survey (version 3.33-2)

rake: Raking of replicate weight design

Description

Raking uses iterative post-stratification to match marginal distributions of a survey sample to known population margins.

Usage

rake(design, sample.margins, population.margins, control = list(maxit =
10, epsilon = 1, verbose=FALSE), compress=NULL)

Arguments

design

A survey object

sample.margins

list of formulas or data frames describing sample margins, which must not contain missing values

population.margins

list of tables or data frames describing corresponding population margins

control

maxit controls the number of iterations. Convergence is declared if the maximum change in a table entry is less than epsilon. If epsilon<1 it is taken to be a fraction of the total sampling weight.

compress

If design has replicate weights, attempt to compress the new replicate weight matrix? When NULL, will attempt to compress if the original weight matrix was compressed

Value

A raked survey design.

Details

The sample.margins should be in a format suitable for postStratify.

Raking (aka iterative proportional fitting) is known to converge for any table without zeros, and for any table with zeros for which there is a joint distribution with the given margins and the same pattern of zeros. The `margins' need not be one-dimensional.

The algorithm works by repeated calls to postStratify (iterative proportional fitting), which is efficient for large multiway tables. For small tables calibrate will be faster, and also allows raking to population totals for continuous variables, and raking with bounded weights.

See Also

postStratify, compressWeights

calibrate for other ways to use auxiliary information.

Examples

# NOT RUN {
data(api)
dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
rclus1 <- as.svrepdesign(dclus1)

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

## population marginal totals for each stratum
pop.types <- data.frame(stype=c("E","H","M"), Freq=c(4421,755,1018))
pop.schwide <- data.frame(sch.wide=c("No","Yes"), Freq=c(1072,5122))

rclus1r <- rake(rclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide))

svymean(~api00, rclus1r)
svytotal(~enroll, rclus1r)

## marginal totals correspond to population
xtabs(~stype, apipop)
svytable(~stype, rclus1r, round=TRUE)
xtabs(~sch.wide, apipop)
svytable(~sch.wide, rclus1r, round=TRUE)

## joint totals don't correspond 
xtabs(~stype+sch.wide, apipop)
svytable(~stype+sch.wide, rclus1r, round=TRUE)

## Do it for a design without replicate weights
dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide))

svymean(~api00, dclus1r)
svytotal(~enroll, dclus1r)

## compare to raking with calibrate()
dclus1gr<-calibrate(dclus1, ~stype+sch.wide, pop=c(6194, 755,1018,5122),
           calfun="raking")
svymean(~stype+api00, dclus1r)
svymean(~stype+api00, dclus1gr)

## compare to joint post-stratification
## (only possible if joint population table is known)
##
pop.table <- xtabs(~stype+sch.wide,apipop)
rclus1ps <- postStratify(rclus1, ~stype+sch.wide, pop.table)
svytable(~stype+sch.wide, rclus1ps, round=TRUE)

svymean(~api00, rclus1ps)
svytotal(~enroll, rclus1ps)

## Example of raking with partial joint distributions
pop.imp<-data.frame(comp.imp=c("No","Yes"),Freq=c(1712,4482))
dclus1r2<-rake(dclus1, list(~stype+sch.wide, ~comp.imp),
               list(pop.table, pop.imp))
svymean(~api00, dclus1r2)

## compare to calibrate() syntax with tables
dclus1r2<-calibrate(dclus1, formula=list(~stype+sch.wide, ~comp.imp),
               population=list(pop.table, pop.imp),calfun="raking")
svymean(~api00, dclus1r2)


# }