comparisons objectCalculate average contrasts by taking the mean of all the
unit-level contrasts computed by the predictions function.
# S3 method for comparisons
tidy(x, conf_level = 0.95, by = NULL, transform_post = NULL, ...)An object produced by the comparisons function.
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
Character vector of variable names over which to compute group-averaged contrasts.
(experimental) A function applied to the estimate and confidence interval just before returning the final results. For example, users can exponentiate their final results by setting transform_post=exp or transform contrasts made on the link scale for ease of interpretation.
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.
A "tidy" data.frame of summary statistics which conforms to the
broom package specification.
To compute standard errors around the average marginaleffects, we begin by applying the mean function to each column of the Jacobian. Then, we use this matrix 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
calculate the mean and the quantile function to the results of Step 1 to
obtain the Average Marginal Effect and its associated interval.
# NOT RUN {
mod <- lm(mpg ~ factor(gear), data = mtcars)
contr <- comparisons(mod, contrast_factor = "sequential")
tidy(contr)
# }
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