Uses the ggplot2 package to draw a point-range plot of the average marginal effects computed by tidy.
# S3 method for marginaleffects
plot(x, conf_level = 0.95, ...)A ggplot2 object
An object produced by the marginaleffects function.
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
Additional arguments are passed to the predict() method
supplied by the modeling package.These arguments are particularly useful
for mixed-effects or bayesian models (see the online vignettes on the
marginaleffects website). Available arguments can vary from model to
model, depending on the range of supported arguments by each modeling
package. See the "Model-Specific Arguments" section of the
?marginaleffects documentation for a non-exhaustive list of available
arguments.
The tidy function calculates average marginal effects by taking the mean
of all the unit-level marginal effects computed by the marginaleffects
function.
The standard error of the average marginal effects is obtained by taking the mean of each column of the Jacobian. . Then, we use this "Jacobian at the mean" in the Delta method to obtained standard errors.
In Bayesian models (e.g., brms), we compute Average Marginal
Effects by applying the mean function twice. First, we apply it to all
marginal effects for each posterior draw, thereby estimating one Average (or
Median) Marginal Effect per iteration of the MCMC chain. Second, we take
the mean and quantile function to the results of Step 1 to obtain the
Average (or Median) Marginal Effect and its associated interval.
Other plot:
plot_cap(),
plot_cco(),
plot_cme()
mod <- glm(am ~ hp + wt, data = mtcars)
mfx <- marginaleffects(mod)
plot(mfx)
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