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 jackknifeafterbootstrap plot is required. The default is not to produce such a plot. 
qdist 
The distribution against which the QQ plot should be drawn. At present
"norm" (normal distribution  the default) and "chisq" (chisquared
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 chisquared
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 sidebyside 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 QQ plot of the bootstrap replicates. The order
statistics
of the replicates can be plotted against normal or chisquared 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 chisquared
it has intercept 0 and slope 1.
If jack
is TRUE
a third plot is produced beneath these two. That plot
is the jackknifeafterbootstrap 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 airconditioning 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:(n1)) 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)