rake  R Documentation 
Raking uses iterative poststratification 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, which must not contain missing values 
population.margins 
list of tables or data frames describing corresponding population margins 
control 

compress 
If 
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 onedimensional.
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.
postStratify
, compressWeights
calibrate
for other ways to use auxiliary information.
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 poststratification
## (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)
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