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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$hours))
plot(air.boot)
# In the difference of means example for the last two series of the
# gravity data
grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ]
grav.fun <- function(dat, w) {
strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
d <- dat[, 1]
ns <- tabulate(strata)
w <- w/tapply(w, strata, sum)[strata]
mns <- as.vector(tapply(d * w, strata, sum)) # drop names
mn2 <- tapply(d * d * w, strata, sum)
s2hat <- sum((mn2 - mns^2)/ns)
c(mns[2] - mns[1], s2hat)
}
grav.boot <- boot(grav1, grav.fun, R = 499, stype = "w", strata = grav1[, 2])
plot(grav.boot)
# now suppose we want to look at the studentized differences.
grav.z <- (grav.boot$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.
frets.fun <- function(data, i) {
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
}
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}
|
