# Raking of replicate weight design Raking uses iterative post-stratification to match marginal distributions of a survey sample to known population margins. ```r 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. ## Returns A raked survey design. ## See Also `postStratify`, `compressWeights` `calibrate` for other ways to use auxiliary information. ## Examples ```r 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) ```

rake function