survey (version 2.8-1)

postStratify: 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.

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

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

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. 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). The method for survey.design objects should be used only in fairly large samples as the resulting standard errors use a simple first-order approximation.

See Also

as.svrepdesign, svrepdesign, rake, compressWeights

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)


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