
Last chance! 50% off unlimited learning
Sale ends in
Get variable and value labels from ggeffects
-objects. Functions
like ggpredict()
or gginteraction()
save
information on variable names and value labels as additional attributes
in the returned data frame. This is especially helpful for labelled
data (see sjlabelled), since these labels can be used to
set axis labels and titles.
get_title(x, case = NULL)get_x_title(x, case = NULL)
get_y_title(x, case = NULL)
get_legend_title(x, case = NULL)
get_legend_labels(x, case = NULL)
get_x_labels(x, case = NULL)
get_complete_df(x, case = NULL)
An object of class ggeffects
, as returned by any ggeffects-function;
for get_complete_df()
, must be a list of ggeffects
-objects.
Desired target case. Labels will automatically converted into the
specified character case. See convert_case
for
more details on this argument.
The titles or labels as character string, or NULL
, if variables
had no labels; get_complete_df()
returns the input list x
as single data frame, where the grouping variable indicates the
marginal effects for each term.
# NOT RUN {
data(efc)
efc$c172code <- sjmisc::to_factor(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
mydf <- ggpredict(fit, terms = c("c12hour", "c161sex", "c172code"))
library(ggplot2)
ggplot(mydf, aes(x = x, y = predicted, colour = group)) +
stat_smooth(method = "lm") +
facet_wrap(~facet, ncol = 2) +
labs(
x = get_x_title(mydf),
y = get_y_title(mydf),
colour = get_legend_title(mydf)
)
# get marginal effects, a list of tibbles (one tibble per term)
eff <- ggalleffects(fit)
eff
get_complete_df(eff)
# get marginal effects for education only, and get x-axis-labels
mydat <- eff[["c172code"]]
ggplot(mydat, aes(x = x, y = predicted, group = group)) +
stat_summary(fun.y = sum, geom = "line") +
scale_x_discrete(labels = get_x_labels(mydat))
# }
Run the code above in your browser using DataLab