Package 'glex' reference manual

Title:	Global Explanations for Tree-Based Models
Description:	Global explanations for tree-based models by decomposing regression or classification functions into the sum of main components and interaction components of arbitrary order. Calculates SHAP values and q-interaction SHAP for all values of q for tree-based models such as xgboost.
Authors:	Marvin N. Wright [aut, cre] (ORCID: <https://orcid.org/0000-0002-8542-6291>), Joseph Theo Meyer [aut], Munir Hiabu [aut], Lukas Burk [aut] (ORCID: <https://orcid.org/0000-0001-7528-3795>), Jinyang Liu [aut] (ORCID: <https://orcid.org/0009-0005-3167-9014>)
Maintainer:	Marvin N. Wright <[email protected]>
License:	GPL-3
Version:	0.7.0.9000
Built:	2026-10-09 02:31:09 UTC
Source:	https://github.com/PlantedML/glex

Plot Prediction Components

Description

Plotting the main effects among the prediction components is effectively identical to a partial dependence plot, centered to 0.

Usage

## S3 method for class 'glex'
autoplot(object, predictors, ...)

plot_main_effect(object, predictor, rug_sides = "b", ...)

plot_threeway_effects(object, predictors, rug_sides = "b", ...)

plot_twoway_effects(object, predictors, rug_sides = "b", ...)
## S3 method for class 'glex'
autoplot(object, predictors, ...)

plot_main_effect(object, predictor, rug_sides = "b", ...)

plot_threeway_effects(object, predictors, rug_sides = "b", ...)

plot_twoway_effects(object, predictors, rug_sides = "b", ...)

Arguments

object

Object of class glex.

...

Used for future expansion.

predictor, predictors

(character) vector of predictor names, e.g. "x1" to plot main effect of x1, and c("x1", "x2") to plot the interaction term x1:x2.

rug_sides

(character(1): "b") Sides to plot rug (see ggplot2::geom_rug()) plot on for continuous predictors.. Default is "b" for both sides. Set to "none" to disable rug plot.

Value

A ggplot2 object.

Examples


library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

# Main effects ----
plot_main_effect(components, "wt")
plot_main_effect(components, "cyl")

# plot_threeway_effects(components, c("hr", "temp", "workingday"))

library(randomPlantedForest)

# 2-degree interaction effects ----
# 2d continuous, scatterplot of arbitrary orientation
plot_twoway_effects(components, c("wt", "drat"))
# flipped: plot_twoway_effects(components, c("drat", "wt"))

# continuous + categorical (forces continuous on x axis, colors by categorical)
plot_twoway_effects(components, c("wt", "cyl"))
# identical: plot_twoway_effects(components, c("cyl", "wt"))

# 2d categorical, heatmap of arbitrary orientation
plot_twoway_effects(components, c("vs", "cyl"))
plot_twoway_effects(components, c("cyl", "vs"))

library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

# Main effects ----
plot_main_effect(components, "wt")
plot_main_effect(components, "cyl")

# plot_threeway_effects(components, c("hr", "temp", "workingday"))

library(randomPlantedForest)

# 2-degree interaction effects ----
# 2d continuous, scatterplot of arbitrary orientation
plot_twoway_effects(components, c("wt", "drat"))
# flipped: plot_twoway_effects(components, c("drat", "wt"))

# continuous + categorical (forces continuous on x axis, colors by categorical)
plot_twoway_effects(components, c("wt", "cyl"))
# identical: plot_twoway_effects(components, c("cyl", "wt"))

# 2d categorical, heatmap of arbitrary orientation
plot_twoway_effects(components, c("vs", "cyl"))
plot_twoway_effects(components, c("cyl", "vs"))

Plot glex Variable Importances

Description

Plot glex Variable Importances

Usage

## S3 method for class 'glex_vi'
autoplot(
  object,
  by_degree = FALSE,
  threshold = 0,
  max_interaction = NULL,
  scale = "absolute",
  ...
)
## S3 method for class 'glex_vi'
autoplot(
  object,
  by_degree = FALSE,
  threshold = 0,
  max_interaction = NULL,
  scale = "absolute",
  ...
)

