Description Usage Arguments Value Examples
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 time-consuming, 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 i-th 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 i-th 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.
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x |
an n \times p data matrix, rows are observed
p-vectors, assumed to be independently sampled from
target population. If p is 1 then |
B |
number of bootstrap replications. |
func |
function \hat{θ}=func(x) computing estimate of the parameter of interest; func(x) should return a real value for any n^\prime \times p matrix x^\prime, n^\prime not necessarily equal to n |
... |
additional arguments for |
m |
an integer less than or equal to n; the routine
collects the n rows of |
mr |
if m < n then |
pct |
|
K |
a non-negative 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{θ} +
\hat{σ}z_{α}, 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{θ^*}
ustats : The bias-corrected estimator 2 * t0 - mean(tt)
,
and an estimate sdu
of its sampling error
seed : The random number state for reproducibility
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