`R/conditional_effects.R`

`conditional_effects.brmsfit.Rd`

Display 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,
...
)
```

- x
An object of class

`brmsfit`

.- effects
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.- conditions
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.- int_conditions
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.- re_formula
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.- prob
A value between 0 and 1 indicating the desired probability to be covered by the uncertainty intervals. The default is 0.95.

- robust
If

`TRUE`

(the default) the median is used as the measure of central tendency. If`FALSE`

the mean is used instead.- method
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.- spaghetti
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.- surface
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.- categorical
Logical. Indicates if effects of categorical or ordinal models should be shown in terms of probabilities of response categories. Defaults to

`FALSE`

.- ordinal
(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`

.- transform
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"`

.- resolution
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.- select_points
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.- too_far
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.- probs
(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`

.- ncol
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`

.- points
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.- rug
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.- mean
Logical. Only relevant for spaghetti plots. If

`TRUE`

(the default), display the mean regression line on top of the regression lines for each sample.- jitter_width
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.- stype
Indicates how surface plots should be displayed. Either

`"contour"`

or`"raster"`

.- line_args
Only used in plots of continuous predictors: A named list of arguments passed to

`geom_smooth`

.- cat_args
Only used in plots of categorical predictors: A named list of arguments passed to

`geom_point`

.- errorbar_args
Only used in plots of categorical predictors: A named list of arguments passed to

`geom_errorbar`

.- surface_args
Only used in surface plots: A named list of arguments passed to

`geom_contour`

or`geom_raster`

(depending on argument`stype`

).- spaghetti_args
Only used in spaghetti plots: A named list of arguments passed to

`geom_smooth`

.- point_args
Only used if

`points = TRUE`

: A named list of arguments passed to`geom_jitter`

.- rug_args
Only used if

`rug = TRUE`

: A named list of arguments passed to`geom_rug`

.- facet_args
Only used if if multiple condtions are provided: A named list of arguments passed to

`facet_wrap`

.- theme
A

`theme`

object modifying the appearance of the plots. For some basic themes see`ggtheme`

and`theme_default`

.- ask
Logical; indicates if the user is prompted before a new page is plotted. Only used if

`plot`

is`TRUE`

.- plot
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)
}
```