Arguments

object

Object of class glex_vi, see glex_vi().

by_degree

(logical(1): FALSE) Optionally sum values by degree of interaction, resulting in one contribution score for all main effects, all second-order interactions, etc.

threshold

(numeric(1): 0) Optional threshold to filter output to include only importance scores greater than this value. Refers to the chosen scale.

max_interaction

(integer(1): NULL) Optionally filter plot to show terms up to the specified degree of interaction. Similar to threshold, all other terms will be aggregated under a "Remaining terms" label.

scale

("absolute") Plot average absolute contributions (default) or the same value but scaled by the average prediction ("relative").

...

(Unused)

Value

A ggplot object.

Bikesharing data

Description

A reduced version of the Bikeshare data as included with ISLR2. The dataset has been converted to a data.table::data.table(), with the following changes:

Usage

bike
bike

Format

An object of class data.table (inherits from data.frame) with 8645 rows and 11 columns.

Details

hr has been converted to a numeric
workingday was recoded to a binary factor with labels c("No Workingday", "Workingday")
season was recoded to a factor with labels c("Winter", "Spring", "Summer", "Fall")
Variables atemp, day, registered and casual were removed

Source

Bikeshare in package ISLR2

Derive feature groups from the factors behind a dummy encoding

Description

Builds the groups list for group_components() from the original, un-encoded data: every factor column of data whose encoded level columns are found in object$x becomes one group. By default, level columns are expected under the model.matrix() naming convention (paste0(feature, levels), e.g. season with level "Winter" becomes "seasonWinter"), which covers both one-hot (~ f - 1) and treatment (~ f) coding – levels without a matching column (such as a dropped reference level) are simply skipped.

Usage

dummy_groups(
  object,
  data,
  naming = function(feature, levels) paste0(feature, levels)
)
dummy_groups(
  object,
  data,
  naming = function(feature, levels) paste0(feature, levels)
)

Arguments

object

(glex) Object of class glex.

data

(data.frame) The data before dummy encoding; its factor and character columns define the candidate groups. Other columns are ignored.

naming

(⁠function(feature, levels)⁠) Maps a factor name and its levels to the encoded column names. Defaults to the model.matrix() convention paste0(feature, levels).

Details

If the data was encoded with a different scheme, pass naming: a function of ⁠(feature, levels)⁠ returning the encoded column names, e.g. function(feature, levels) paste(feature, levels, sep = "_"). It must reproduce the column names the model was trained with, i.e. those found in object$x.

Value

Named list of encoded column names, one element per matched factor, suitable as the groups argument of group_components().

Examples


library(xgboost)
set.seed(1)
n <- 200
d <- data.frame(
  f = factor(sample(c("a", "b", "c"), n, replace = TRUE)),
  x1 = rnorm(n)
)
y <- c(a = 0, b = 2, c = -1)[d$f] + d$x1 + rnorm(n)
x <- model.matrix(~ f + x1 - 1, d)

xg <- xgboost(x, y, nrounds = 10, max_depth = 3, nthreads = 1)
gl <- glex(xg, x)

dummy_groups(gl, d)
grouped <- group_components(gl, dummy_groups(gl, d))
names(grouped$m)

library(xgboost)
set.seed(1)
n <- 200
d <- data.frame(
  f = factor(sample(c("a", "b", "c"), n, replace = TRUE)),
  x1 = rnorm(n)
)
y <- c(a = 0, b = 2, c = -1)[d$f] + d$x1 + rnorm(n)
x <- model.matrix(~ f + x1 - 1, d)

xg <- xgboost(x, y, nrounds = 10, max_depth = 3, nthreads = 1)
gl <- glex(xg, x)

dummy_groups(gl, d)
grouped <- group_components(gl, dummy_groups(gl, d))
names(grouped$m)

Global explanations for tree-based models.

