# svrepdesign

##### 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.

##### See Also

##### 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*