Combine multiple effect plots for simultaneous display

Runs plotEffects() over several predictors and arranges the results as a labelled panel grid. Each panel keeps its own rug, so the panels stay individually readable rather than becoming a wall of curves.

Usage

combinePlots(
  model,
  dat,
  vars,
  title = "",
  var.transform = c("none", "log", "log10", "sqrt"),
  scale = c("auto", "link", "response"),
  interval = c("auto", "se", "ci", "cri"),
  level = 0.95,
  n = 100,
  rug.type = c("histogram", "density"),
  bins = 30,
  labels = "A",
  label.size = 14,
  title.size = 14,
  common.legend = TRUE,
  ...
)

Arguments

model

A fitted model. GAMs from mgcv are shown as partial effects; other model classes are shown as predictions. See plotEffects().

dat

Raw data the model was fitted on.

vars

Variables of interest, as a character vector.

title

Plot title, optional.

var.transform

How to transform the variables before plotting. One value used for every variable, or one per entry in vars.

scale

"auto", "link", or "response", passed to plotEffects().

interval

"se" or "ci", passed to plotEffects().

level

Confidence level used when interval = "ci".

n

Number of points at which to evaluate each effect.

rug.type

Type of rug plot to draw above each effect.

bins

Number of bins for a histogram rug.

labels

Panel labels: "A" (the default) for upper-case letters, "a" for lower-case, "1" for numbers, "none" for none, or a character vector used verbatim, one per panel.

label.size

Font size of the panel labels. These are drawn by the arranging step rather than by the theme, so they do not follow base_size and have to be set here.

title.size

Font size of the overall figure title, for the same reason.

common.legend

Whether the panels share one legend.

...

Passed through to plotEffects() and on to the backend.

Value

The arranged effect plots.

See also

plotEffects() for a single predictor and for what the y axis means under each model type.

Other effect plots: comparePlots(), plotEffects(), plotRugs(), plotSmooths()

Examples

gam.fit <- mgcv::gam(Petal.Length ~ s(Sepal.Length) + s(Petal.Width),
                     data = iris)
combinePlots(gam.fit, iris, vars = c("Sepal.Length", "Petal.Width"),
             title = "Partial effects on petal length")


# Non-GAM models work the same way
lm.fit <- lm(mpg ~ wt + hp, data = mtcars)
combinePlots(lm.fit, mtcars, vars = c("wt", "hp"), rug.type = "density")