svyratio {survey}R Documentation

Ratio estimation

Description

Ratio estimation and estimates of totals based on ratios for complex survey samples.

Usage

## S3 method for class 'survey.design2':
svyratio(numerator, denominator, design,separate=FALSE,...)
## S3 method for class 'svyrep.design':
svyratio(numerator, denominator, design,...)
## S3 method for class 'svyratio':
predict(object, total, se=TRUE,...)
## S3 method for class 'svyratio_separate':
predict(object, total, se=TRUE,...)

Arguments

numerator formula, expression, or data frame giving numerator variable(s)
denominator formula, expression, or data frame giving denominator variable(s)
design from svydesign for svyratio, from svrepdesign for svrepratio
object result of svyratio
total vector of population totals for the denominator variables in object, or list of vectors of population stratum totals if separate=TRUE
se Return standard errors?
separate Estimate ratio separately for strata
... Other unused arguments for other methods

Details

The separate ratio estimate of a total is the sum of ratio estimates in each stratum. If the stratum totals supplied in the total argument and the strata in the design object both have names these names will be matched. If they do not have names it is important that the sample totals are supplied in the correct order, the same order as shown in the output of summary(design).

Value

svyratio returns an object of class svyratio. The predict method returns a matrix of population totals and optionally a matrix of standard errors.

Author(s)

Thomas Lumley

References

Levy and Lemeshow. "Sampling of Populations" (3rd edition). Wiley

See Also

svydesign

svymean for estimating proportions and domain means

calibrate for estimators related to the separate ratio estimator.

Examples

data(scd)

## survey design objects
scddes<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
nest=TRUE, fpc=rep(5,6))
scdnofpc<-svydesign(data=scd, prob=~1, id=~ambulance, strata=~ESA,
nest=TRUE)

# convert to BRR replicate weights
scd2brr <- as.svrepdesign(scdnofpc, type="BRR")

# use BRR replicate weights from Levy and Lemeshow
repweights<-2*cbind(c(1,0,1,0,1,0), c(1,0,0,1,0,1), c(0,1,1,0,0,1),
c(0,1,0,1,1,0))
scdrep<-svrepdesign(data=scd, type="BRR", repweights=repweights)

# ratio estimates
svyratio(~alive, ~arrests, design=scddes)
svyratio(~alive, ~arrests, design=scdnofpc)
svyratio(~alive, ~arrests, design=scd2brr)
svyratio(~alive, ~arrests, design=scdrep)

data(api)
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

## domain means are ratio estimates, but available directly
svyratio(~I(api.stu*(comp.imp=="Yes")), ~as.numeric(comp.imp=="Yes"), dstrat)
svymean(~api.stu, subset(dstrat, comp.imp=="Yes"))

## separate and combined ratio estimates of total
(sep<-svyratio(~api.stu,~enroll, dstrat,separate=TRUE))
(com<-svyratio(~api.stu, ~enroll, dstrat))

stratum.totals<-list(E=1877350, H=1013824, M=920298)

predict(sep, total=stratum.totals)
predict(com, total=sum(unlist(stratum.totals)))


[Package survey version 3.0-1 Index]