survey (version 3.3-2)

svyratio: 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

Value

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

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).

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)))