# svrepdesign

0th

Percentile

##### Specify survey design with replicate weights

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.

Keywords
survey
##### Usage
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 '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.
##### 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. 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.

##### Value

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

##### Note

To use replication-weight analyses on a survey specified by sampling design, use as.svrepdesign to convert it.

##### References

Levy and Lemeshow. "Sampling of Populations". Wiley. Shao and Tu. "The Jackknife and Bootstrap." Springer.

as.svrepdesign, svydesign, brrweights

##### Aliases
• svrepdesign
• [.svyrep.design
• image.svyrep.design
• print.svyrep.design
• model.frame.svyrep.design
• summary.svyrep.design
• print.summary.svyrep.design
##### 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)
Documentation reproduced from package survey, version 3.9-1, License: GPL-2 | GPL-3

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