Marginal Effects Plots

The plots function generates one or more graphs displaying the relationship between each predictor and the outcome, controlling for the other predictors in the model.

me_plots(
  x,
  points = FALSE,
  size = 1,
  alpha = 0.2,
  jitter = 0.03,
  ci = TRUE,
  page = FALSE,
  ...
)

Arguments

x

an object of class "lm" or "glm"

points

logical; If TRUE, raw data points are plotted (default = FALSE)

size

size of data points (default = 1)

alpha

transparency of data points (from 0 to 1; default is 0.2)

jitter

amount of jitter for points (from 0 to 1; default = .03)

ci

logical; If TRUE, display 95% confidence intervals (default = TRUE)

page

logical; If TRUE, each graph will be printed separately.

...

additional arguments to be passed to plot.ggeffects.

Value

a list of ggplot2 graphs.

Details

The plots function uses the ggeffect and plot.ggeffects functions from the ggeffects package to plot marginal effects for a linear or generalized linear model.

A plot is created for each predictor variable (called the "focal" variable). Predicted response values are calculated by varying the values of the focal variable while keeping the non-focal variables (the other predictor variables) constant. Non-focal numeric variables are set to their means. If factors are present in the model, predicted values are calculated for each combination of non-focal categorical variable levels and averaged based on category size. See the https://strengejacke.github.io/ggeffects/index.html for details.

If page = TRUE, the individual graphs are combined into a single plot via the the wrap_plots function in the patchwork package. Otherwise, individual plots are printed.

Limitations

The me_plots function can not handle models that include as.factor or factor in their formulas. Create the modified variables prior to fitting the model (e.g., mtcars$cyl <- factor(mtcars$cyl)). Additionally, the function can not handle models that include interactions. In this case, consider using the ggeffects package with grouping and faceting.

Examples

#######################
# Multiple regression #
#######################
fit <- lm(mpg ~ hp + wt + accel + origin, data = auto_mpg)
me_plots(fit)


#######################
# Logistic regression #
#######################
fit2 <- glm(caesarian ~ age + bp + delivery.time, family = binomial, data = caesarian)
me_plots(fit2)