plot.kde.rd

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\name{plot.kde}
\alias{plot.kde}

\title{Kernel density estimate plot for 1- to 3-dimensional data}
\description{
  Kernel density estimate plot for 1- to 3-dimensional data.
}

\synopsis{\method{plot}{kde}(x, drawpoints=FALSE, ...)}

\usage{

## univariate
\method{plot}{kde}(x, xlab, ylab="Density function", add=FALSE,
  drawpoints=FALSE, ptcol="blue", col="black", jitter=TRUE, ...)

## bivariate
\method{plot}{kde}(x, display="slice", cont=c(25,50,75), abs.cont,
    xlab, ylab, zlab="Density function", add=FALSE, drawpoints=FALSE,
    drawlabels=TRUE, theta=-30, phi=40, d=4,
    ptcol="blue", col="black", ...)

## trivariate
\method{plot}{kde}(x, cont=c(25,50,75), abs.cont, colors,add=FALSE,
    drawpoints=FALSE, alpha, alphavec, xlab, ylab, zlab, size=3,
    ptcol="blue", ...)
}

\arguments{
  \item{x}{an object of class \code{kde} (output from
    \code{\link{kde}} function)}
  \item{display}{type of display, "slice" for contour plot, "persp" for
    perspective plot, "image" for image plot, "filled" for filled
    contyour plot (2-d plot)}
  \item{cont}{vector of percentages for contour
    level curves}
  \item{abs.cont}{vector of absolute density estimate heights for contour
    level curves}
  \item{ptcol}{plotting colour for data points}
  \item{col}{plotting colour for density estimate (1-d, 2-d plot)}
  \item{colors}{vector of colours for each contour (3-d plot)}
  \item{jitter}{if TRUE then jitter rug plot (1-d plot)}
  \item{xlab,ylab,zlab}{axes labels}
  \item{add}{if TRUE then add to current plot}
  \item{theta,phi,d}{graphics parameters for perspective plots (2-d plot)}
  \item{drawpoints}{if TRUE then draw data points on density estimate}
  \item{drawlabels}{if TRUE then draw contour labels (2-d plot)}
  \item{alpha}{transparency value of plotting symbol (3-d plot)}
  \item{alphavec}{vector of transparency values for contours (3-d plot)}
  \item{size}{size of plotting symbol (3-d plot)}
  \item{...}{other graphics parameters}
}
  

\value{
  Plot of 1-d and 2-d kernel density estimates are sent to graphics window. Plot
  for 3-d is generated by the \code{misc3d} and \code{rgl}
  libraries and is sent to RGL window.
}

\references{
  Bowman, A.W. & Azzalini, A. (1997) \emph{Applied Smoothing Techniques
    for Data Analysis}. Clarendon Press. Oxford.
  
  Simonoff, J. S., (1996) \emph{Smoothing Methods in Statistics}.
  Springer-Verlag. New York.
}

\details{
  -- The 1-d plot is a standard plot of a 1-d curve. If
  \code{drawpoints=TRUE} then a rug plot is added.
  
  -- There are three types of plotting displays for 2-d data available,
  controlled by the \code{display} parameter.

  If \code{display="slice"} then a slice/contour plot
  is generated using \code{contour}.
  The default contours are at 25\%, 50\%, 75\% or
  \code{cont=c(25,50,75)} which are upper percentages of
  highest density regions. See below for alternative
  contour levels.
  
  If \code{display="persp"} then a perspective/wire-frame plot
  is generated.  The default z-axis limits \code{zlim} are the default
  from the usual \code{persp} command. 
  
  If \code{display="image"} then an image plot
  is generated. Default colours are the default from the usual
  \code{image} command.

  -- For 3-dimensional data, the interactive plot is a series of nested
  3-d contours. 
  The default contours are \code{cont=c(25,50,75)}. See below for
  alternative  contour levels. The
  default \code{colors} are \code{heat.colors} and the
  default opacity \code{alphavec} ranges from 0.1 to 0.5.

  -- To specify contours, either one of \code{cont} or \code{abs.cont}
  is required. \code{cont} specifies upper percentages which
  correspond to 
  highest density regions. If \code{abs.cont=NULL} then a \code{pretty}
  set of contours is drawn. If \code{abs.cont} is set to particular
  values, then contours at these levels are drawn.
  This third option is useful for plotting
  multiple density estimates with common contour levels. See
  \code{\link{contourLevels}} for details on computing contour levels
  for \code{kde} objects.  
}
 

\seealso{\code{\link{kde}}}

\examples{
## univariate example
x <- rnorm.mixt(n=100, mus=1, sigmas=1, props=1)
fhat <- kde(x=x, h=hpi(x))  
plot(fhat)


## bivariate example
data(unicef)
H.scv <- Hscv(x=unicef)
fhat <- kde(x=unicef, H=H.scv)

plot(fhat)
plot(fhat, drawpoints=TRUE, drawlabels=FALSE, col=3, lwd=2)
plot(fhat, display="persp", border=NA, col="grey96", shade=0.75)
plot(fhat, display="image", col=rev(heat.colors(100)))
plot(fhat, display="filled")

## pair of densities with same absolute contour levels
x <- rmvnorm.mixt(n=100, mus=c(0,0), Sigmas=diag(2), props=1)
Hx <- Hpi(x)
fhatx <- kde(x=x, H=Hx, xmin=c(-4,-4), xmax=c(4,4)) 
y <- rmvnorm.mixt(n=100, mus=c(0.5,0.5), Sigmas=0.5*diag(2), props=1)
Hy <- Hpi(y)
fhaty <- kde(x=y, H=Hy, xmin=c(-4,-4), xmax=c(4,4))
lev <- contourLevels(fhatx, prob=c(0.25, 0.5, 0.75))
plot(fhatx, abs.cont=lev)
plot(fhaty, abs.cont=lev, col=3, add=TRUE) 

## large sample - 10000 sample from bivariate standard normal 
x <- rmvnorm.mixt(10000, c(0,0), diag(2))    
H.pi <- Hpi.diag(x, binned=TRUE)
fhat <- kde(x, H=H.pi, binned=TRUE) 
plot(fhat, drawpoints=FALSE, cont=seq(10,90, by=20))


## trivariate example
mus <- rbind(c(0,0,0), c(-1,1,1))
Sigma <- matrix(c(1, 0.7, 0.7, 0.7, 1, 0.7, 0.7, 0.7, 1), nr=3, nc=3) 
Sigmas <- rbind(Sigma, Sigma)
props <- c(1/2, 1/2)

x <- rmvnorm.mixt(n=100, mus=mus, Sigmas=Sigmas, props=props)
H.pi <- Hpi(x, pilot="samse")
fhat <- kde(x, H=H.pi)  
plot(fhat)
plot(fhat, axes=FALSE, box=FALSE, drawpoints=TRUE); axes3d(c('x','y','z'))
}

\keyword{hplot}