label = "lm", data = iris, y = "Sepal.Length")
plot(light_breakdown(fl, new_obs = iris[1, ]))

Visualize Multiple Types of Profiles Together

Description

Visualizes response-, prediction-, partial dependence, and/or ALE profiles of a (multi-)flashlight with respect to a covariable v. Different flashlights or a single flashlight with one "by" variable are separated by a facet wrap.

Usage

## S3 method for class 'light_effects'
plot(
  x,
  use = c("response", "predicted", "pd"),
  zero_counts = TRUE,
  size_factor = 1,
  facet_scales = "free_x",
  facet_nrow = 1L,
  rotate_x = TRUE,
  show_points = TRUE,
  ...
)

Arguments

Value

An object of class "ggplot".

See Also

light_effects(), plot_counts()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
plot(light_effects(fl, v = "Species"))

Plot Global Surrogate Trees

Description

Use rpart.plot::rpart.plot() to visualize trees fitted by light_global_surrogate().

Usage

## S3 method for class 'light_global_surrogate'
plot(x, type = 5, auto_main = TRUE, mfrow = NULL, ...)

Arguments

Value

An object of class "ggplot".

See Also

light_global_surrogate()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
x <- flashlight(model = fit, label = "lm", data = iris)
plot(light_global_surrogate(x))

Visualize ICE profiles

Description

Minimal visualization of an object of class "light_ice" as ggplot2::geom_line(). The object returned is of class "ggplot" and can be further customized.

Usage

## S3 method for class 'light_ice'
plot(x, facet_scales = "fixed", rotate_x = FALSE, ...)

Arguments

Details

Each observation is visualized by a line. The first "by" variable is represented by the color, a second "by" variable or a multiflashlight by facets.

Value

An object of class "ggplot".

See Also

light_ice()

Examples

fit_full <- lm(Sepal.Length ~ ., data = iris)
fit_part <- lm(Sepal.Length ~ Petal.Length, data = iris)
mod_full <- flashlight(model = fit_full, label = "full", data = iris)
mod_part <- flashlight(model = fit_part, label = "part", data = iris)
mods <- multiflashlight(list(mod_full, mod_part))
plot(light_ice(mod_full, v = "Species"), alpha = 0.2)
indices <- (1:15) * 10
plot(light_ice(mods, v = "Species", indices = indices))
plot(light_ice(mods, v = "Species", indices = indices, center = "first"))
plot(light_ice(mods, v = "Petal.Width", by = "Species", n_bins = 5, indices = indices))

Visualize Variable Importance

Description

Minimal visualization of an object of class "light_importance" via ggplot2::geom_bar(). If available, standard errors are added by ggplot2::geom_errorbar(). The object returned is of class "ggplot" and can be further customized.

Usage

## S3 method for class 'light_importance'
plot(
  x,
  top_m = Inf,
  swap_dim = FALSE,
  facet_scales = "fixed",
  rotate_x = FALSE,
  error_bars = TRUE,
  ...
)

Arguments

Details

The plot is organized as a bar plot with variable names as x-aesthetic. Up to two additional dimensions (multiflashlight and one "by" variable or single flashlight with two "by" variables) can be visualized by facetting and dodge/fill. Set swap_dim = FALSE to revert the role of these two dimensions. One single additional dimension is visualized by a facet wrap, or - if swap_dim = FALSE - by dodge/fill.

Value

An object of class "ggplot".

See Also

light_importance()

Examples

fit_full <- lm(Sepal.Length ~ ., data = iris)
fit_part <- lm(Sepal.Length ~ Petal.Length, data = iris)
mod_full <- flashlight(model = fit_full, label = "full", data = iris, y = "Sepal.Length")
mod_part <- flashlight(model = fit_part, label = "part", data = iris, y = "Sepal.Length")
mods <- multiflashlight(list(mod_full, mod_part), by = "Species")
plot(light_importance(mod_part, m_repetitions = 4), fill = "darkred")
plot(light_importance(mods), swap_dim = TRUE)

Visualize Model Performance

Description

Minimal visualization of an object of class "light_performance" as ggplot2::geom_bar(). The object returned has class "ggplot", and can be further customized.