Description

Global explanations for tree-based models by decomposing regression or classification functions into the sum of main components and interaction components of arbitrary order. Calculates SHAP values and q-interaction SHAP for all values of q for tree-based models such as xgboost.

Usage

glex(object, x, max_interaction = NULL, features = NULL, ...)

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

## S3 method for class 'rpf'
glex(object, x, max_interaction = NULL, features = NULL, ...)

## S3 method for class 'xgb.Booster'
glex(
  object,
  x,
  max_interaction = NULL,
  features = NULL,
  max_background_sample_size = NULL,
  weighting_method = "fastpd",
  ...
)

## S3 method for class 'ranger'
glex(
  object,
  x,
  max_interaction = NULL,
  features = NULL,
  max_background_sample_size = NULL,
  weighting_method = "fastpd",
  ...
)
glex(object, x, max_interaction = NULL, features = NULL, ...)

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

## S3 method for class 'rpf'
glex(object, x, max_interaction = NULL, features = NULL, ...)

## S3 method for class 'xgb.Booster'
glex(
  object,
  x,
  max_interaction = NULL,
  features = NULL,
  max_background_sample_size = NULL,
  weighting_method = "fastpd",
  ...
)

## S3 method for class 'ranger'
glex(
  object,
  x,
  max_interaction = NULL,
  features = NULL,
  max_background_sample_size = NULL,
  weighting_method = "fastpd",
  ...
)

Arguments

object

Model to be explained, either of class xgb.Booster or rpf.

x

Data to be explained. For xgboost models with categorical features (fit on a data.frame with factor columns), a data.frame whose factor columns have the same levels in the same order as the training data: the model stores category codes, not levels, so a mismatch cannot be detected beyond a missing level (this is the same contract as predict()). For ranger models, a data.frame with the same factor columns as the training data, encoded as predict() does under any respect.unordered.factors option. Otherwise a numeric matrix or data.frame.

max_interaction

(integer(1): NULL)
Maximum interaction size to consider. Defaults to using all possible interactions available in the model.
For xgboost, this defaults to the max_depth parameter of the model fit.
If not set in xgboost, the default value of 6 is assumed.

features

Vector of column names in x to calculate components for. Default is NULL, i.e. all features are used.

...

Further arguments passed to methods. Arguments a method does not use are disregarded with a warning.

max_background_sample_size

The maximum number of background samples used for the FastPD algorithm, only used when weighting_method = "fastpd". Defaults to nrow(x).

weighting_method

Use either "path-dependent", "fastpd" (default), or "empirical". See References for details.

Details

For parallel execution using xgboost models, register a backend, e.g. with doParallel::registerDoParallel().

The different weighting methods are described in detail in Liu et al. (2025). The default method is "fastpd" as it consistently estimates the correct partial dependence function.

Value

Decomposition of the regression or classification function. A list with elements:

shap: SHAP values, derived from the functional decomposition as $\phi_j = \sum_{S \ni j} m_S / |S|$ . This reconstruction is only valid if the decomposition is complete: if it is constrained (see constrained), the components no longer sum to the full model prediction and the SHAP efficiency property cannot hold, so shap is a scalar NA (with a warning) while m remains valid. For multiclass models, columns are class-specific like those of m. Note that randomPlantedForest models report a single intercept for all classes, so for multiclass models intercept + rowSums(shap) reconstructs the predicted class scores only approximately.
m: Functional decomposition into all main and interaction components in the model, up to the degree specified by max_interaction. The variable names correspond to the original variable names, with : separating interaction terms as one would specify in a formula interface.
intercept: Intercept term, the expected value of the prediction.
constrained: Character vector naming the arguments that constrained the decomposition ("max_interaction", "features"), or character(0) if it is complete. Use length(x$constrained) > 0 to check whether shap is valid. A constraint that only drops terms whose value is zero leaves the decomposition unchanged; glex() confirms this against the model's predictions, reports it with a message, and treats the result as complete.
remainder: What the dropped terms are collectively worth, per observation: prediction - (intercept + rowSums(m)). Present exactly when the decomposition is constrained, and absent otherwise, so intercept + rowSums(m) + remainder reconstructs the prediction in either case. For multiclass randomPlantedForest models it is class-wise, mirroring m.

