as.svrepdesign: Convert a survey design to use replicate weights

Description Usage Arguments Value References See Also Examples

View source: R/surveyrep.R

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

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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

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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)

Example output

Loading required package: grid
Loading required package: Matrix
Loading required package: survival

Attaching package: 'survey'

The following object is masked from 'package:graphics':

    dotchart

Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, design = scd2brr)
Ratios=
        arrests
alive 0.1535064
SEs=
            [,1]
[1,] 0.009418401
Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, design = scd2brr1)
Ratios=
        arrests
alive 0.1535064
SEs=
           [,1]
[1,] 0.01001468
Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, design = scd2fay)
Ratios=
        arrests
alive 0.1535064
SEs=
            [,1]
[1,] 0.009525187
Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, design = scd2jkn)
Ratios=
        arrests
alive 0.1535064
SEs=
            [,1]
[1,] 0.009846457
Ratio estimator: svyratio.svyrep.design(~alive, ~arrests, design = scd2jknf)
Ratios=
        arrests
alive 0.1535064
SEs=
            [,1]
[1,] 0.007627033
        mean     SE
api00 644.17 23.542
         total     SE
enroll 3404940 932235
        mean     SE
api00 644.17 26.329
         total     SE
enroll 3404940 932235
        mean     SE
api00 644.17 23.155
         total     SE
enroll 3404940 949160
            total        SE
api00  3440375.75 926665.59
stypeE    3493.56   1119.75
stypeH     688.87    289.35
stypeM     946.25    311.81
            total        SE
api00  3440375.75 916874.45
stypeE    3493.56   1106.50
stypeH     688.87    307.00
stypeM     946.25    312.15

survey documentation built on Oct. 11, 2018, 3 p.m.