Usage

## S3 method for class 'light_performance'
plot(
  x,
  swap_dim = FALSE,
  geom = c("bar", "point"),
  facet_scales = "free_y",
  rotate_x = FALSE,
  ...
)

Arguments

Details

The plot is organized as a bar plot as follows: For flashlights without "by" variable specified, a single bar is drawn. Otherwise, the "by" variable (or the flashlight label if there is no "by" variable) is represented by the "x" aesthetic.

The flashlight label (in case of one "by" variable) is represented by dodged bars. This strategy makes sure that performance of different flashlights can be compared easiest. Set "swap_dim = TRUE" to revert the role of dodging and x aesthetic. Different metrics are always represented by facets.

Value

An object of class "ggplot".

See Also

light_performance()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "ols", data = iris, y = "Sepal.Length")
plot(light_performance(fl, by = "Species"), fill = "darkred")

Visualize Profiles, e.g. Partial Dependence

Description

Minimal visualization of an object of class "light_profile". The object returned is of class "ggplot" and can be further customized.

Usage

## S3 method for class 'light_profile'
plot(
  x,
  swap_dim = FALSE,
  facet_scales = "free_x",
  rotate_x = x$type != "partial dependence",
  show_points = TRUE,
  ...
)

Arguments

Details

Either lines and points are plotted (if stats = "mean") or quartile boxes. If there is a "by" variable or a multiflashlight, this first dimension is represented by color (or if swap_dim = TRUE by facets). If there are two "by" variables or a multiflashlight with one "by" variable, the first "by" variable is visualized as color, while the second one or the multiflashlight is shown via facet (change with swap_dim).

Value

An object of class "ggplot".

See Also

light_profile(), plot.light_effects()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
plot(light_profile(fl, v = "Species"))
plot(light_profile(fl, v = "Petal.Width", by = "Species", evaluate_at = 2:4))
plot(light_profile(fl, v = "Petal.Width", type = "predicted"))

Visualize 2D-Profiles, e.g., of Partial Dependence

Description

Minimal visualization of an object of class "light_profile2d". The object returned is of class "ggplot" and can be further customized.

Usage

## S3 method for class 'light_profile2d'
plot(x, swap_dim = FALSE, rotate_x = TRUE, numeric_as_factor = FALSE, ...)

Arguments

Details

The main geometry is ggplot2::geom_tile(). Additional dimensions ("by" variable(s) and/or multiflashlight) are represented by facet_wrap/grid. For all types of profiles except "partial dependence", it is natural to see empty parts in the plot. These are combinations of the v variables that do not appear in the data. Even for type "partial dependence", such gaps can occur, e.g. for cut_type = "quantile" or if n_bins are larger than the number of distinct values of a v variable. Such gaps can be suppressed by setting numeric_as_factor = TRUE or by using the arguments breaks, pd_evaluate_at or pd_grid in light_profile2d().

Value

An object of class "ggplot".

See Also

light_profile2d()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
plot(light_profile2d(fl, v = c("Petal.Length", "Species")))

Scatter Plot

Description

Values are plotted against a variable. The object returned is of class "ggplot" and can be further customized. To avoid overplotting, try alpha = 0.2 or position = "jitter".

Usage

## S3 method for class 'light_scatter'
plot(x, swap_dim = FALSE, facet_scales = "free_x", rotate_x = FALSE, ...)

Arguments

Value

An object of class "ggplot".

See Also

light_scatter()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "ols", data = iris)
plot(light_scatter(fl, v = "Petal.Length", by = "Species"), alpha = 0.2)

DEPRECATED - Add Counts to Effects Plot

Description

Add counts as labelled bar plot on top of light_effects plot.

Usage

plot_counts(
  p,
  x,
  text_size = 3,
  facet_scales = "free_x",
  show_labels = TRUE,
  big.mark = "'",
  scientific = FALSE,
  digits = 0,
  ...
)

Arguments

Details

Experimental. Uses package ggpubr to rearrange the figure. Thus, the resulting plot cannot be easily modified. Furthermore, adding counts only works if the legend in plot.light_effects() is not placed on the left or right side of the plot. It has to be placed inside or at the bottom.

Value

An object of class "ggplot".