Like m and shap, it is on the scale that the model is decomposed on, which for xgboost is the link scale and not the response: for a binary:logistic model the reconstruction gives the margin, and plogis(intercept + rowSums(m) + remainder) gives the predicted probability. ranger probability forests and randomPlantedForest are decomposed on the response scale, where no such back-transformation is needed. Adding remainder to a probability is therefore never correct for xgboost.

References

Liu, J., Steensgaard, T., Wright, M. N., Pfister, N., & Hiabu, M. (2025). Fast Estimation of Partial Dependence Functions using Trees. Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:39496-39534. PMLR | arXiv:2410.13448

Examples



# Random Planted Forest -----
library(randomPlantedForest)

rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)

glex_rpf <- glex(rp, mtcars[27:32, ])
str(glex_rpf, list.len = 5)


# xgboost -----
library(xgboost)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
xg <- xgboost(x[1:26, ], y[1:26],
  max_depth = 4, learning_rate = .1,
  nrounds = 10, verbosity = 0, nthreads = 1
)
glex(xg, x[27:32, ])
glex(xg, mtcars[27:32, ])

## Not run: 
# Parallel execution
doParallel::registerDoParallel()
glex(xg, x[27:32, ])

## End(Not run)


# ranger -----
library(ranger)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
rf <- ranger(
  x = x[1:26, ], y = y[1:26],
  num.trees = 5, max.depth = 3,
  node.stats = TRUE
)
glex(rf, x[27:32, ])
glex(rf, mtcars[27:32, ])

## Not run: 
# Parallel execution
doParallel::registerDoParallel()
glex(rf, x[27:32, ])

## End(Not run)

# Random Planted Forest -----
library(randomPlantedForest)

rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)

glex_rpf <- glex(rp, mtcars[27:32, ])
str(glex_rpf, list.len = 5)


# xgboost -----
library(xgboost)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
xg <- xgboost(x[1:26, ], y[1:26],
  max_depth = 4, learning_rate = .1,
  nrounds = 10, verbosity = 0, nthreads = 1
)
glex(xg, x[27:32, ])
glex(xg, mtcars[27:32, ])

## Not run: 
# Parallel execution
doParallel::registerDoParallel()
glex(xg, x[27:32, ])

## End(Not run)


# ranger -----
library(ranger)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
rf <- ranger(
  x = x[1:26, ], y = y[1:26],
  num.trees = 5, max.depth = 3,
  node.stats = TRUE
)
glex(rf, x[27:32, ])
glex(rf, mtcars[27:32, ])

## Not run: 
# Parallel execution
doParallel::registerDoParallel()
glex(rf, x[27:32, ])

## End(Not run)

Explain a single prediction

Description

Plots the prediction components for a single observation, identified by the row number in the dataset used with glex(). Since the resulting plot can be quite busy due to potentially large amounts of elements, it is highly recommended to use predictors, max_interaction, or threshold to restrict the number of elements in the plot.

Usage

glex_explain(
  object,
  id,
  threshold = 0,
  max_interaction = NULL,
  predictors = NULL,
  class = NULL,
  barheight = 0.5
)
glex_explain(
  object,
  id,
  threshold = 0,
  max_interaction = NULL,
  predictors = NULL,
  class = NULL,
  barheight = 0.5
)

Arguments

object

Object of class glex containing prediction components and data to be explained.

id

(integer(1)) Row ID of the observation to be explained in object$x.

threshold

(numeric(1): 0) Threshold to filter output by in case of many negligible effects.

max_interaction

(integer(1): NULL) Optionally filter plot to show terms up to the specified degree of interaction. Similar to threshold, all other terms will be aggregated under a "Remaining terms" label.

predictors

(character: NULL) Vector of column names in ⁠$x⁠ to restrict plot to.

class

(character: NULL) For multiclass targets, specifies the target class to limit output.

barheight

(numeric(1): 0.5) Relative height of horizontal bars. Preferred value may depend on the number of vertical elements, hence it may be necessary to adjust this value as needed.

