plot.boot(boot.out, index=1, t0=NULL, t=NULL, jack=F, qdist="norm", nclass=NULL, df, ...)
boot.out
|
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.
|
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.boot
, boot.object
, 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) data(aircondit) air.boot <- boot(aircondit$hours, mean, R=999, sim="parametric", ran.gen=air.rg, mle=mean(aircondit$hours)) plot(air.boot) # In the difference of means example for the last two series of the # gravity data data(gravity) grav1 <- gravity[as.numeric(gravity[,2])>=7,] grav.fun <- function(dat, w) { strata <- tapply(dat[, 2], as.numeric(dat[, 2])) d <- dat[, 1] ns <- tabulate(strata) w <- w/tapply(w, strata, sum)[strata] mns <- tapply(d * w, strata, sum) mn2 <- tapply(d * d * w, strata, sum) s2hat <- sum((mn2 - mns^2)/ns) c(mns[2]-mns[1],s2hat) } grav.boot <- boot(grav1, grav.fun, R=499, stype="w", strata=grav1[,2]) plot(grav.boot) # now suppose we want to look at the studentized differences. grav.z <- (grav.boot$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,]) } data(frets) frets.boot <- boot(log(as.matrix(frets)), frets.fun, R=999) plot(frets.boot,index=7,jack=T,stinf=F,useJ=F)