| jack.after.boot {boot} | R Documentation |
This function calculates the jackknife influence values from a bootstrap output object and plots the corresponding jackknife-after-bootstrap plot.
jack.after.boot(boot.out, index = 1, t = NULL, L = NULL,
useJ = TRUE, stinf = TRUE, alpha = NULL,
main = "", ylab = NULL, ...)
boot.out |
An object of class |
index |
The index of the statistic of interest in the output of |
t |
A vector of length |
L |
The empirical influence values for the statistic of interest. These are used
only if |
useJ |
A logical variable indicating if the jackknife influence values calculated from
the bootstrap replicates should be used. If |
stinf |
A logical variable indicating whether to standardize the jackknife values
before plotting them. If |
alpha |
The quantiles at which the plots are required. The default is
|
main |
A character string giving the main title for the plot. |
ylab |
The label for the Y axis. If the default values of |
... |
Any extra arguments required by |
The centred jackknife quantiles for each observation are estimated from those
bootstrap samples in which the particular observation did not appear. These
are then plotted against the influence values. If useJ is TRUE then the
influence values are found in the same way as the difference between the
mean of the statistic in the samples excluding the observations and the mean in
all samples. If useJ is FALSE then empirical influence values are
calculated by calling empinf.
The resulting plots are useful diagnostic tools for looking at the way individual observations affect the bootstrap output.
The plot will consist of a number of horizontal dotted lines which correspond to the quantiles of the centred bootstrap distribution. For each data point the quantiles of the bootstrap distribution calculated by omitting that point are plotted against the (possibly standardized) jackknife values. The observation number is printed below the plots. To make it easier to see the effect of omitting points on quantiles, the plotted quantiles are joined by line segments. These plots provide a useful diagnostic tool in establishing the effect of individual observations on the bootstrap distribution. See the references below for some guidelines on the interpretation of the plots.
There is no returned value but a plot is generated on the current graphics display.
A plot is created on the current graphics device.
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
Efron, B. (1992) Jackknife-after-bootstrap standard errors and influence functions (with Discussion). Journal of the Royal Statistical Society, B, 54, 83–127.
# To draw the jackknife-after-bootstrap plot for the head size data as in
# Example 3.24 of Davison and Hinkley (1997)
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)
# we will concentrate on the partial correlation between head breadth
# for the first son and head length for the second. This is the 7th
# element in the output of frets.fun so we set index = 7
jack.after.boot(frets.boot, useJ = FALSE, stinf = FALSE, index = 7)