Value

A ggplot object.

Examples


set.seed(1)
# Random Planted Forest -----
library(randomPlantedForest)

rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)

glex_rpf <- glex(rp, mtcars[27:32, ])

glex_explain(glex_rpf, id = 3, predictors = "hp", threshold = 0.01)

set.seed(1)
# Random Planted Forest -----
library(randomPlantedForest)

rp <- rpf(mpg ~ ., data = mtcars[1:26, ], max_interaction = 2)

glex_rpf <- glex(rp, mtcars[27:32, ])

glex_explain(glex_rpf, id = 3, predictors = "hp", threshold = 0.01)

Package options for glex plots

Description

The color choices used across all glex visualization functions can be adjusted globally via options():

Details

glex.palette (NULL): Diverging palette used to color continuous interaction effects in plot_twoway_effects() and plot_threeway_effects(). The default NULL uses a blue/red gradient built from glex.colors_sign, matching the look of the Python shap and shapiq packages. Set to the name of a diverging scico palette (e.g. "vik", "roma") to use that instead.
glex.palette_discrete ("Dark2"): Discrete palette used to color categorical predictors in interaction plots. Accepts a vector of colors (used via ggplot2::scale_color_manual()), the string "okabe-ito" (the colorblind-safe Okabe-Ito palette via grDevices::palette.colors()), the name of a scico palette, or the name of an RColorBrewer palette.
glex.colors_sign (c("#008BFB", "#FF0051")): Two colors for negative and positive contributions in glex_explain(), also used as the endpoints of the default continuous gradient. The defaults follow the blue/red convention familiar from the Python shap and shapiq packages.
glex.color_line ("#194155"): Color for main effect lines and columns drawn by autoplot() and plot_pdp().

Examples

# Use a scico palette for continuous effects instead of the default gradient
options(glex.palette = "roma")

# Restore the default shap-style gradient
options(glex.palette = NULL)

# Categorical predictors: Okabe-Ito, a scico/brewer palette, or custom colors
options(glex.palette_discrete = "okabe-ito")
options(glex.palette_discrete = "batlow")
options(glex.palette_discrete = c("#E69F00", "#56B4E9", "#009E73"))

# Use a scico palette for continuous effects instead of the default gradient
options(glex.palette = "roma")

# Restore the default shap-style gradient
options(glex.palette = NULL)

# Categorical predictors: Okabe-Ito, a scico/brewer palette, or custom colors
options(glex.palette_discrete = "okabe-ito")
options(glex.palette_discrete = "batlow")
options(glex.palette_discrete = c("#E69F00", "#56B4E9", "#009E73"))

Variable Importance for Main and Interaction Terms

Description

Variable Importance for Main and Interaction Terms

Usage

glex_vi(object, ...)
glex_vi(object, ...)

Arguments

object

Object of class glex.

...

(Unused)

Details

The m reported here is the average absolute value of m as reported by glex(), aggregated by term:

$\mathtt{m} = \frac{1}{n} \sum_{i = 1}^n |m|$

In turn, m_rel rescales m by the average prediction of the model ( $m_0$ , intercept as reported by glex()):

$\mathtt{m\_rel} = \frac{\mathtt{m}}{m_0}$

Value

A data.table::data.table() with columns:

degree (integer): Degree of interaction of the term, with 1 being main effects, 2 being 2-degree interactions etc.
term (character): Model term, e.g. main effect x1 or interaction term x1:x2, x1:x3:x5 etc.
class (factor): For multiclass targets only: The associated target class. Lists all classes in the target, not limited to the majority vote.
m (numeric): Average absolute contribution of term, see Details.
m_rel (numeric): m but relative to the average prediction (intercept in glex() output).

