# plot.boot

##### Plots of the Output of a Bootstrap Simulation

This takes a bootstrap object and produces plots for the bootstrap replicates of the variable of interest.

- Keywords
- hplot, nonparametric

##### Usage

```
# S3 method for boot
plot(x, index = 1, t0 = NULL, t = NULL, jack = FALSE,
qdist = "norm", nclass = NULL, df, …)
```

##### Arguments

- 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.

##### 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 `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.

##### Value

`boot.out`

is returned invisibly.

##### 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.

##### See Also

##### Examples

```
# NOT RUN {
# 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)
# }
```

*Documentation reproduced from package boot, version 1.3-25, License: Unlimited*