R Under development (unstable) (2026-02-23 r89457 ucrt) -- "Unsuffered Consequences"
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> library(survey)
Loading required package: grid
Loading required package: Matrix
Loading required package: survival

Attaching package: 'survey'

The following object is masked from 'package:graphics':

    dotchart

> 
> data(api)
> dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
> rclus1 <- as.svrepdesign(dclus1)
> 
> ## 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),
+                 control=list(epsilon=1e-10,maxit=30))
> 
> svymean(~api00, rclus1r)
        mean     SE
api00 641.23 26.874
> svytotal(~enroll, rclus1r)
         total     SE
enroll 3647280 463582
> 
> ff<-~stype+sch.wide
> poptotals<-colSums(model.matrix(ff,model.frame(ff,apipop)))
> rclus1g<-calibrate(rclus1, ~stype+sch.wide, poptotals,calfun="raking",tol=1e-10)
> 
> svymean(~api00,rclus1g)
        mean     SE
api00 641.23 26.874
> svytotal(~enroll,rclus1g)
         total     SE
enroll 3647280 463582
> 
> all.equal(as.vector(weights(rclus1g)/weights(rclus1r)),rep(1,183))
[1] "Numeric: lengths (2745, 183) differ"
> 
> ## Do it for a design without replicate weights
> dclus1r<-rake(dclus1, list(~stype,~sch.wide), list(pop.types, pop.schwide),
+               control=list(epsilon=1e-10,maxit=30))
> 
> svymean(~api00, dclus1r)
        mean     SE
api00 641.23 23.704
> svytotal(~enroll, dclus1r)
         total     SE
enroll 3647280 400603
> 
> dclus1g<-calibrate(dclus1, ~stype+sch.wide, poptotals,calfun="raking",tol=1e-10)
> 
> svymean(~api00,dclus1g)
        mean     SE
api00 641.23 23.704
> svytotal(~enroll,dclus1g)
         total     SE
enroll 3647280 400603
> 
> all.equal(as.vector(weights(dclus1g)/weights(dclus1r)),rep(1,183))
[1] TRUE
> 
> 
> ## Example of raking with partial joint distributions
> pop.table <- xtabs(~stype+sch.wide,apipop)
> 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),
+                control=list(epsilon=1e-10,maxit=30))
Warning message:
In rake(dclus1, list(~stype + sch.wide, ~comp.imp), list(pop.table,  :
  Raking did not converge after 30 iterations.

> svymean(~api00, dclus1r2)
        mean     SE
api00 642.61 22.731
> 
> ff1 <-~stype*sch.wide+comp.imp
> 
> poptotals1<-colSums(model.matrix(ff1,model.frame(ff1,apipop)))
> dclus1g2<-calibrate(dclus1, ~stype*sch.wide+comp.imp, poptotals1, calfun="raking",tol=1e-10)
> 
> svymean(~api00, dclus1g2)
        mean     SE
api00 642.61 22.731
> 
> all.equal(as.vector(weights(dclus1g2)/weights(dclus1r2)),rep(1,183))
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   1.28    0.15    1.43