ggalt/man/stat_ash.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_ash.r
\name{stat_ash}
\alias{stat_ash}
\title{Compute and display a univariate averaged shifted histogram (polynomial kernel)}
\usage{
stat_ash(mapping = NULL, data = NULL, geom = "area", position = "stack",
  ab = NULL, nbin = 50, m = 5, kopt = c(2, 2), na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, ...)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link{aes}} or
\code{\link{aes_}}. If specified and \code{inherit.aes = TRUE} (the
default), is combined with the default mapping at the top level of the
plot. You only need to supply \code{mapping} if there isn't a mapping
defined for the plot.}

\item{data}{A data frame. If specified, overrides the default data frame
defined at the top level of the plot.}

\item{geom}{Use to override the default Geom}

\item{position}{Position adjustment, either as a string, or the result of
a call to a position adjustment function.}

\item{ab}{half-open interval for bins \emph{[a,b)}. If no value is specified,
the range of x is stretched by \code{5\%} at each end and used the
interval.}

\item{nbin}{number of bins desired. Default \code{50}.}

\item{m}{integer smoothing parameter; Default \code{5}.}

\item{kopt}{vector of length 2 specifying the kernel, which is proportional
to \emph{( 1 - abs(i/m)^kopt(1) )i^kopt(2)}; (2,2)=biweight (default);
 (0,0)=uniform; (1,0)=triangle; (2,1)=Epanechnikov; (2,3)=triweight.}

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

\item{show.legend}{logical. Should this layer be included in the legends?
\code{NA}, the default, includes if any aesthetics are mapped.
\code{FALSE} never includes, and \code{TRUE} always includes.}

\item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. \code{\link{borders}}.}

\item{...}{other arguments passed on to \code{\link{layer}}. There are
  three types of arguments you can use here:

  \itemize{
  \item Aesthetics: to set an aesthetic to a fixed value, like
     \code{color = "red"} or \code{size = 3}.
  \item Other arguments to the layer, for example you override the
    default \code{stat} associated with the layer.
  \item Other arguments passed on to the stat.
  }}
}
\description{
See \code{\link[ash]{bin1}} & \code{\link[ash]{ash1}} for more information.
}
\details{
\if{html}{
A sample of the output from \code{stat_ash()}:

\figure{stat_ash_01.png}{options: width="100\%" alt="Figure: stat_ash_01.png"}
}

\if{latex}{
A sample of the output from \code{stat_ash()}:

\figure{statash01.pdf}{options: width=10cm}
}
}
\section{Aesthetics}{

\code{geom_ash} understands the following aesthetics (required aesthetics
are in bold):
\itemize{
  \item \strong{\code{x}}
  \item \code{alpha}
  \item \code{color}
  \item \code{fill}
  \item \code{linetype}
  \item \code{size}
}
}

\section{Computed variables}{

\describe{
  \item{\code{density}}{ash density estimate}
}
}
\examples{
# compare
library(gridExtra)
set.seed(1492)
dat <- data.frame(x=rnorm(100))
grid.arrange(ggplot(dat, aes(x)) + stat_ash(),
             ggplot(dat, aes(x)) + stat_bkde(),
             ggplot(dat, aes(x)) + stat_density(),
             nrow=3)

cols <- RColorBrewer::brewer.pal(3, "Dark2")
ggplot(dat, aes(x)) +
  stat_ash(alpha=1/2, fill=cols[3]) +
  stat_bkde(alpha=1/2, fill=cols[2]) +
  stat_density(alpha=1/2, fill=cols[1]) +
  geom_rug() +
  labs(x=NULL, y="density/estimate") +
  scale_x_continuous(expand=c(0,0)) +
  theme_bw() +
  theme(panel.grid=element_blank()) +
  theme(panel.border=element_blank())
}
\references{
David Scott (1992), \emph{"Multivariate Density Estimation,"}
            John Wiley, (chapter 5 in particular).\cr
            \cr
            B. W. Silverman (1986), \emph{"Density Estimation for Statistics
            and Data Analysis,"} Chapman & Hall.
}