R/conditional_effects.R
conditional_effects.brmsfit.RdDisplay conditional effects of one or more numeric and/or categorical predictors including two-way interaction effects.
# S3 method for brmsfit
conditional_effects(
x,
effects = NULL,
conditions = NULL,
int_conditions = NULL,
re_formula = NA,
prob = 0.95,
robust = TRUE,
method = "posterior_epred",
spaghetti = FALSE,
surface = FALSE,
categorical = FALSE,
ordinal = FALSE,
transform = NULL,
resolution = 100,
select_points = 0,
too_far = 0,
probs = NULL,
...
)
conditional_effects(x, ...)
# S3 method for brms_conditional_effects
plot(
x,
ncol = NULL,
points = getOption("brms.plot_points", FALSE),
rug = getOption("brms.plot_rug", FALSE),
mean = TRUE,
jitter_width = 0,
stype = c("contour", "raster"),
line_args = list(),
cat_args = list(),
errorbar_args = list(),
surface_args = list(),
spaghetti_args = list(),
point_args = list(),
rug_args = list(),
facet_args = list(),
theme = NULL,
ask = TRUE,
plot = TRUE,
...
)An object of class brmsfit.
An optional character vector naming effects (main effects or
interactions) for which to compute conditional plots. Interactions are
specified by a : between variable names. If NULL (the
default), plots are generated for all main effects and two-way interactions
estimated in the model. When specifying effects manually, all
two-way interactions (including grouping variables) may be plotted
even if not originally modeled.
An optional data.frame containing variable values
to condition on. Each effect defined in effects will
be plotted separately for each row of conditions. Values in the
cond__ column will be used as titles of the subplots. If cond__
is not given, the row names will be used for this purpose instead.
It is recommended to only define a few rows in order to keep the plots clear.
See make_conditions for an easy way to define conditions.
If NULL (the default), numeric variables will be conditionalized by
using their means and factors will get their first level assigned.
NA values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.
An optional named list whose elements are
vectors of values of the variables specified in effects.
At these values, predictions are evaluated. The names of
int_conditions have to match the variable names exactly.
Additionally, the elements of the vectors may be named themselves,
in which case their names appear as labels for the conditions in the plots.
Instead of vectors, functions returning vectors may be passed and are
applied on the original values of the corresponding variable.
If NULL (the default), predictions are evaluated at the
\(mean\) and at \(mean +/- sd\) for numeric predictors and at
all categories for factor-like predictors.
A formula containing group-level effects to be considered
in the conditional predictions. If NULL, include all group-level
effects; if NA (default), include no group-level effects.
A value between 0 and 1 indicating the desired probability to be covered by the uncertainty intervals. The default is 0.95.
If TRUE (the default) the median is used as the
measure of central tendency. If FALSE the mean is used instead.
Method used to obtain predictions. Can be set to
"posterior_epred" (the default), "posterior_predict",
or "posterior_linpred". For more details, see the respective
function documentations.
Logical. Indicates if predictions should
be visualized via spaghetti plots. Only applied for numeric
predictors. If TRUE, it is recommended
to set argument ndraws to a relatively small value
(e.g., 100) in order to reduce computation time.
Logical. Indicates if interactions or
two-dimensional smooths should be visualized as a surface.
Defaults to FALSE. The surface type can be controlled
via argument stype of the related plotting method.
Logical. Indicates if effects of categorical
or ordinal models should be shown in terms of probabilities
of response categories. Defaults to FALSE.
(Deprecated) Please use argument categorical.
Logical. Indicates if effects in ordinal models
should be visualized as a raster with the response categories
on the y-axis. Defaults to FALSE.
A function or a character string naming
a function to be applied on the predicted responses
before summary statistics are computed. Only allowed
if method = "posterior_predict".
Number of support points used to generate
the plots. Higher resolution leads to smoother plots.
Defaults to 100. If surface is TRUE,
this implies 10000 support points for interaction terms,
so it might be necessary to reduce resolution
when only few RAM is available.
Positive number.
Only relevant if points or rug are set to TRUE:
Actual data points of numeric variables that
are too far away from the values specified in conditions
can be excluded from the plot. Values are scaled into
the unit interval and then points more than select_points
from the values in conditions are excluded.
By default, all points are used.
Positive number.
For surface plots only: Grid points that are too
far away from the actual data points can be excluded from the plot.
too_far determines what is too far. The grid is scaled into
the unit square and then grid points more than too_far
from the predictor variables are excluded. By default, all
grid points are used. Ignored for non-surface plots.
(Deprecated) The quantiles to be used in the computation of
uncertainty intervals. Please use argument prob instead.
Further arguments such as draw_ids or ndraws
passed to posterior_predict or posterior_epred.
