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 
index 
The index of the variable of interest within the output of

t0 
The original value of the statistic. This defaults to

t 
The bootstrap replicates of the statistic. Usually this will take
on its default value of 
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 
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

df 
If 
... 
When 
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 < 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:(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 } 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)