ggeffects (version 0.4.0)

ggalleffects: 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.

Examples

# NOT RUN {
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)

# }