Raw File
plot.imp.Rd
\name{plot.imp}
\alias{plot.imp}
\title{ Plot method for objects of class imp }
\description{
  This function provides several diagnostic plots for the imputed data set in order to see 
  how the imputated values are distributed in comparison with the original data values.
}
\usage{
\method{plot}{imp}(x, ..., which = 1, ord = 1:ncol(x), colcomb = "missnonmiss", plotvars = NULL, 
  col = c("skyblue", "red"), alpha = NULL, lty = par("lty"), xaxt = "s", xaxlabels = NULL, 
  las = 3, interactive = TRUE, pch = c(1, 3), smooth = FALSE, reg.line = FALSE, 
  legend.plot = FALSE, ask = prod(par("mfcol")) < length(which) && dev.interactive(), 
  center = FALSE, scale = FALSE, id = FALSE, seg.l = 0.02, seg1 = TRUE)
}
\arguments{
  \item{x}{ object of class \sQuote{imp} }
  \item{\dots}{ other parameters to be passed through to plotting functions. }
  \item{which}{ if a subset of the plots is required, specify a subset of the numbers 1:3. }
  \item{ord}{ determines the ordering of the variables }
  \item{colcomb}{ if colcomb\eqn{=}\dQuote{missnonmiss}, observations with missings in any variable 
    are highlighted. Otherwise, observations with missings in any of the variables 
    specified by colcomb are highlighted in the parallel coordinate plot. }
  \item{plotvars}{ Parameter for the parallel coordinate plot. A vector giving the variables to be plotted. 
                   If NULL (the default), all variables are plotted. }
  \item{col}{ a vector of length two giving the colors to be used in the plot. 
       The second color will be used for highlighting. }
  \item{alpha}{ a numeric value between 0 and 1 giving the level of 
    transparency of the colors, or NULL. This can be used to prevent overplotting. }
  \item{lty}{ a vector of length two giving the line types. 
    The second line type will be used for the highlighted observations. 
    If a single value is supplied, 
    it will be used for both non-highlighted and highlighted observations. }
  \item{xaxt}{ the x-axis type (see \code{\link{par}}). }
  \item{xaxlabels}{ a character vector containing the labels for the x-axis. 
    If NULL, the column names of x will be used. }
  \item{las}{ the style of axis labels (see \code{\link{par}}). }
  \item{interactive}{ a logical indicating whether the variables to be used 
    for highlighting can be selected interactively (see \sQuote{Details}). }
  \item{pch}{ a vector of length two giving the symbol of the plotting points. 
    The symbol will be used for the highlighted observations. 
    If a single value is supplied, 
    it will be used for both non-highlighted and highlighted observations. }
  \item{smooth}{ if TRUE a lowess smooth is plotted in each off-diagonal panel of
    the multiple scatterplot. Further detail can be found in package car.}
  \item{reg.line}{ if not FALSE a line is plotted using the function given by this argument; 
    e.g., using rlm in package MASS plots a robust-regression line within the 
     multiple scatterplot. }
  \item{legend.plot}{ if TRUE then a legend for the groups is plotted in the bottom-right cell
    of the multiple scatterplot. }
  \item{ask}{ logical; if TRUE, the user is asked before each plot, see \code{\link{par}}(ask=.). }
  \item{center}{ logical, indicates if the data should be centered prior plotting the ternary plot.  }
  \item{scale}{ logical, indicates if the data should be centered prior plotting the ternary plot. }
  \item{id}{ reads the position of the graphics pointer when the (first) 
    mouse button is pressed and returns the corresponding index of the observation.
    (only used by the ternary plot) }
  \item{seg.l}{ length of the plotting symbol (spikes) for the ternary plot. }
  \item{seg1}{ if TRUE, the spikes of the plotting symbol are justified. }
}
\details{
  The first plot (which \eqn{== 1}) is a multiple scatterplot where for the imputed values 
  another plot symbol and color is used in order to highlight them.
  
  Plot 2 is a parallel coordinate plot in which imputed values 
  in certain variables are highlighted. 
  In parallel coordinate plots, the variables are represented by parallel axes. 
  Each observation of the scaled data is shown as a line. 
  If interactive is TRUE, the variables to be used for highlighting can be 
  selected interactively. Observations which includes imputed values in any of the 
  selected variables will be highlighted. 
  A variable can be added to the selection by clicking on a coordinate axis. 
  If a variable is already selected, clicking on its coordinate axis will remove 
  it from the selection. Clicking anywhere outside the plot region quits the 
  interactive session. 
  
  Plot 3 shows a ternary diagram in which imputed values are highlighted, i.e. those spikes 
  of the chosen plotting symbol are colored in red for which of the values are missing
  in the unimputed data set. 
}
\value{
None (invisible NULL).
}
\references{ 
Aitchison, J. (1986) \emph{The Statistical Analysis of Compositional
Data} Monographs on Statistics and Applied Probability. Chapman \&
Hall Ltd., London (UK). 416p.

Wegman, E. J. (1990) \emph{Hyperdimensional data analysis using parallel coordinates} 
Journal of the American Statistical Association 85, 664--675.  
}
\author{ Matthias Templ }
\seealso{ \code{\link{impCoda}}, \code{\link{impKNNa}}, \
  \code{\link[car]{scatterplot.matrix}} }
\examples{
data(expenditures)
expenditures[1,3]
expenditures[1,3] <- NA
xi <- impKNNa(expenditures)
xi
summary(xi)
plot(xi, which=1)
plot(xi, which=2)
plot(xi, which=3)
plot(xi, which=3, seg1=FALSE)
}
\keyword{ aplot }
\keyword{ hplot }
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