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.
See Also
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)