# svyratio

0th

Percentile

##### Ratio estimation

Ratio estimation and estimates of totals based on ratios for complex survey samples. Estimating domain means can be done more easily with svymean.

Keywords
survey
##### Usage
## S3 method for class 'survey.design2':
svyratio(numerator=formula, denominator,
design,separate=FALSE, na.rm=FALSE,formula, covmat=FALSE,...)
## S3 method for class 'svyrep.design':
svyratio(numerator=formula, denominator, design,
na.rm=FALSE,formula, covmat=FALSE,return.replicates=FALSE, ...)
## S3 method for class 'twophase':
svyratio(numerator=formula, denominator, design,
separate=FALSE, na.rm=FALSE,formula,...)
## S3 method for class 'svyratio':
predict(object, total, se=TRUE,...)
## S3 method for class 'svyratio_separate':
predict(object, total, se=TRUE,...)
##### Arguments
numerator,formula
formula, expression, or data frame giving numerator variable(s)
denominator
formula, expression, or data frame giving denominator variable(s)
design
survey design object
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
na.rm
Remove missing values?
covmat
Compute the full variance-covariance matrix of the ratios
return.replicates
Return replicate estimates of ratios
...
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).

When design is a two-phase design, stratification will be on the second phase.

##### Value

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

##### References

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

svydesign svymean for estimating proportions and domain means calibrate for estimators related to the separate ratio estimator.

##### Aliases
• svyratio
• print.svyratio
• print.svyratio_separate
• svyratio.svyrep.design
• svyratio.survey.design
• svyratio.survey.design2
• svyratio.twophase
• SE.svyratio
• predict.svyratio
• predict.svyratio_separate
##### 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)))
Documentation reproduced from package survey, version 3.9-1, License: GPL-2 | GPL-3

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