bcajack2 {bcaboot}  R Documentation 
This function is a version of bcajack
that allows
all the recomputations of the original statistic function
f
to be carried out separately. This is an advantage
if f
is timeconsuming, in which case the B
replications for the nonparametric bca calculations might need
to be done on a distributed basis.
To use bcajack2
in this mode, we first compute a list Blist
via
Blist < list(Y = Y, tt = tt, t0 = t0)
. Here tt
is a vector of
length B
having ith entry tt[i] < func(x[Ii,], ...)
, where x
is the n \times p
data matrix and Ii
is a bootstrap vector
of (observation) indices. Y
is a B
by n
count matrix,
whose ith row is the counts corresponding to Ii
. For example if
n = 5 and Ii = (2, 5, 2, 1, 4)
, then Yi = (1, 2, 0, 1, 1)
. Having computed Blist
, bcajack2
is invoked as
bcajack2(Blist)
without need to enter the function func
.
bcajack2(
x,
B,
func,
...,
m = nrow(x),
mr,
pct = 0.333,
K = 2,
J = 12,
alpha = c(0.025, 0.05, 0.1, 0.16),
verbose = TRUE
)
x 
an 
B 
number of bootstrap replications. 
func 
function 
... 
additional arguments for 
m 
an integer less than or equal to 
mr 
if 
pct 

K 
a nonnegative integer. If 
J 
the number of groups into which the bootstrap replications are split 
alpha 
percentiles desired for the bca confidence limits. One
only needs to provide 
verbose 
logical for verbose progress messages 
a named list of several items
lims : first column shows the estimated bca confidence limits
at the requested alpha percentiles. These can be compared with
the standard limits \hat{\theta} +
\hat{\sigma}z_{\alpha}
, third column. The second column
jacksd
gives the internal standard errors for the bca limits,
quite small in the example. Column 4, pct
, gives the
percentiles of the ordered B bootstrap replications
corresponding to the bca limits, eg the 897th largest
replication equalling the .975 bca limit .557.
stats : top line of stats shows 5 estimates: theta is
func(x)
, original point estimate of the parameter of
interest; sdboot
is its bootstrap estimate of standard error;
z0
is the bca bias correction value, in this case quite
negative; a
is the acceleration, a component of the bca
limits (nearly zero here); sdjack
is the jackknife estimate
of standard error for theta. Bottom line gives the internal
standard errors for the five quantities above. This is
substantial for z0
above.
B.mean : bootstrap sample size B, and the mean of the B
bootstrap replications \hat{\theta^*}
ustats : The biascorrected estimator 2 * t0  mean(tt)
,
and an estimate sdu
of its sampling error
seed : The random number state for reproducibility
data(diabetes, package = "bcaboot")
Xy < cbind(diabetes$x, diabetes$y)
rfun < function(Xy) {
y < Xy[, 11]
X < Xy[, 1:10]
summary(lm(y~X) )$adj.r.squared
}
set.seed(1234)
bcajack2(x = Xy, B = 1000, func = rfun, m = 40, verbose = FALSE)