R: Convert a survey design to use replicate weights

as.svrepdesign {survey} | R Documentation

## Convert a survey design to use replicate weights

### Description

Creates a replicate-weights survey design object from a traditional strata/cluster survey design object. `JK1` and `JKn` are jackknife methods, `BRR` is Balanced Repeated Replicates and `Fay` is Fay's modification of this, `bootstrap` is Canty and Davison's bootstrap, `subbootstrap` is Rao and Wu's (n-1) bootstrap, and `mrbbootstrap` is Preston's multistage rescaled bootstrap.

### Usage

    as.svrepdesign(design, type=c("auto", "JK1", "JKn", "BRR", "bootstrap",
       "subbootstrap","mrbbootstrap","Fay"),
       fay.rho = 0, fpc=NULL,fpctype=NULL,..., compress=TRUE,
       mse=getOption("survey.replicates.mse"))

### Arguments

`design` | Object of class `survey.design`
`type` | Type of replicate weights. `"auto"` uses JKn for stratified, JK1 for unstratified designs
`fay.rho` | Tuning parameter for Fay's variance method
`fpc,fpctype,...` | Passed to `jk1weights`, `jknweights`, `brrweights`, `bootweights`, `subbootweights`, or `mrbweights`.
`compress` | Use a compressed representation of the replicate weights matrix.
`mse` | if `TRUE`, compute variances from sums of squares around the point estimate, rather than the mean of the replicates

### Value

Object of class `svyrep.design`.

### References

Canty AJ, Davison AC. (1999) Resampling-based variance estimation for labour force surveys. The Statistician 48:379-391

Judkins, D. (1990), "Fay's Method for Variance Estimation," Journal of Official Statistics, 6, 223-239.

Preston J. (2009) Rescaled bootstrap for stratified multistage sampling. Survey Methodology 35(2) 227-234

Rao JNK, Wu CFJ. Bootstrap inference for sample surveys. Proc Section on Survey Research Methodology. 1993 (866–871)

### See Also

`brrweights`, `svydesign`, `svrepdesign`, `bootweights`, `subbootweights`, `mrbweights`

### Examples

    data(scd)
    scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
    nest=TRUE, fpc=rep(5,6))
    scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
    nest=TRUE)

    # convert to BRR replicate weights
    scd2brr <- as.svrepdesign(scdnofpc, type="BRR")
    scd2fay <- as.svrepdesign(scdnofpc, type="Fay",fay.rho=0.3)
    # convert to JKn weights
    scd2jkn <- as.svrepdesign(scdnofpc, type="JKn")

    # convert to JKn weights with finite population correction
    scd2jknf <- as.svrepdesign(scddes, type="JKn")

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

    svyratio(~alive, ~arrests, design=scd2brr)
    svyratio(~alive, ~arrests, design=scd2brr1)
    svyratio(~alive, ~arrests, design=scd2fay)
    svyratio(~alive, ~arrests, design=scd2jkn)
    svyratio(~alive, ~arrests, design=scd2jknf)

    data(api)
    ## one-stage cluster sample
    dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
    ## convert to JK1 jackknife
    rclus1<-as.svrepdesign(dclus1)
    ## convert to bootstrap
    bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100)

    svymean(~api00, dclus1)
    svytotal(~enroll, dclus1)

    svymean(~api00, rclus1)
    svytotal(~enroll, rclus1)

    svymean(~api00, bclus1)
    svytotal(~enroll, bclus1)

    dclus2<-svydesign(id = ~dnum + snum, fpc = ~fpc1 + fpc2, data = apiclus2)
    mrbclus2<-as.svrepdesign(dclus2, type="mrb",replicates=100)
    svytotal(~api00+stype, dclus2)
    svytotal(~api00+stype, mrbclus2)

[Package survey version 3.32-1 Index]