See Also

plot.light_effects()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "iris", data = iris, y = "Sepal.Length")
x <- light_effects(fl, v = "Species")
plot_counts(plot(x), x, width = 0.3, alpha = 0.2)

Predictions for flashlight

Description

Predict method for an object of class "flashlight". Pass additional elements to update the flashlight, typically data.

Usage

## S3 method for class 'flashlight'
predict(object, ...)

Arguments

Value

A vector with predictions.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, data = iris, y = "Sepal.Length", label = "ols")
predict(fl)[1:5]
predict(fl, data = iris[1:5, ])

Predictions for multiflashlight

Description

Predict method for an object of class "multiflashlight". Pass additional elements to update the flashlight, typically data.

Usage

## S3 method for class 'multiflashlight'
predict(object, ...)

Arguments

Value

A named list of prediction vectors.

Examples

fit_part <- lm(Sepal.Length ~ Petal.Length, data = iris)
fit_full <- lm(Sepal.Length ~ ., data = iris)
mod_full <- flashlight(model = fit_full, label = "full")
mod_part <- flashlight(model = fit_part, label = "part")
mods <- multiflashlight(list(mod_full, mod_part), data = iris, y = "Sepal.Length")
predict(mods, data = iris[1:5, ])

Prints a flashlight

Description

Print method for an object of class "flashlight".

Usage

## S3 method for class 'flashlight'
print(x, ...)

Arguments

Value

Invisibly, the input is returned.

See Also

flashlight()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
x <- flashlight(model = fit, label = "lm", y = "Sepal.Length", data = iris)
x

Prints light Object

Description

Print method for an object of class "light".

Usage

## S3 method for class 'light'
print(x, ...)

Arguments

Value

Invisibly, the input is returned.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
fl <- flashlight(model = fit, label = "lm", y = "Sepal.Length", data = iris)
light_performance(fl, v = "Species")

Prints a multiflashlight

Description

Print method for an object of class "multiflashlight".

Usage

## S3 method for class 'multiflashlight'
print(x, ...)

Arguments

Value

Invisibly, the input is returned.

See Also

multiflashlight()

Examples

fit_lm <- lm(Sepal.Length ~ ., data = iris)
fit_glm <- glm(Sepal.Length ~ ., family = Gamma(link = log), data = iris)
fl_lm <- flashlight(model = fit_lm, label = "lm")
fl_glm <- flashlight(model = fit_glm, label = "glm")
multiflashlight(list(fl_lm, fl_glm), data = iris)

Residuals for flashlight

Description

Residuals method for an object of class "flashlight". Pass additional elements to update the flashlight before calculation of residuals.

Usage

## S3 method for class 'flashlight'
residuals(object, ...)

Arguments

Value

A numeric vector with residuals.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
x <- flashlight(model = fit, data = iris, y = "Sepal.Length", label = "ols")
residuals(x)[1:5]

Residuals for multiflashlight

Description

Residuals method for an object of class "multiflashlight". Pass additional elements to update the multiflashlight before calculation of residuals.

Usage

## S3 method for class 'multiflashlight'
residuals(object, ...)

Arguments

Value

A named list with residuals per flashlight.

Examples

fit_part <- lm(Sepal.Length ~ Petal.Length, data = iris)
fit_full <- lm(Sepal.Length ~ ., data = iris)
mod_full <- flashlight(model = fit_full, label = "full")
mod_part <- flashlight(model = fit_part, label = "part")
mods <- multiflashlight(list(mod_full, mod_part), data = iris, y = "Sepal.Length")
residuals(mods, data = head(iris))

Response of multi/-flashlight

Description

Extracts response from object of class "flashlight".

Usage

response(object, ...)

## Default S3 method:
response(object, ...)

## S3 method for class 'flashlight'
response(object, ...)

## S3 method for class 'multiflashlight'
response(object, ...)

Arguments

Value

A numeric vector of responses.

Methods (by class)

• response(default): Default method not implemented yet.

• response(flashlight): Extract response from flashlight object.

• response(multiflashlight): Extract responses from multiflashlight object.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
(fl <- flashlight(model = fit, data = iris, y = "Sepal.Length", label = "ols"))
response(fl)[1:5]
response(fl, data = iris[1:5, ])
response(fl, data = iris[1:5, ], linkinv = exp)