plot is a generic plot-method for ggeffects-objects.
ggeffects_palette() returns show_palettes()
Usage
# S3 method for class '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_size = NULL,
  dot_alpha = 0.35,
  dot_shape = NULL,
  line_size = NULL,
  jitter = NULL,
  dodge = 0.25,
  use_theme = TRUE,
  log_y = FALSE,
  connect_lines = FALSE,
  facets,
  grid,
  one_plot = TRUE,
  n_rows = NULL,
  verbose = TRUE,
  ...
)
theme_ggeffects(base_size = 11, base_family = "")
ggeffects_palette(palette = "metro", n = NULL)
show_palettes()Arguments
- x
- An object of class - ggeffects, as returned by the functions from this package.
- show_ci
- Logical, if - TRUE, confidence bands (for continuous variables at x-axis) resp. error bars (for factors at x-axis) are plotted.
- 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.
- show_data
- 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.
- show_residuals
- 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.
- show_residuals_line
- Logical, if - TRUE, a loess-fit line is added to the partial residuals plot. Only applies if- residualsis- TRUE.
- data_labels
- Logical, if - TRUEand row names in data are available, data points will be labelled by their related row name.
- limit_range
- Logical, if - TRUE, limits the range of the prediction bands to the range of the data.
- collapse_group
- 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_groupis a name of a group factor, data is collapsed by that specific random effect. See- collapse_by_group()for further details.
- 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. 
- case
- Desired target case. Labels will automatically converted into the specified character case. See - ?sjlabelled::convert_casefor more details on this argument.
- colors
- Character vector with color values in hex-format, valid color value names (see - demo("colors")) or a name of a ggeffects-color-palette (see- ggeffects_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_palettes()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. 
- dot_size
- Numeric, size of the point geoms. 
- dot_alpha
- Alpha value for data points, when - show_data = TRUE.
- dot_shape
- Shape of data points, when - show_data = TRUE.
- line_size
- Numeric, size of the line geoms. 
- jitter
- Numeric, between 0 and 1. If not - NULLand- 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.
- 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.
- 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.
- connect_lines
- Logical, if - TRUEand 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.
- facets, grid
- Logical, defaults to - TRUEif- xhas a column named- facet, and defaults to- FALSEif- xhas no such column. Set- facets = TRUEto wrap the plot into facets even for grouping variables (see 'Examples').- gridis an alias for- facets.
- one_plot
- Logical, if - TRUEand- xhas a- gridcolumn (i.e. when five- termswere used), a single, integrated plot is produced.
- n_rows
- 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. 
- verbose
- Logical, toggle warnings and messages. 
- ...
- 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. 
- palette
- Name of a pre-defined color-palette as string. See - show_palettes()to show all available palettes. Use- NULLto return a list with names and color-codes of all avaibale palettes.
- n
- Number of color-codes from the palette that should be returned. 
Details
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.
Note
Load library(ggplot2) and use theme_set(theme_ggeffects()) to set
the ggeffects-theme as default plotting theme. You can then use further
plot-modifiers, e.g. from sjPlot, like legend_style() or font_size()
without losing the theme-modifications.
There are pre-defined colour palettes in this package. Use show_palettes()
to show all available colour palettes as plot, or
ggeffects_palette(palette = NULL) to show the color codes.
Partial Residuals
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.
Examples
library(sjlabelled)
#> 
#> Attaching package: ‘sjlabelled’
#> The following object is masked from ‘package:ggplot2’:
#> 
#>     as_label
#> The following objects are masked from ‘package:datawizard’:
#> 
#>     to_factor, to_numeric
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")
# \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)
# 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)
# 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)
# for three variables, automatic facetting
dat <- predict_response(fit, terms = c("c12hour", "c172code", "c161sex"))
plot(dat)
 # }
# show color codes of specific palette
ggeffects_palette("okabe-ito")
#> [1] "#E69F00" "#56B4E9" "#009E73" "#CC79A7" "#F0E442" "#999999" "#000000"
#> [8] "#0072B2" "#D55E00"
# show all color palettes
show_palettes()
# }
# show color codes of specific palette
ggeffects_palette("okabe-ito")
#> [1] "#E69F00" "#56B4E9" "#009E73" "#CC79A7" "#F0E442" "#999999" "#000000"
#> [8] "#0072B2" "#D55E00"
# show all color palettes
show_palettes()
 
