control(boot.out, L = NULL, distn = NULL, index = 1, t0 = NULL,
t = NULL, bias.adj = FALSE, alpha = NULL, ...)
boot
. The bootstrap
replicates must have been generated using the usual nonparametric
bootstrap.L
is not supplied then empinf
is called to calculate
them from boot.out
.smooth.spline
giving
the distribution function of the linear approximation. This is used
only if bias.adj
is FALSE
. Normally this would be
found using a saddlepoiboot.out$statistic
.boot.out$data
. This argument is used only if
bias.adj
is FALSE
. The input value is ignored if
t
is not also supplied.bias.adj
is FALSE
. The input
is ignored if t0
is not supplied also. The default value is
boot.out$t[,ind
TRUE
specifies that the adjusted
bias estimate using post-simulation balance is all that is required.
If bias.adj
is FALSE
(default) then the linear
approximation to the statisbias.adj
is
FALSE
.boot.out$statistic
requires.
These are passed unchanged every time boot.out$statistic
is
called. boot.out$statistic
is called once if bias.adj
is TRUE
bias.adj
is TRUE
then the returned value is the
adjusted bias estimate. If bias.adj
is FALSE
then the returned value is a list
with the following components
empinf
.t
of
the statistic of interest.t
as a control variate.t
as a control variate.t
as a control variate.t
as a control variate.smooth.spline
describing the
saddlepoint approximation to the bootstrap distribution of the
linear approximation to t
. If distn
was supplied on
input then this is the same as the input otherwise it is calculated
by a call to saddle.distn
.bias.adj
is FALSE
then the linear approximation to
the statistic is found and evaluated at each bootstrap replicate.
Then using the equation T* = Tl*+(T*-Tl*), moment estimates can
be found. For quantile estimation the distribution of the linear
approximation to t
is approximated very accurately by
saddlepoint methods, this is then combined with the bootstrap
replicates to approximate the bootstrap distribution of t
and
hence to estimate the bootstrap quantiles of t
.Efron, B. (1990) More efficient bootstrap computations. Journal of the American Statistical Association, 55, 79--89.
boot
, empinf
, k3.linear
, linear.approx
, saddle.distn
, smooth.spline
, var.linear
# Use of control variates for the variance of the air-conditioning data
mean.fun <- function(d, i)
{ m <- mean(d$hours[i])
n <- nrow(d)
v <- (n-1)*var(d$hours[i])/n^2
c(m, v)
}
air.boot <- boot(aircondit, mean.fun, R = 999)
control(air.boot, index = 2, bias.adj = TRUE)
air.cont <- control(air.boot, index = 2)
# Now let us try the variance on the log scale.
air.cont1 <- control(air.boot, t0 = log(air.boot$t0[2]),
t = log(air.boot$t[, 2]))
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