R: Get marginal effects for all model predictors

ggalleffects {ggeffects}

R Documentation

Get marginal effects for all model predictors

Description

ggalleffects() computes marginal effects of model terms. It internally calls allEffects and puts the result into tidy data frames.

Usage

ggalleffects(model, terms = NULL, ci.lvl = 0.95, ...)

Arguments

`model`	A fitted model object, or a list of model objects. Any model that is supported by the effects-package should work.
`terms`	Character vector with term names of selected variables from `model`, which should be used to compute marginal effects. If `terms = NULL`, marginal effects for all model terms are returned.
`ci.lvl`	Numeric, the level of the confidence intervals. For `ggpredict()`, use `ci.lvl = NA`, if confidence intervals should not be calculated (for instance, due to computation time).
`...`	Further arguments passed down to `allEffects`.

Value

A list of tibbles (with ggeffects class attribute) with consistent data columns. The list contains one tibble per model term. Columns are:

x: the values of the model predictor to which the effect pertains, used as x-position in plots.
predicted: the predicted values, used as y-position in plots.
conf.low: the lower bound of the confidence interval for the predicted values.
conf.high: the upper bound of the confidence interval for the predicted values.

Note

Interaction effects are not included in the return value, i.e. ggalleffects() does not compute marginal effects for interaction terms. Use gginteraction to create tidy data frames especially for interaction terms.

Examples

data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggalleffects(fit)

library(ggplot2)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c160age, data = efc)
mydf <- get_complete_df(ggalleffects(fit))

ggplot(mydf, aes(x, predicted)) +
  geom_line() +
  facet_wrap(~group, scale = "free_x", ncol = 1)

[Package ggeffects version 0.4.0 Index]