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\name{plot.boot}
\alias{plot.boot}
\title{
Plots of the Output of a Bootstrap Simulation
}
\description{
This takes a bootstrap object and produces plots for the bootstrap
replicates of the variable of interest.
}
\usage{
\method{plot}{boot}(x, index=1, t0=NULL, t=NULL, jack=FALSE, qdist="norm",
nclass=NULL, df, \dots)
}
\arguments{
\item{x}{
An object of class \code{"boot"} returned from one of the bootstrap generation
functions.
}
\item{index}{
The index of the variable of interest within the output of \code{boot.out}. This
is ignored if \code{t} and \code{t0} are supplied.
}
\item{t0}{
The original value of the statistic. This defaults to \code{boot.out$t0[index]}
unless \code{t} is supplied when it defaults to \code{NULL}. In that case no vertical
line is drawn on the histogram.
}
\item{t}{
The bootstrap replicates of the statistic. Usually this will take on its
default value of \code{boot.out$t[,index]}, however it may be useful sometimes
to supply a different set of values which are a function of \code{boot.out$t}.
}
\item{jack}{
A logical value indicating whether a jackknife-after-bootstrap plot is
required. The default is not to produce such a plot.
}
\item{qdist}{
The distribution against which the Q-Q plot should be drawn. At present
\code{"norm"} (normal distribution - the default) and \code{"chisq"} (chi-squared
distribution) are the only possible values.
}
\item{nclass}{
An integer giving the number of classes to be used in the bootstrap histogram.
The default is the integer between 10 and 100 closest to
\code{ceiling(length(t)/25)}.
}
\item{df}{
If \code{qdist} is \code{"chisq"} then this is the degrees of freedom for the chi-squared
distribution to be used. It is a required argument in that case.
}
\item{...}{
When \code{jack} is \code{TRUE} additional parameters to \code{jack.after.boot} can be
supplied. See the help file for \code{jack.after.boot} for details of the
possible parameters.
}}
\value{
\code{boot.out} is returned invisibly.
}
\section{Side Effects}{
All screens are closed and cleared and a number of plots are produced on the
current graphics device. Screens are closed but not cleared at termination
of this function.
}
\details{
This function will generally produce two side-by-side plots. The left plot
will be a histogram of the bootstrap replicates. Usually the breaks of the
histogram will be chosen so that \code{t0} is at a breakpoint and all intervals
are of equal length. A vertical dotted line indicates the position of \code{t0}.
This cannot be done if \code{t} is supplied but \code{t0} is not and so, in that case,
the breakpoints are computed by \code{hist} using the \code{nclass} argument and no
vertical line is drawn.
The second plot is a Q-Q plot of the bootstrap replicates. The order
statistics
of the replicates can be plotted against normal or chi-squared quantiles. In
either case the expected line is also plotted. For the normal, this will
have intercept \code{mean(t)} and slope \code{sqrt(var(t))} while for the chi-squared
it has intercept 0 and slope 1.
If \code{jack} is \code{TRUE} a third plot is produced beneath these two. That plot
is the jackknife-after-bootstrap plot. This plot may only be requested
when nonparametric simulation has been used. See \code{jack.after.boot} for further
details of this plot.
}
\seealso{
\code{\link{boot}}, \code{\link{jack.after.boot}}, \code{\link{print.boot}}
}
\examples{
# We fit an exponential model to the air-conditioning data and use
# that for a parametric bootstrap. Then we look at plots of the
# resampled means.
air.rg <- function(data, mle)
rexp(length(data), 1/mle)
air.boot <- boot(aircondit$hours, mean, R=999, sim="parametric",
ran.gen=air.rg, mle=mean(aircondit$hours))
plot(air.boot)
# In the difference of means example for the last two series of the
# gravity data
grav1 <- gravity[as.numeric(gravity[,2])>=7,]
grav.fun <- function(dat, w)
{ strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
d <- dat[, 1]
ns <- tabulate(strata)
w <- w/tapply(w, strata, sum)[strata]
mns <- tapply(d * w, strata, sum)
mn2 <- tapply(d * d * w, strata, sum)
s2hat <- sum((mn2 - mns^2)/ns)
c(mns[2]-mns[1],s2hat)
}
grav.boot <- boot(grav1, grav.fun, R=499, stype="w", strata=grav1[,2])
plot(grav.boot)
# now suppose we want to look at the studentized differences.
grav.z <- (grav.boot$t[,1]-grav.boot$t0[1])/sqrt(grav.boot$t[,2])
plot(grav.boot,t=grav.z,t0=0)
# In this example we look at the one of the partial correlations for the
# head dimensions in the dataset frets.
pcorr <- function( x )
{
# Function to find the correlations and partial correlations between
# the four measurements.
v <- cor(x);
v.d <- diag(var(x));
iv <- solve(v);
iv.d <- sqrt(diag(iv));
iv <- - diag(1/iv.d) \%*\% iv \%*\% diag(1/iv.d);
q <- NULL;
n <- nrow(v);
for (i in 1:(n-1))
q <- rbind( q, c(v[i,1:i],iv[i,(i+1):n]) );
q <- rbind( q, v[n,] );
diag(q) <- round(diag(q));
q
}
frets.fun <- function( data, i )
{ d <- data[i,];
v <- pcorr( d );
c(v[1,],v[2,],v[3,],v[4,])
}
frets.boot <- boot(log(as.matrix(frets)), frets.fun, R=999)
plot(frets.boot, index=7, jack=TRUE, stinf=FALSE, useJ=FALSE)
}
\keyword{hplot}
\keyword{nonparametric}
% Converted by Sd2Rd version 1.15.
|