Examples


set.seed(1)

# xgboost -----
library(xgboost)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
xg <- xgboost(x[1:26, ], y[1:26],
  max_depth = 4, learning_rate = .1,
  nrounds = 10, verbosity = 0, nthreads = 1
)
glex_xgb <- glex(xg, x[27:32, ])
vi_xgb <- glex_vi(glex_xgb)

library(ggplot2)
autoplot(vi_xgb)
autoplot(vi_xgb, by_degree = TRUE)

set.seed(1)

# xgboost -----
library(xgboost)
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
xg <- xgboost(x[1:26, ], y[1:26],
  max_depth = 4, learning_rate = .1,
  nrounds = 10, verbosity = 0, nthreads = 1
)
glex_xgb <- glex(xg, x[27:32, ])
vi_xgb <- glex_vi(glex_xgb)

library(ggplot2)
autoplot(vi_xgb)
autoplot(vi_xgb, by_degree = TRUE)

Group features of a decomposition

Description

Aggregates the terms of a decomposition such that a set of features is treated as a single feature, named after the group. All terms involving only members of a group are summed into the group's main effect, and terms mixing group members with other features become interactions of the group. Since the decomposition is additive, this regrouping is exact: components still sum to the model prediction, and SHAP values of a group are the sums of its members' SHAP values, preserving the efficiency property.

Usage

group_components(object, groups)
group_components(object, groups)

Arguments

object

(glex) Object of class glex.

groups

(list) Named list of character vectors of feature names in object$x. Each vector becomes a single feature named after the list element, e.g. list(f = c("fa", "fb", "fc")). Features may appear in at most one group; features not listed remain unchanged.

Details

The motivating use case is dummy-encoded categorical features in xgboost: the decomposition of a one-hot encoded factor spreads its effect across the dummies and their interactions (e.g. fa:fb), which are artifacts of the encoding rather than meaningful interactions. Grouping the dummies restores the factor as one feature, e.g. groups = list(f = c("fa", "fb", "fc")) turns fa, fb, fc, fa:fb, ... into f, and fa:x1, fa:fb:x1, ... into f:x1.

For the grouped columns of x, a factor is reconstructed when the group's columns form a dummy encoding (0/1 values, at most one active per row), with the active column's name as the level and "(base)" for rows with no active column (treatment coding). Otherwise the group's column in x is NA and plot functions cannot be used for that group's main effect.

Value

Object of class glex with grouped m, shap and x.

Examples


library(xgboost)
set.seed(1)
n <- 200
f <- factor(sample(c("a", "b", "c"), n, replace = TRUE))
x1 <- rnorm(n)
y <- c(a = 0, b = 2, c = -1)[f] + x1 + c(a = 1, b = 0, c = -1)[f] * x1 + rnorm(n)
x <- cbind(model.matrix(~ f - 1), x1)

xg <- xgboost(x, y, nrounds = 10, max_depth = 3, nthreads = 1)
gl <- glex(xg, x)
names(gl$m)

grouped <- group_components(gl, groups = list(f = c("fa", "fb", "fc")))
names(grouped$m)
all.equal(rowSums(grouped$m), rowSums(gl$m))

library(xgboost)
set.seed(1)
n <- 200
f <- factor(sample(c("a", "b", "c"), n, replace = TRUE))
x1 <- rnorm(n)
y <- c(a = 0, b = 2, c = -1)[f] + x1 + c(a = 1, b = 0, c = -1)[f] * x1 + rnorm(n)
x <- cbind(model.matrix(~ f - 1), x1)

xg <- xgboost(x, y, nrounds = 10, max_depth = 3, nthreads = 1)
gl <- glex(xg, x)
names(gl$m)

grouped <- group_components(gl, groups = list(f = c("fa", "fb", "fc")))
names(grouped$m)
all.equal(rowSums(grouped$m), rowSums(gl$m))

Partial Dependence Plot

Description

A version of plot_main_effect with the intercept term (horizontal line) added, resulting in a partial dependence plot.

Usage

plot_pdp(object, predictor, rug_sides = "b", ...)
plot_pdp(object, predictor, rug_sides = "b", ...)

