brrweights {survey} | R Documentation

## Compute replicate weights

### Description

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.

### See Also

`hadamard`, `as.svrepdesign`, `svrVar`, `surveyoptions`

### 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)
    svyratio(~alive, ~arrests, scd2brr)

    ## with user-supplied hadamard matrix
    scd2brr1 <- as.svrepdesign(scdnofpc, type="BRR", hadamard.matrix=paley(11))
    svymean(~alive, scd2brr1)
    svyratio(~alive, ~arrests, scd2brr1)

[Package survey version 3.19 Index]