survey (version 3.1)

svrepdesign: Specify survey design with replicate weights

Description

Some recent large-scale surveys specify replication weights rather than the sampling design (partly for privacy reasons). This function specifies the data structure for such a survey.

Usage

svrepdesign(variables , repweights , weights, data,...)
## S3 method for class 'default':
svrepdesign(variables = NULL, repweights = NULL, weights = NULL, data =
NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap","other"),
combined.weights=FALSE, rho = NULL, bootstrap.average=NULL,
scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"),...)
## S3 method for class 'imputationList':
svrepdesign(variables=NULL, repweights,weights,data,...)
## S3 method for class 'svyrep.design':
image(x, ..., col=grey(seq(.5,1,length=30)), type.=c("rep","total"))

Arguments

variables

formula or data frame specifying variables to include in the design (default is all)

repweights

formula or data frame specifying replication weights

weights

sampling weights

data

data frame to look up variables in formulas

type

Type of replication weights

combined.weights

TRUE if the repweights already include the sampling weights

rho

Shrinkage factor for weights in Fay's method

bootstrap.average

For type="bootstrap", if the bootstrap weights have been averaged, gives the number of iterations averaged over

scale, rscales

Scaling constant for variance, see Details below

fpc,fpctype

Finite population correction information

x

survey design with replicate weights

...

Other arguments to image

col

Colors

type.

"rep" for only the replicate weights, "total" for the replicate and sampling weights combined.

Value

• Object of class svyrep.design, with methods for print, summary, weights, image.

Details

In the BRR method, the dataset is split into halves, and the difference between halves is used to estimate the variance. In Fay's method, rather than removing observations from half the sample they are given weight rho in one half-sample and 2-rho in the other. The ideal BRR analysis is restricted to a design where each stratum has two PSUs, however, it has been used in a much wider class of surveys. The JK1 and JKn types are both jackknife estimators deleting one cluster at a time. JKn is designed for stratified and JK1 for unstratified designs.

Averaged bootstrap weights ("mean bootstrap") are used for some surveys from Statistics Canada. Yee et al (1999) describe their construction and use for one such survey. The variance is computed as the sum of squared deviations of the replicates from their mean. This may be rescaled: scale is an overall multiplier and rscale is a vector of replicate-specific multipliers for the squared deviations. If the replication weights incorporate the sampling weights (combined.weights=TRUE) or for type="other" these must be specified, otherwise they can be guessed from the weights.

A finite population correction may be specified for type="other", type="JK1" and type="JKn". fpc must be a vector with one entry for each replicate. To specify sampling fractions use fpctype="fraction" and to specify the correction directly use fpctype="correction"

To generate your own replicate weights either use as.svrepdesign on a survey.design object, or see brrweights, bootweights, jk1weights and jknweights The model.frame method extracts the observed data.

References

Levy and Lemeshow. "Sampling of Populations". Wiley.

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

Yee et al (1999). Bootstrat Variance Estimation for the National Population Health Survey. Proceedings of the ASA Survey Research Methodology Section. http://www.amstat.org/Sections/Srms/Proceedings/papers/1999_136.pdf

See Also

as.svrepdesign, svydesign, brrweights, bootweights

Examples

data(scd)
# use BRR replicate weights from Levy and Lemeshow
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights)
svyratio(~alive, ~arrests, scdrep)