| plot.boot {boot} | R Documentation |
This takes a bootstrap object and produces plots for the bootstrap replicates of the variable of interest.
## S3 method for class 'boot':
plot(x, index=1, t0=NULL, t=NULL, jack=FALSE, qdist="norm",
nclass=NULL, df, ...)
x |
An object of class "boot" returned from one of the bootstrap generation
functions.
|
index |
The index of the variable of interest within the output of boot.out. This
is ignored if t and t0 are supplied.
|
t0 |
The original value of the statistic. This defaults to boot.out$t0[index]
unless t is supplied when it defaults to NULL. In that case no vertical
line is drawn on the histogram.
|
t |
The bootstrap replicates of the statistic. Usually this will take on its
default value of boot.out$t[,index], however it may be useful sometimes
to supply a different set of values which are a function of boot.out$t.
|
jack |
A logical value indicating whether a jackknife-after-bootstrap plot is required. The default is not to produce such a plot. |
qdist |
The distribution against which the Q-Q plot should be drawn. At present
"norm" (normal distribution - the default) and "chisq" (chi-squared
distribution) are the only possible values.
|
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
ceiling(length(t)/25).
|
df |
If qdist is "chisq" then this is the degrees of freedom for the chi-squared
distribution to be used. It is a required argument in that case.
|
... |
When jack is TRUE additional parameters to jack.after.boot can be
supplied. See the help file for jack.after.boot for details of the
possible parameters.
|
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 t0 is at a breakpoint and all intervals
are of equal length. A vertical dotted line indicates the position of t0.
This cannot be done if t is supplied but t0 is not and so, in that case,
the breakpoints are computed by hist using the 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 mean(t) and slope sqrt(var(t)) while for the chi-squared
it has intercept 0 and slope 1.
If jack is 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 jack.after.boot for further
details of this plot.
boot.out is returned invisibly.
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.
boot, jack.after.boot, print.boot
# 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)