Arguments

object

Object of class glex.

predictor

(character(1)) predictor names, e.g. "x1" to plot main effect of x1.

rug_sides

(character(1): "b") Sides to plot rug (see ggplot2::geom_rug()) plot on for continuous predictors.. Default is "b" for both sides. Set to "none" to disable rug plot.

...

Used for future expansion.

Value

A ggplot2 object.

Examples


library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

plot_pdp(components, "wt")
plot_pdp(components, "cyl")

library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

plot_pdp(components, "wt")
plot_pdp(components, "cyl")

Print glex objects

Description

This is implemented mainly to avoid flooding the console in cases where the glex object uses many terms, which leads to a large amount of column names of ⁠$m⁠ being printed to the console. This function wraps str() with a truncated output for a more compact representation.

Usage

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

Arguments

x

Object to print.

...

(Unused)

Examples


# Random Planted Forest -----
library(randomPlantedForest)
rp <- rpf(mpg ~ hp + wt + drat, data = mtcars[1:26, ], max_interaction = 2)

glex(rp, mtcars[27:32, ])

# Random Planted Forest -----
library(randomPlantedForest)
rp <- rpf(mpg ~ hp + wt + drat, data = mtcars[1:26, ], max_interaction = 2)

glex(rp, mtcars[27:32, ])

Subset components

Description

Subset components

Usage

subset_components(components, term)

subset_component_names(components, term)
subset_components(components, term)

subset_component_names(components, term)

Arguments

components

An object of class glex.

term

(character(1)) A main term name to subset by, e.g. "x1".

Value

subset_components(): An object of class glex.
subset_component_names(): A character vector.

Examples


library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

# Get component object with only "hp" and its interactions
subset_components(components, "hp")

subset_component_names(components, "hp")

library(randomPlantedForest)

# introduce factor variables to show categorical feature handling
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)

# Fit forest, get components
set.seed(12)
rpfit <- rpf(mpg ~ cyl + wt + hp + drat + vs, data = mtcars, ntrees = 25, max_interaction = 3)
components <- glex(rpfit, mtcars)

# Get component object with only "hp" and its interactions
subset_components(components, "hp")

subset_component_names(components, "hp")

A ggplot2 theme for glex plots

Description

This is a slight variation of ggplot2::theme_minimal() with increased font size.

Usage

theme_glex(
  base_size = 13,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22,
  grid_x = TRUE,
  grid_y = FALSE
)
theme_glex(
  base_size = 13,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22,
  grid_x = TRUE,
  grid_y = FALSE
)

Arguments

base_size

(13) Base font size, given in pts.

base_family

("") Base font family

base_line_size, base_rect_size

(base_size / 22) Base size for line and rect elements

grid_x

(TRUE) Display horizontal grid lines?

grid_y

(FALSE) Display vertical grid lines?

Value

A ggplot2 theme object

Examples

library(ggplot2)

ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  theme_glex()
library(ggplot2)

ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  theme_glex()

Package 'glex'

Help Index

Plot Prediction Components

Description

Usage

Arguments

Value

See Also

Examples

Plot glex Variable Importances

Description

Usage

Arguments

Value

See Also

Bikesharing data

Description

Usage

Format

Details

Source

Derive feature groups from the factors behind a dummy encoding

Description

Usage

Arguments

Details

Value

Examples

Global explanations for tree-based models.

Description

Usage

Arguments

Details

Value

References

Examples

Explain a single prediction

Description

Usage

Arguments

Value

See Also

Examples

Package options for glex plots

Description

Details

Examples

Variable Importance for Main and Interaction Terms

Description

Usage

Arguments

Details

Value

See Also

Examples

Group features of a decomposition

Description

Usage

Arguments

Details

Value

Examples

Partial Dependence Plot

Description

Usage

Arguments

Value

See Also

Examples

Print glex objects

Description

Usage

Arguments

Examples

Subset components

Description

Usage

Arguments

Value

Examples