as.svrepdesign | R Documentation |

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. With a `svyimputationList`

object, the same
replicate weights will be used for each imputation if the sampling
weights are all the same and `separate.replicates=FALSE`

.

```
as.svrepdesign(design,...)
## Default S3 method:
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"))
## S3 method for class 'svyimputationList'
as.svrepdesign(design, type=c("auto", "JK1", "JKn", "BRR", "bootstrap",
"subbootstrap","mrbbootstrap","Fay"),
fay.rho = 0, fpc=NULL,fpctype=NULL, separate.replicates=FALSE, ..., compress=TRUE,
mse=getOption("survey.replicates.mse"))
```

`design` |
Object of class |

`type` |
Type of replicate weights. |

`fay.rho` |
Tuning parameter for Fay's variance method |

`fpc` , `fpctype` , `...` |
Passed to |

`separate.replicates` |
Compute replicate weights separately for each design (useful for the bootstrap types, which are not deterministic |

`compress` |
Use a compressed representation of the replicate weights matrix. |

`mse` |
if |

Object of class `svyrep.design`

.

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)

`brrweights`

, `svydesign`

,
`svrepdesign`

, `bootweights`

, `subbootweights`

, `mrbweights`

```
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)
```

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