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.
## 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 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 |
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 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.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.