Number of plots to display per column for each effect.
If NULL (default), ncol is computed internally based
on the number of rows of conditions.
Logical. Indicates if the original data points should be added
via geom_jitter. Default is FALSE. Can be controlled
globally via the brms.plot_points option. Note that only those data
points will be added that match the specified conditions defined in
conditions. For categorical predictors, the conditions have to match
exactly. For numeric predictors, argument select_points is used to
determine, which points do match a condition.
Logical. Indicates if a rug representation of predictor values
should be added via geom_rug. Default is FALSE.
Depends on select_points in the same way as points does. Can
be controlled globally via the brms.plot_rug option.
Logical. Only relevant for spaghetti plots.
If TRUE (the default), display the mean regression
line on top of the regression lines for each sample.
Only used if points = TRUE:
Amount of horizontal jittering of the data points.
Mainly useful for ordinal models. Defaults to 0 that
is no jittering.
Indicates how surface plots should be displayed.
Either "contour" or "raster".
Only used in plots of continuous predictors:
A named list of arguments passed to
geom_smooth.
Only used in plots of categorical predictors:
A named list of arguments passed to
geom_point.
Only used in plots of categorical predictors:
A named list of arguments passed to
geom_errorbar.
Only used in surface plots:
A named list of arguments passed to
geom_contour or
geom_raster
(depending on argument stype).
Only used in spaghetti plots:
A named list of arguments passed to
geom_smooth.
Only used if points = TRUE:
A named list of arguments passed to
geom_jitter.
Only used if rug = TRUE:
A named list of arguments passed to
geom_rug.
Only used if if multiple condtions are provided:
A named list of arguments passed to
facet_wrap.
A theme object
modifying the appearance of the plots.
For some basic themes see ggtheme
and theme_default.
Logical; indicates if the user is prompted
before a new page is plotted.
Only used if plot is TRUE.
Logical; indicates if plots should be
plotted directly in the active graphic device.
Defaults to TRUE.
An object of class 'brms_conditional_effects' which is a
named list with one data.frame per effect containing all information
required to generate conditional effects plots. Among others, these
data.frames contain some special variables, namely estimate__
(predicted values of the response), se__ (standard error of the
predicted response), lower__ and upper__ (lower and upper
bounds of the uncertainty interval of the response), as well as
cond__ (used in faceting when conditions contains multiple
rows).
The corresponding plot method returns a named
list of ggplot objects, which can be further
customized using the ggplot2 package.
When creating conditional_effects for a particular predictor
(or interaction of two predictors), one has to choose the values of all
other predictors to condition on. By default, the mean is used for
continuous variables and the reference category is used for factors, but
you may change these values via argument conditions. This also has
an implication for the points argument: In the created plots, only
those points will be shown that correspond to the factor levels actually
used in the conditioning, in order not to create the false impression of
bad model fit, where it is just due to conditioning on certain factor
levels.
To fully change colors of the created plots, one has to amend both
scale_colour and scale_fill. See
scale_colour_grey or scale_colour_gradient for
more details.
if (FALSE) {
fit <- brm(count ~ zAge + zBase * Trt + (1 | patient),
data = epilepsy, family = poisson())
## plot all conditional effects
plot(conditional_effects(fit), ask = FALSE)
## change colours to grey scale
library(ggplot2)
ce <- conditional_effects(fit, "zBase:Trt")
plot(ce, plot = FALSE)[[1]] +
scale_color_grey() +
scale_fill_grey()
## only plot the conditional interaction effect of 'zBase:Trt'
## for different values for 'zAge'
conditions <- data.frame(zAge = c(-1, 0, 1))
plot(conditional_effects(fit, effects = "zBase:Trt",
conditions = conditions))
## also incorporate group-level effects variance over patients
## also add data points and a rug representation of predictor values
plot(conditional_effects(fit, effects = "zBase:Trt",
conditions = conditions, re_formula = NULL),
points = TRUE, rug = TRUE)
## change handling of two-way interactions
int_conditions <- list(
zBase = setNames(c(-2, 1, 0), c("b", "c", "a"))
)
conditional_effects(fit, effects = "Trt:zBase",
int_conditions = int_conditions)
conditional_effects(fit, effects = "Trt:zBase",
int_conditions = list(zBase = quantile))
## fit a model to illustrate how to plot 3-way interactions
fit3way <- brm(count ~ zAge * zBase * Trt, data = epilepsy)
conditions <- make_conditions(fit3way, "zAge")
conditional_effects(fit3way, "zBase:Trt", conditions = conditions)
## only include points close to the specified values of zAge
ce <- conditional_effects(
fit3way, "zBase:Trt", conditions = conditions,
select_points = 0.1
)
plot(ce, points = TRUE)
}