Compute estimated marginal means for specified factors.
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Categorical predictors over which to compute marginal means
Categorical predictors used to construct the
prediction grid over which adjusted predictions are averaged (character
Matrix or boolean
Type(s) of prediction as string or vector This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero".
This function begins by calling the
predictions function to obtain a
grid of predictors, and adjusted predictions for each cell. The grid
includes all combinations of the categorical variables listed in the
variables_grid arguments, or all combinations of the
categorical variables used to fit the model if
In the prediction grid, numeric variables are held at their means.
After constructing the grid and filling the grid with adjusted predictions,
marginalmeans computes marginal means for the variables listed in the
variables argument, by average across all categories in the grid.
marginalmeans can only compute standard errors for linear models, or for
predictions on the link scale, that is, with the
type argument set to
marginaleffects website compares the output of this function to the
emmeans package, which provides similar but more advanced
Data frame of marginal means with one row per variable-value combination.
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