Compute replicate weights

Compute replicate weights from a survey design. These functions are usually called from as.svrepdesign rather than directly by the user.

Usage

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"))

Arguments

strata: Stratum identifiers
psu: PSU (cluster) identifier
match: Optional variable to use in matching.
small: How to handle strata with only one PSU
large: How to handle strata with more than two PSUs
fpc: Optional population (stratum) size or finite population correction
fpctype: How fpc is coded.
fay.rho: Parameter for Fay's extended BRR method
only.weights: If TRUE return only the matrix of replicate weights
compress: If TRUE, store the replicate weights in compressed form
hadamard.matrix: Optional user-supplied Hadamard matrix for brrweights
lonely.psu: Handling of non-certainty single-PSU strata

Details

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). When 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 fpctype="correction"

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 hadamard

Value

For brrweights with only.weights=FALSE a list with elements

weights: two-column matrix indicating the weight for each half-stratum in one particular set of split samples
wstrata: New stratum variable incorporating merged or split strata
strata: Original strata for distinct PSUs
psu: Distinct PSUs
npairs: Dimension of Hadamard matrix used in BRR construction
sampler: function returning replicate weights
compress: Indicates whether the sampler returns per PSU or per observation weights

For jk1weights and jknweights a data frame of replicate weights and the scale and rscale arguments to svrVar.

References

Levy and Lemeshow "Sampling of Populations". Wiley.

Shao and Tu "The Jackknife and Bootstrap". Springer.

Examples

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