A generic plot-method for ggeffects-objects.
# S3 method for ggeffects
plot(
x,
show_ci = TRUE,
ci_style = c("ribbon", "errorbar", "dash", "dot"),
show_data = FALSE,
show_residuals = FALSE,
show_residuals_line = FALSE,
data_labels = FALSE,
limit_range = FALSE,
collapse_group = FALSE,
show_legend = TRUE,
show_title = TRUE,
show_x_title = TRUE,
show_y_title = TRUE,
case = NULL,
colors = NULL,
alpha = 0.15,
dot_alpha = 0.35,
jitter = NULL,
dodge = 0.25,
dot_size = NULL,
line_size = NULL,
use_theme = TRUE,
log_y = FALSE,
connect_lines = FALSE,
facets,
grid,
one_plot = TRUE,
n_rows = NULL,
verbose = TRUE,
ci = show_ci,
ci.style = ci_style,
rawdata = show_data,
add.data = show_data,
collapse.group = collapse_group,
dot.alpha = dot_alpha,
dot.size = dot_size,
line.size = line_size,
connect.lines = connect_lines,
show.title = show_title,
show.x.title = show_x_title,
show.y.title = show_y_title,
use.theme = use_theme,
show.legend = show_legend,
one.plot = one_plot,
log.y = log_y,
...
)theme_ggeffects(base_size = 11, base_family = "")
show_pals()
A ggplot2-object.
An object of class ggeffects, as returned by the functions
from this package.
Logical, if TRUE, confidence bands (for continuous variables
at x-axis) resp. error bars (for factors at x-axis) are plotted.
Character vector, indicating the style of the confidence
bands. May be either "ribbon", "errorbar", "dash" or "dot", to plot
a ribbon, error bars, or dashed or dotted lines as confidence bands.
Logical, if TRUE, a layer with raw data from response
by predictor on the x-axis, plotted as point-geoms, is added to the plot.
Note that if the model has a transformed response variable, and the
predicted values are not back-transformed (i.e. if back_transform = FALSE),
the raw data points are plotted on the transformed scale, i.e. same scale
as the predictions.
Logical, if TRUE, a layer with partial residuals is
added to the plot. See vignette
Effect Displays with Partial Residuals.
from effects for more details on partial residual plots.
Logical, if TRUE, a loess-fit line is added to the
partial residuals plot. Only applies if residuals is TRUE.
Logical, if TRUE and row names in data are available,
data points will be labelled by their related row name.
Logical, if TRUE, limits the range of the prediction
bands to the range of the data.
For mixed effects models, name of the grouping variable
of random effects. If collapse_group = TRUE, data points "collapsed"
by the first random effect groups are added to the plot. Else, if
collapse_group is a name of a group factor, data is collapsed by
that specific random effect. See collapse_by_group() for further
details.
Logical, shows or hides the plot legend.
Logical, shows or hides the plot title-
Logical, shows or hides the plot title for the x-axis.
Logical, shows or hides the plot title for the y-axis.
Desired target case. Labels will automatically converted into the
specified character case. See ?sjlabelled::convert_case for more details
on this argument.
Character vector with color values in hex-format, valid
color value names (see demo("colors")) or a name of a
ggeffects-color-palette.
Following options are valid for colors:
If not specified, the color brewer palette "Set1" will be used.
If "gs", a greyscale will be used.
If "bw", the plot is black/white and uses different line types to
distinguish groups.
There are some pre-defined color-palettes in this package that can be used,
e.g. colors = "metro". See show_pals() to show all available palettes.
Else specify own color values or names as vector (e.g.
colors = c("#f00000", "#00ff00")).
Alpha value for the confidence bands.
Alpha value for data points, when show_data = TRUE.
Numeric, between 0 and 1. If not NULL and show_data = TRUE,
adds a small amount of random variation to the location of data points dots,
to avoid overplotting. Hence the points don't reflect exact values in the
data. May also be a numeric vector of length two, to add different
horizontal and vertical jittering. For binary outcomes, raw data is not
jittered by default to avoid that data points exceed the axis limits.
Value for offsetting or shifting error bars, to avoid overlapping.
Only applies, if a factor is plotted at the x-axis (in such cases, the
confidence bands are replaced by error bars automatically), or if
ci_style = "errorbars".
Numeric, size of the point geoms.
Numeric, size of the line geoms.
Logical, if TRUE, a slightly tweaked version of ggplot's
minimal-theme, theme_ggeffects(), is applied to the plot. If FALSE, no
theme-modifications are applied.
Logical, if TRUE, the y-axis scale is log-transformed.
This might be useful for binomial models with predicted probabilities on
the y-axis.
Logical, if TRUE and plot has point-geoms with
error bars (this is usually the case when the x-axis is discrete), points
of same groups will be connected with a line.
Logical, defaults to TRUE if x has a column named
facet, and defaults to FALSE if x has no such column. Set
facets = TRUE to wrap the plot into facets even for grouping variables
(see 'Examples'). grid is an alias for facets.
Logical, if TRUE and x has a panel column (i.e. when
four terms were used), a single, integrated plot is produced.
Number of rows to align plots. By default, all plots are aligned in one row. For facets, or multiple panels, plots can also be aligned in multiiple rows, to avoid that plots are too small.
Logical, toggle warnings and messages.
Deprecated arguments. Use show_ci, show_data, collapse_group,
dot_alpha, dot_size, line_size, connect_lines, show_title,
show_x_title, show_y_title, use_theme, ci_style, show_legend,
log_y and one_plot instead.
Further arguments passed down to ggplot::scale_y*(), to
control the appearance of the y-axis.
Base font size.
Base font family.
For generalized linear models (glms), residualized scores are
computed as inv.link(link(Y) + r) where Y are the predicted
values on the response scale, and r are the working residuals.
For (generalized) linear mixed models, the random effect are also
partialled out.
For proportional odds logistic regression (see ?MASS::polr)
or cumulative link models in general, plots are automatically facetted
by response.level, which indicates the grouping of predictions based on
the level of the model's response.
if (FALSE) { # requireNamespace("ggplot2") && requireNamespace("sjlabelled")
library(sjlabelled)
data(efc)
efc$c172code <- as_label(efc$c172code)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
dat <- predict_response(fit, terms = "c12hour")
plot(dat)
# \donttest{
# facet by group, use pre-defined color palette
dat <- predict_response(fit, terms = c("c12hour", "c172code"))
plot(dat, facet = TRUE, colors = "hero")
# don't use facets, b/w figure, w/o confidence bands
dat <- predict_response(fit, terms = c("c12hour", "c172code"))
plot(dat, colors = "bw", show_ci = FALSE)
# factor at x axis, plot exact data points and error bars
dat <- predict_response(fit, terms = c("c172code", "c161sex"))
plot(dat)
# for three variables, automatic facetting
dat <- predict_response(fit, terms = c("c12hour", "c172code", "c161sex"))
plot(dat)
# }
# show all color palettes
show_pals()
}
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