stat_smooth.Rd

% Generated by roxygen2 (4.0.0): do not edit by hand
\name{stat_smooth}
\alias{stat_smooth}
\title{Add a smoother.}
\usage{
stat_smooth(mapping = NULL, data = NULL, geom = "smooth",
  position = "identity", method = "auto", formula = y ~ x, se = TRUE,
  n = 80, fullrange = FALSE, level = 0.95, na.rm = FALSE, ...)
}
\arguments{
\item{method}{smoothing method (function) to use, eg. lm, glm, gam, loess,
rlm. For datasets with n < 1000 default is \code{\link{loess}}. For datasets
with 1000 or more observations defaults to gam, see \code{\link[mgcv]{gam}}
for more details.}

\item{formula}{formula to use in smoothing function, eg. \code{y ~ x},
\code{y ~ poly(x, 2)}, \code{y ~ log(x)}}

\item{se}{display confidence interval around smooth? (TRUE by default, see
level to control}

\item{fullrange}{should the fit span the full range of the plot, or just
the data}

\item{level}{level of confidence interval to use (0.95 by default)}

\item{n}{number of points to evaluate smoother at}

\item{na.rm}{If \code{FALSE} (the default), removes missing values with
a warning.  If \code{TRUE} silently removes missing values.}

\item{...}{other arguments are passed to smoothing function}

\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{geom}{The geometric object to use display the data}

\item{position}{The position adjustment to use for overlappling points
on this layer}
}
\value{
a data.frame with additional columns
  \item{y}{predicted value}
  \item{ymin}{lower pointwise confidence interval around the mean}
  \item{ymax}{upper pointwise confidence interval around the mean}
  \item{se}{standard error}
}
\description{
Aids the eye in seeing patterns in the presence of overplotting.
}
\details{
Calculation is performed by the (currently undocumented)
\code{predictdf} generic function and its methods.  For most methods
the confidence bounds are computed using the \code{\link{predict}}
method - the exceptions are \code{loess} which uses a t-based
approximation, and for \code{glm} where the normal confidence interval
is constructed on the link scale, and then back-transformed to the response
scale.
}
\section{Aesthetics}{

\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "smooth")}
}
\examples{
\donttest{
c <- ggplot(mtcars, aes(qsec, wt))
c + stat_smooth()
c + stat_smooth() + geom_point()

# Adjust parameters
c + stat_smooth(se = FALSE) + geom_point()

c + stat_smooth(span = 0.9) + geom_point()
c + stat_smooth(level = 0.99) + geom_point()
c + stat_smooth(method = "lm") + geom_point()

library(splines)
library(MASS)
c + stat_smooth(method = "lm", formula = y ~ ns(x,3)) +
  geom_point()
c + stat_smooth(method = rlm, formula= y ~ ns(x,3)) + geom_point()

# The default confidence band uses a transparent colour.
# This currently only works on a limited number of graphics devices
# (including Quartz, PDF, and Cairo) so you may need to set the
# fill colour to a opaque colour, as shown below
c + stat_smooth(fill = "grey50", size = 2, alpha = 1)
c + stat_smooth(fill = "blue", size = 2, alpha = 1)

# The colour of the line can be controlled with the colour aesthetic
c + stat_smooth(fill="blue", colour="darkblue", size=2)
c + stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2)
c + geom_point() +
  stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2)

# Smoothers for subsets
c <- ggplot(mtcars, aes(y=wt, x=mpg)) + facet_grid(. ~ cyl)
c + stat_smooth(method=lm) + geom_point()
c + stat_smooth(method=lm, fullrange = TRUE) + geom_point()

# Geoms and stats are automatically split by aesthetics that are factors
c <- ggplot(mtcars, aes(y=wt, x=mpg, colour=factor(cyl)))
c + stat_smooth(method=lm) + geom_point()
c + stat_smooth(method=lm, aes(fill = factor(cyl))) + geom_point()
c + stat_smooth(method=lm, fullrange=TRUE, alpha = 0.1) + geom_point()

# Use qplot instead
qplot(qsec, wt, data=mtcars, geom=c("smooth", "point"))

# Example with logistic regression
data("kyphosis", package="rpart")
qplot(Age, Kyphosis, data=kyphosis)
qplot(Age, data=kyphosis, facets = . ~ Kyphosis, binwidth = 10)
qplot(Age, Kyphosis, data=kyphosis, position="jitter")
qplot(Age, Kyphosis, data=kyphosis, position=position_jitter(height=0.1))

qplot(Age, as.numeric(Kyphosis) - 1, data = kyphosis) +
  stat_smooth(method="glm", family="binomial")
qplot(Age, as.numeric(Kyphosis) - 1, data=kyphosis) +
  stat_smooth(method="glm", family="binomial", formula = y ~ ns(x, 2))
}
}
\seealso{
\code{\link{lm}} for linear smooths,
  \code{\link{glm}} for generalised linear smooths,
  \code{\link{loess}} for local smooths
}