rake {survey}R Documentation

Raking of replicate weight design


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


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


design A survey object
sample.margins list of formulas or data frames describing sample margins
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


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.


A raked survey design.

See Also

postStratify, compressWeights

calibrate for other ways to use auxiliary information.


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),
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
dclus1r2<-rake(dclus1, list(~stype+sch.wide, ~comp.imp),
               list(pop.table, pop.imp))
svymean(~api00, dclus1r2)

[Package survey version 3.18 Index]