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\name{geom_smooth}
\alias{geom_smooth}
\title{Add a smoothed conditional mean.}
\usage{
geom_smooth(mapping = NULL, data = NULL, stat = "smooth",
position = "identity", ...)
}
\arguments{
\item{mapping}{The aesthetic mapping, usually constructed
with \code{\link{aes}} or \code{\link{aes_string}}. Only
needs to be set at the layer level if you are overriding
the plot defaults.}
\item{data}{A layer specific dataset - only needed if you
want to override the plot defaults.}
\item{stat}{The statistical transformation to use on the
data for this layer.}
\item{position}{The position adjustment to use for
overlappling points on this layer}
\item{...}{other arguments passed on to
\code{\link{layer}}. This can include aesthetics whose
values you want to set, not map. See \code{\link{layer}}
for more details.}
}
\description{
Add a smoothed conditional mean.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom",
"smooth")}
}
\examples{
# See stat_smooth for examples of using built in model fitting
# if you need some more flexible, this example shows you how to
# plot the fits from any model of your choosing
qplot(wt, mpg, data=mtcars, colour=factor(cyl))
model <- lm(mpg ~ wt + factor(cyl), data=mtcars)
grid <- with(mtcars, expand.grid(
wt = seq(min(wt), max(wt), length = 20),
cyl = levels(factor(cyl))
))
grid$mpg <- stats::predict(model, newdata=grid)
qplot(wt, mpg, data=mtcars, colour=factor(cyl)) + geom_line(data=grid)
# or with standard errors
err <- stats::predict(model, newdata=grid, se = TRUE)
grid$ucl <- err$fit + 1.96 * err$se.fit
grid$lcl <- err$fit - 1.96 * err$se.fit
qplot(wt, mpg, data=mtcars, colour=factor(cyl)) +
geom_smooth(aes(ymin = lcl, ymax = ucl), data=grid, stat="identity")
}
\seealso{
The default stat for this geom is
\code{\link{stat_smooth}} see that documentation for more
options to control the underlying statistical
transformation.
}
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