# rake: Raking of replicate weight design In survey: Analysis of Complex Survey Samples

 rake R Documentation

## 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

### 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.

### Value

A raked survey design.

`postStratify`, `compressWeights`

`calibrate` for other ways to use auxiliary information.

### Examples

``````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)

``````

survey documentation built on May 3, 2023, 9:12 a.m.