```
if (FALSE) { # requireNamespace("sjlabelled") && requireNamespace("ggplot2")
library(sjlabelled)
data(efc)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = "c12hour")
ggpredict(fit, terms = c("c12hour", "c172code"))
ggpredict(fit, terms = c("c12hour", "c172code", "c161sex"))
# specified as formula
ggpredict(fit, terms = ~ c12hour + c172code + c161sex)
# only range of 40 to 60 for variable 'c12hour'
ggpredict(fit, terms = "c12hour [40:60]")
# terms as named list
ggpredict(fit, terms = list(c12hour = 40:60))
# covariate "neg_c_7" is held constant at a value of 11.84 (its mean value).
# To use a different value, use "condition"
ggpredict(fit, terms = "c12hour [40:60]", condition = c(neg_c_7 = 20))
# to plot ggeffects-objects, you can use the 'plot()'-function.
# the following examples show how to build your ggplot by hand.
if (FALSE) {
# plot predicted values, remaining covariates held constant
library(ggplot2)
mydf <- ggpredict(fit, terms = "c12hour")
ggplot(mydf, aes(x, predicted)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)
# three variables, so we can use facets and groups
mydf <- ggpredict(fit, terms = c("c12hour", "c161sex", "c172code"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet, ncol = 2)
# select specific levels for grouping terms
mydf <- ggpredict(fit, terms = c("c12hour", "c172code [1,3]", "c161sex"))
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~facet) +
labs(
y = get_y_title(mydf),
x = get_x_title(mydf),
colour = get_legend_title(mydf)
)
# level indication also works for factors with non-numeric levels
# and in combination with numeric levels for other variables
data(efc)
efc$c172code <- sjlabelled::as_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
ggpredict(fit, terms = c("c12hour",
"c172code [low level of education, high level of education]",
"c161sex [1]"))
# when "terms" is a named list
ggpredict(fit, terms = list(
c12hour = seq(0, 170, 30),
c172code = c("low level of education", "high level of education"),
c161sex = 1)
)
# use categorical value on x-axis, use axis-labels, add error bars
dat <- ggpredict(fit, terms = c("c172code", "c161sex"))
ggplot(dat, aes(x, predicted, colour = group)) +
geom_point(position = position_dodge(.1)) +
geom_errorbar(
aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(.1)
) +
scale_x_discrete(breaks = 1:3, labels = get_x_labels(dat))
# 3-way-interaction with 2 continuous variables
data(efc)
# make categorical
efc$c161sex <- as_factor(efc$c161sex)
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)
# select only levels 30, 50 and 70 from continuous variable Barthel-Index
dat <- ggpredict(fit, terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))
ggplot(dat, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm", se = FALSE, fullrange = TRUE) +
facet_wrap(~facet) +
labs(
colour = get_legend_title(dat),
x = get_x_title(dat),
y = get_y_title(dat),
title = get_title(dat)
)
# or with ggeffects' plot-method
plot(dat, ci = FALSE)}
# predictions for polynomial terms
data(efc)
fit <- glm(
tot_sc_e ~ c12hour + e42dep + e17age + I(e17age^2) + I(e17age^3),
data = efc,
family = poisson()
)
ggeffect(fit, terms = "e17age")
}
```

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