Description Usage Arguments Details Value Examples
A generic plotmethod for ggeffects
objects.
1 2 3 
x 
An object of class 
ci 
Logical, if 
facets 
Logical, defaults to 
rawdata 
Logical, if 
colors 
Character vector with color values in hexformat, valid
color value names (see

alpha 
Alpha value for the confidence bands, or for data points. Data points
are only plotted for 
dodge 
Value for offsetting or shifting error bars, to avoid overlapping. Only applies, if a factor is plotted at the xaxis; in such cases, the confidence bands are replaced by error bars. 
... 
Currently not used. 
ggpredict()
with argument full.data = FALSE
computes
marginal effects at the mean, where covariates are held constant. In
this case, the slope between groups does not vary and the standard
errors and confidence intervals have the same "trend" as the predicted
values. Hence, plotting confidence bands or error bars is possible.
However, ggpredict()
with argument full.data = TRUE
,
covariates and standard errors vary between groups, so plotting
confidence bands and error bars would follow a "winding" shape,
while the predicted values are smoothened by geom_smooth
.
Predicted values and confidence bands or error bars would no
longer match, thus, ci
is automatically set to FALSE
in such cases. You still may want to plot objects returned by
ggpredict()
with argument full.data = TRUE
to additionally
plot the raw data points, which is automatically done.
For ggaverage()
, which computes average marginal effects,
the same problem with standard errors and confidence bands would
apply. However, the standard errors for the average marginal effects
are taken from the marginal effects at the mean, and the predicted
values from the average marginal effects are used to compute
another regression on these values, to get the "smoothened" values
that are used to compute standard errors and confidence intervals
that match the predicted values of the average marginal effects
(maybe, at this point, it is helpful to inspect the code to better
understand what is happening...).
A ggplot2object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  library(sjmisc)
data(efc)
efc$c172code < to_label(efc$c172code)
fit < lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
dat < ggpredict(fit, terms = "c12hour")
plot(dat)
dat < ggpredict(fit, terms = "c12hour", full.data = TRUE)
plot(dat)
dat < ggaverage(fit, terms = "neg_c_7")
plot(dat)
# facet by group
dat < ggpredict(fit, terms = c("c12hour", "c172code"))
plot(dat, facet = TRUE)
# don't use facets, b/w figure, w/o confidence bands
dat < ggaverage(fit, terms = c("c12hour", "c172code"))
plot(dat, colors = "bw", ci = FALSE)
# factor at x axis, plot exact data points and error bars
dat < ggpredict(fit, terms = c("c172code", "c161sex"))
plot(dat)
# for three variables, automatic facetting
dat < ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
plot(dat)

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