R: Specify survey design with replicate weights

svrepdesign {survey} | R Documentation

## 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,...)
    ## Default S3 method:
    svrepdesign(variables = NULL, repweights = NULL, weights = NULL,
       data = NULL, type = c("BRR", "Fay", "JK1","JKn","bootstrap","other"),
       combined.weights=TRUE, rho = NULL, bootstrap.average=NULL,
       scale=NULL, rscales=NULL,fpc=NULL, fpctype=c("fraction","correction"),
       mse=getOption("survey.replicates.mse"),...)
    ## S3 method for class 'imputationList'
    svrepdesign(variables=NULL, repweights,weights,data,
       mse=getOption("survey.replicates.mse"),...)
    ## S3 method for class 'character'
    svrepdesign(variables=NULL,repweights=NULL, weights=NULL,data=NULL,
    type=c("BRR","Fay","JK1", "JKn","bootstrap","other"),combined.weights=TRUE, rho=NULL,
    bootstrap.average=NULL, scale=NULL,rscales=NULL,fpc=NULL,
    fpctype=c("fraction","correction"),mse=getOption("survey.replicates.mse"),
     dbtype="SQLite", dbname,...)

    ## 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, or character string specifying a regular expression that matches the names of the replication weight variables
`weights` | sampling weights
`data` | data frame to look up variables in formulas, or character string giving name of database table
`type` | Type of replication weights
`combined.weights` | `TRUE` if the `repweights` already include the sampling weights. This is usually the case.
`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
`mse` | If `TRUE`, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates
`dbname` | name of database, passed to `DBI::dbConnect()`
`dbtype` | Database driver: see Details
`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.

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 `rscales` is a vector of replicate-specific multipliers for the squared deviations. That is, `rscales` should have one entry for each column of `repweights` If thereplication 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"`

`repweights` may be a character string giving a regular expression for the replicate weight variables. For example, in the California Health Interview Survey public-use data, the sampling weights are `"rakedw0"` and the replicate weights are `"rakedw1"` to `"rakedw80"`. The regular expression `"rakedw[1-9]"` matches the replicate weight variables (and not the sampling weight variable).

`data` may be a character string giving the name of a table or view in a relational database that can be accessed through the `DBI` or `ODBC` interfaces. For DBI interfaces `dbtype` should be the name of the database driver and `dbname` should be the name by which the driver identifies the specific database (eg file name for SQLite). For ODBC databases `dbtype` should be `"ODBC"` and `dbname` should be the registed DSN for the database. On the Windows GUI, `dbname=""` will produce a dialog box for interactive selection.

The appropriate database interface package must already be loaded (eg `RSQLite` for SQLite, `RODBC` for ODBC). The survey design object will contain the replicate weights, but actual variables will be loaded from the database only as needed. Use `close` to close the database connection and `open` to reopen the connection, eg, after loading a saved object.

The database interface does not attempt to modify the underlying database and so can be used with read-only permissions on the database.

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.

### Note

The successive difference weights in the American Community Survey use `scale = 4/ncol(repweights)` and `rscales=rep(1, ncol(repweights)`. JK2 weights use `scale=1`, `rscales=rep(1, ncol(repweights))`

### 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. https://web.archive.org/web/20151110170959/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, combined.weights=FALSE)
    svyratio(~alive, ~arrests, scdrep)

    ## Not run:
    ## Needs RSQLite
    library(RSQLite)
    db_rclus1<-svrepdesign(weights=~pw, repweights="wt[1-9]+", type="JK1", scale=(1-15/757)*14/15,
    data="apiclus1rep",dbtype="SQLite", dbname=system.file("api.db",package="survey"), combined=FALSE)
    svymean(~api00+api99,db_rclus1)

    summary(db_rclus1)

    ## closing and re-opening a connection
    close(db_rclus1)
    db_rclus1
    try(svymean(~api00+api99,db_rclus1))
    db_rclus1<-open(db_rclus1)
    svymean(~api00+api99,db_rclus1)

    ## End(Not run)

[Package survey version 3.32-1 Index]