ggeffects (version 0.11.0)

plot: Plot ggeffects-objects

Description

A generic plot-method for ggeffects-objects.

Usage

# S3 method for ggeffects
plot(x, ci = TRUE, ci.style = c("ribbon",
  "errorbar", "dash", "dot"), facets, rawdata = FALSE, colors = "Set1",
  alpha = 0.15, dodge = 0.25, use.theme = TRUE, dot.alpha = 0.5,
  jitter = 0.2, log.y = FALSE, case = NULL, show.legend = TRUE,
  show.title = TRUE, show.x.title = TRUE, show.y.title = TRUE,
  dot.size = NULL, line.size = NULL, connect.lines = FALSE, grid,
  one.plot = TRUE, ...)
theme_ggeffects(base_size = 11, base_family = "")
show_pals()

Arguments

x

An object of class ggeffects, as returned by the functions from this package.

ci

Logical, if TRUE, confidence bands (for continuous variables at x-axis) resp. error bars (for factors at x-axis) are plotted.For ggeffects-objects from ggpredict() with argument full.data = TRUE, ci is automatically set to FALSE.

ci.style

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.

facets, grid

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.

rawdata

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.

colors

Character vector with color values in hex-format, valid color value names (see demo("colors")) or a name of a color brewer 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.

• If colors is any valid color brewer palette name, the related palette will be used. Use display.brewer.all to view all available palette names.

• 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

Alpha value for the confidence bands.

dodge

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".

use.theme

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.

dot.alpha

Alpha value for data points, when rawdata = TRUE.

jitter

Numeric, between 0 and 1. If not NULL and rawdata = 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.

log.y

Logical, if TRUE, the y-axis scale is log-transformed. This might be useful for binomial models with predicted probabilities on the y-axis.

case

Desired target case. Labels will automatically converted into the specified character case. See convert_case for more details on this argument.

show.legend

Logical, shows or hides the plot legend.

show.title

Logical, shows or hides the plot title-

show.x.title

Logical, shows or hides the plot title for the x-axis.

show.y.title

Logical, shows or hides the plot title for the y-axis.

dot.size

Numeric, size of the point geoms.

line.size

Numeric, size of the line geoms.

connect.lines

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.

one.plot

Logical, if TRUE and x has a panel column (i.e. when four terms were used), a single, integrated plot is produced.

...

Further arguments passed down to ggplot::scale_y*(), to control the appearance of the y-axis.

base_size

Base font size.

base_family

Base font family.

Value

A ggplot2-object.

Details

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 averaged predicted values, the same problem with standard errors and confidence bands would apply. However, the standard errors are taken from the marginal effects at the mean, and the predicted values from the averaged predictions 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 averaged predicted values (maybe, at this point, it is helpful to inspect the code to better understand what is happening...).

For proportional odds logistic regression (see 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.

Examples

# NOT RUN {
library(sjlabelled)
data(efc)
efc$c172code <- as_label(efc$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)
plot(dat, ci = "dash")

# facet by group, use pre-defined color palette
dat <- ggpredict(fit, terms = c("c12hour", "c172code"))
plot(dat, facet = TRUE, colors = "hero")

# 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)


# }