survey (version 2.5)

postStratify: Post-stratify a replicate weight 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. The advantage of post-stratification is that sampling frames need not be available for the strata.

Usage

postStratify(design, strata, population, partial = FALSE)

Arguments

design

A survey design with replicate weights

strata

A formula or data frame of post-stratifying variables

population

A table, xtabs or data.frame with population frequencies

partial

if TRUE, ignore population strata not present in the sample

Value

• A new survey design object.

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.

See Also

as.svrepdesign, svrepdesign, rake

Examples

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

svrepmean(~api00, rclus1)
svreptotal(~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)
svrepmean(~api00, rclus1p)
svreptotal(~enroll, rclus1p)