R: Post-stratify a survey

postStratify {survey} | R Documentation

## Post-stratify a survey

### Description

Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Use `rake` when the full joint distribution is not available.

### Usage

    postStratify(design, strata, population, partial = FALSE, ...)
    ## S3 method for class 'svyrep.design'
    postStratify(design, strata, population, partial = FALSE, compress=NULL,...)
    ## S3 method for class 'survey.design'
    postStratify(design, strata, population, partial = FALSE, ...)

### Arguments

`design` | A survey design with replicate weights
`strata` | A formula or data frame of post-stratifying variables, which must not contain missing values.
`population` | A `table`, `xtabs` or `data.frame` with population frequencies
`partial` | if `TRUE`, ignore population strata not present in the sample
`compress` | Attempt to compress the replicate weight matrix? When `NULL` will attempt to compress if the original weight matrix was compressed
`...` | arguments for future expansion

### Details

The `population` totals can be specified as a table with the strata variables in the margins, or as a data frame where one column lists frequencies and the other columns list the unique combinations of strata variables (the format produced by `as.data.frame` acting on a `table` object). A table must have named dimnames to indicate the variable names.

Compressing the replicate weights will take time and may even increase memory use if there is actually little redundancy in the weight matrix (in particular if the post-stratification variables have many values and cut across PSUs).

If a `svydesign` object is to be converted to a replication design the post-stratification should be performed after conversion.

The variance estimate for replication designs follows the same procedure as Valliant (1993) described for estimating totals. Rao et al (2002) describe this procedure for estimating functions (and also the GREG or g-calibration procedure, see `calibrate`)

### Value

A new survey design object.

### Note

If the sampling weights are already post-stratified there will be no change in point estimates after `postStratify` but the standard error estimates will decrease to correctly reflect the post-stratification.

### References

Valliant R (1993) Post-stratification and conditional variance estimation. JASA 88: 89-96

Rao JNK, Yung W, Hidiroglou MA (2002) Estimating equations for the analysis of survey data using poststratification information. Sankhya 64 Series A Part 2, 364-378.

### See Also

`rake`, `calibrate` for other things to do with auxiliary information

`compressWeights` for information on compressing weights

### Examples

    data(api)
    dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
    rclus1<-as.svrepdesign(dclus1)

    svymean(~api00, rclus1)
    svytotal(~enroll, rclus1)

    # post-stratify on school type
    pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018))
    #or: pop.types <- xtabs(~stype, data=apipop)
    #or: pop.types <- table(stype=apipop$stype)

    rclus1p<-postStratify(rclus1, ~stype, pop.types)
    summary(rclus1p)
    svymean(~api00, rclus1p)
    svytotal(~enroll, rclus1p)

    ## and for svydesign objects
    dclus1p<-postStratify(dclus1, ~stype, pop.types)
    summary(dclus1p)
    svymean(~api00, dclus1p)
    svytotal(~enroll, dclus1p)

[Package survey version 3.32-1 Index]