Plots "EffectData" Object

Versatile plot function for an "EffectData" object. By default, all calculated statistics (except "resid_mean") are shown. To select certain statistics, use the stats argument. Set plotly = TRUE for interactive plots. Note that all statistics are plotted at bin means, except for ALE (shown at right bin breaks).

# S3 method for class 'EffectData'
plot(
  x,
  stats = NULL,
  ncol = grDevices::n2mfrow(length(x))[2L],
  byrow = TRUE,
  share_y = c("no", "all", "rows", "cols"),
  ylim = NULL,
  discrete_lines = TRUE,
  continuous_points = FALSE,
  title = "",
  subplot_titles = TRUE,
  ylab = NULL,
  legend_labels = NULL,
  interval = c("no", "ci", "ciw", "sd"),
  ci_level = 0.95,
  colors = getOption("effectplots.colors"),
  fill = getOption("effectplots.fill"),
  alpha = 1,
  bar_height = 1,
  bar_width = 1,
  bar_measure = c("weight", "N"),
  wrap_x = 10,
  rotate_x = 0,
  plotly = getOption("effectplots.plotly"),
  ...
)

Arguments

x

An object of class "EffectData".

stats

Vector of statistics to show. The default NULL equals either c("y_mean", "pred_mean", "pd", "ale"), or "resid_mean" (when x results from bias()). Only available statistics are shown. Additionally, this argument controls the order used to plot the lines.

ncol

Number of columns of the plot layout, by default grDevices::n2mfrow(length(x))[2L]. Only relevant for multiple plots.

byrow

Should plots be placed by row? Default is TRUE. Only for multiple plots.

share_y

Should y axis be shared across subplots? The default is "no". Other choices are "all", "rows", and "cols". Note that this currently does not take into account error bars/ribbons. Has no effect if ylim is passed. Only for multiple plots.

ylim

A vector of length 2 with manual y axis limits, or a list thereof.

discrete_lines

Show lines for discrete features. Default is TRUE.

continuous_points

Show points for continuous features. Default is FALSE.

title

Overall plot title, by default "" (no title).

subplot_titles

Should variable names be shown as subplot titles? Default is TRUE. Only for multiple plots.

ylab

Label of the y axis. The default NULL automatically derives a reasonable name.

legend_labels

Vector of legend labels in the same order as the statistics plotted, or NULL (default).

interval

What intervals should be shown for observed y and residuals? One of

• "no" (default),

• "ci": Z confidence intervals using sqrt(N) as standard error of the mean,

• "ciw": Like "ci", but using sqrt(weight) as standard error of the mean, or

• "sd": standard deviations. Ribbons for continuous features, and error bars otherwise.

ci_level

The nominal level of the Z confidence intervals (only when error equals "ci" or "ciw"). The default is 0.95.

colors

Vector of line/point colors of sufficient length. By default, a color blind friendly palette from "ggthemes". To change globally, set options(effectplots.colors = new colors).

fill

Fill color of bars. The default equals "lightgrey". To change globally, set options(effectplots.fill = new color).

alpha

Alpha transparency of lines and points. Default is 1.

bar_height

Relative bar height (default 1). Set to 0 for no bars.

bar_width

Bar width multiplier (for discrete features). By default 1.

bar_measure

What should bars represent? Either "weight" (default) or "N".

wrap_x

Should categorical x axis labels be wrapped after this length? The default is 10. Set to 0 for no wrapping. Vectorized over x. Only for "ggplot2" backend.

rotate_x

Should categorical xaxis labels be rotated by this angle? The default is 0 (no rotation). Vectorized over x. Only for "ggplot2" backend.

plotly

Should 'plotly' be used? The default is FALSE ('ggplot2' with 'patchwork'). Use options(effectplots.plotly = TRUE) to change globally.

...

Passed to patchwork::plot_layout() or plotly::subplot(). Typically not used.

Value

If a single plot, an object of class "ggplot" or "plotly". Otherwise, an object of class "patchwork", or a "plotly" subplot.

See also

feature_effects(), average_observed(), average_predicted(), partial_dependence(), bias(), ale()

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
xvars <- colnames(iris)[-1]
M <- feature_effects(fit, v = xvars, data = iris, y = "Sepal.Length", breaks = 5)
plot(M, share_y = "all")

plot(M, stats = c("pd", "ale"), legend_labels = c("PD", "ALE"))

plot(M, stats = "resid_mean", share_y = "all", interval = "ci")