Skip to contents

Compare MID Component Functions

Source: R/plot_mids.R
plot.mids.Rd

For "mids" collection objects, plot() visualizes and compares a single main effect across multiple models.

Usage

# S3 method for class 'mids'
plot(
  x,
  term,
  type = c("effect", "series"),
  theme = NULL,
  intercept = FALSE,
  limits = NULL,
  resolution = NULL,
  labels = base::labels(x),
  ...
)

Arguments

x

a "mids" collection object to be visualized.

term

a character string specifying the main effect to evaluate.

type

the plotting style: "effect" plots the effect curve per model, while "series" plots the effect trend over models per feature value.

theme

a character string or object defining the color theme. See color.theme for details.

intercept

logical. If TRUE, the model intercept is added to the component effect.

limits

a numeric vector of length two specifying the limits of the plotting scale.

resolution

an integer specifying the number of evaluation points for continuous variables.

labels

an optional numeric or character vector to specify the model labels. Defaults to labels(object). The function attempts to parse these labels into numeric values where possible.

...

optional parameters passed to the main layer (e.g., linewidth, alpha).

Value

plot.mids() produces a plot as a side-effect and returns NULL invisibly.

Details

This is an S3 method for the plot() generic that evaluates the specified term over a grid of values and compares the results across all models in the collection.

The type argument controls the visualization style. The default, type = "effect", plots the component functions of the specified term for each model individually. The type = "series" option transposes the view to plot the effect trend over the models for each feature value.

Note: Comparative plotting for interaction terms (2D surfaces) is not supported for collection objects.

See also

plot.mid, ggmid.mids

Examples

# Use a lightweight dataset for fast execution
data(mtcars, package = "datasets")

# Fit two models with different complexities
fit1 <- lm(mpg ~ wt, data = mtcars)
mid1 <- interpret(mpg ~ wt, data = mtcars, model = fit1)
fit2 <- lm(mpg ~ wt + hp, data = mtcars)
mid2 <- interpret(mpg ~ wt + hp, data = mtcars, model = fit2)

# Combine them into a "midlist" collection (which inherits from "mids")
mids <- midlist("wt" = mid1, "wt + hp" = mid2)

# Compare the main effect of 'wt' across both models
plot(mids, term = "wt")


# Compare the effect of 'wt' as a series plot across the models
plot(mids, term = "wt", type = "series")