Compute replicate weights from a survey design. These functions are
usually called from
as.svrepdesign rather than directly
by the user.
brrweights(strata, psu, match = NULL, small = c("fail","split","merge"), large = c("split", "merge", "fail"), fay.rho=0, only.weights=FALSE, compress=TRUE, hadamard.matrix=NULL) jk1weights(psu,fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE) jknweights(strata,psu, fpc=NULL, fpctype=c("population","fraction","correction"), compress=TRUE, lonely.psu=getOption("survey.lonely.psu"))
PSU (cluster) identifier
Optional variable to use in matching.
How to handle strata with only one PSU
How to handle strata with more than two PSUs
Optional population (stratum) size or finite population correction
fpc is coded.
Parameter for Fay's extended BRR method
TRUE return only the matrix of
TRUE, store the replicate weights in
Optional user-supplied Hadamard matrix for
Handling of non-certainty single-PSU strata
JK1 and JKn are jackknife schemes for unstratified and stratified
designs respectively. The finite population correction may be
specified as a single number, a vector with one entry per stratum, or
a vector with one entry per observation (constant within strata).
fpc is a vector with one entry per stratum it may not have
names that differ from the stratum identifiers (it may have no names,
in which case it must be in the same order as
unique(strata)). To specify population stratum sizes use
fpctype="population", to specify sampling fractions use
fpctype="fraction" and to specify the correction directly use
The only reason not to use
compress=TRUE is that it is new and
there is a greater possibility of bugs. It reduces the number of
rows of the replicate weights matrix from the number of observations
to the number of PSUs.
In BRR variance estimation each stratum is split in two to give half-samples. Balanced replicated weights are needed, where observations in two different strata end up in the same half stratum as often as in different half-strata.BRR, strictly speaking, is defined only when each stratum has exactly two PSUs. A stratum with one PSU can be merged with another such stratum, or can be split to appear in both half samples with half weight. The latter approach is appropriate for a PSU that was deterministically sampled.
A stratum with more than two PSUs can be split into multiple smaller strata each with two PSUs or the PSUs can be merged to give two superclusters within the stratum.
When merging small strata or grouping PSUs in large strata the
match variable is used to sort PSUs before merging, to give
approximate matching on this variable.
If you want more control than this you should probably construct your
own weights using the Hadamard matrices produced by
only.weights=FALSE a list with elements
two-column matrix indicating the weight for each half-stratum in one particular set of split samples
New stratum variable incorporating merged or split strata
Original strata for distinct PSUs
Dimension of Hadamard matrix used in BRR construction
function returning replicate weights
Indicates whether the
sampler returns per PSU
or per observation weights
jknweights a data frame of replicate
weights and the
rscale arguments to
Levy and Lemeshow "Sampling of Populations". Wiley.
Shao and Tu "The Jackknife and Bootstrap". Springer.
data(scd) scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA, nest=TRUE) ## convert to BRR replicate weights scd2brr <- as.svrepdesign(scdnofpc, type="BRR") svymean(~alive, scd2brr) #> mean SE #> alive 46.333 3.5824 svyratio(~alive, ~arrests, scd2brr) #> Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, scd2brr) #> Ratios= #> arrests #> alive 0.1535064 #> SEs= #> [,1] #> [1,] 0.009418401 ## with user-supplied hadamard matrix scd2brr1 <- as.svrepdesign(scdnofpc, type="BRR", hadamard.matrix=paley(11)) svymean(~alive, scd2brr1) #> mean SE #> alive 46.333 3.5824 svyratio(~alive, ~arrests, scd2brr1) #> Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, scd2brr1) #> Ratios= #> arrests #> alive 0.1535064 #> SEs= #> [,1] #> [1,] 0.01001468