% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/conditional_smooths.R
\name{conditional_smooths.brmsfit}
\alias{conditional_smooths.brmsfit}
\alias{marginal_smooths}
\alias{marginal_smooths.brmsfit}
\alias{conditional_smooths}
\title{Display Smooth Terms}
\usage{
\method{conditional_smooths}{brmsfit}(
  x,
  smooths = NULL,
  int_conditions = NULL,
  prob = 0.95,
  spaghetti = FALSE,
  surface = TRUE,
  resolution = 100,
  too_far = 0,
  ndraws = NULL,
  draw_ids = NULL,
  nsamples = NULL,
  subset = NULL,
  probs = NULL,
  ...
)

conditional_smooths(x, ...)
}
\arguments{
\item{x}{An object of class \code{brmsfit}.}

\item{smooths}{Optional character vector of smooth terms
to display. If \code{NULL} (the default) all smooth terms
are shown.}

\item{int_conditions}{An optional named \code{list} whose elements are
vectors of values of the variables specified in \code{effects}.
At these values, predictions are evaluated. The names of
\code{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 \code{NULL} (the default), predictions are evaluated at the
\eqn{mean} and at \eqn{mean +/- sd} for numeric predictors and at
all categories for factor-like predictors.}

\item{prob}{A value between 0 and 1 indicating the desired probability
to be covered by the uncertainty intervals. The default is 0.95.}

\item{spaghetti}{Logical. Indicates if predictions should
be visualized via spaghetti plots. Only applied for numeric
predictors. If \code{TRUE}, it is recommended
to set argument \code{ndraws} to a relatively small value
(e.g., \code{100}) in order to reduce computation time.}

\item{surface}{Logical. Indicates if interactions or
two-dimensional smooths should be visualized as a surface.
Defaults to \code{TRUE}. The surface type can be controlled
via argument \code{stype} of the related plotting method.}

\item{resolution}{Number of support points used to generate
the plots. Higher resolution leads to smoother plots.
Defaults to \code{100}. If \code{surface} is \code{TRUE},
this implies \code{10000} support points for interaction terms,
so it might be necessary to reduce \code{resolution}
when only few RAM is available.}

\item{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.
\code{too_far} determines what is too far. The grid is scaled into
the unit square and then grid points more than \code{too_far}
from the predictor variables are excluded. By default, all
grid points are used. Ignored for non-surface plots.}

\item{ndraws}{Positive integer indicating how many
posterior draws should be used.
If \code{NULL} (the default) all draws are used.
Ignored if \code{draw_ids} is not \code{NULL}.}

\item{draw_ids}{An integer vector specifying
the posterior draws to be used.
If \code{NULL} (the default), all draws are used.}

\item{nsamples}{Deprecated alias of \code{ndraws}.}

\item{subset}{Deprecated alias of \code{draw_ids}.}

\item{probs}{(Deprecated) The quantiles to be used in the computation of
uncertainty intervals. Please use argument \code{prob} instead.}

\item{...}{Currently ignored.}
}
\value{
For the \code{brmsfit} method,
an object of class \code{brms_conditional_effects}. See
\code{\link{conditional_effects}} for
more details and documentation of the related plotting function.
}
\description{
Display smooth \code{s} and \code{t2} terms of models
fitted with \pkg{brms}.
}
\details{
Two-dimensional smooth terms will be visualized using
  either contour or raster plots.
}
\examples{
\dontrun{
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
# show all smooth terms
plot(conditional_smooths(fit), rug = TRUE, ask = FALSE)
# show only the smooth term s(x2)
plot(conditional_smooths(fit, smooths = "s(x2)"), ask = FALSE)

# fit and plot a two-dimensional smooth term
fit2 <- brm(y ~ t2(x0, x2), data = dat)
ms <- conditional_smooths(fit2)
plot(ms, stype = "contour")
plot(ms, stype = "raster")
}

}
