survey (version 4.0)

svyratio: Ratio estimation

Description

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

Usage

# S3 method for survey.design2
svyratio(numerator=formula, denominator,
   design,separate=FALSE, na.rm=FALSE,formula, covmat=FALSE,
   deff=FALSE,influence=FALSE,...)
# S3 method for svyrep.design
svyratio(numerator=formula, denominator, design,
   na.rm=FALSE,formula, covmat=FALSE,return.replicates=FALSE,deff=FALSE, ...)
# S3 method for twophase
svyratio(numerator=formula, denominator, design,
    separate=FALSE, na.rm=FALSE,formula,...)
# S3 method for svyratio
predict(object, total, se=TRUE,...)
# S3 method for svyratio_separate
predict(object, total, se=TRUE,...)
# S3 method for svyratio
SE(object,...,drop=TRUE)
# S3 method for svyratio
coef(object,...,drop=TRUE)
# S3 method for svyratio
confint(object,  parm, level = 0.95,df =Inf,...)

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

deff

Compute design effects

return.replicates

Return replicate estimates of ratios

influence

Return influence functions

drop

Return a vector rather than a matrix

parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

the confidence level required.

df

degrees of freedom for t-distribution in confidence interval, use degf(design) for number of PSUs minus number of 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).

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

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

# NOT RUN {
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)))

SE(com)
coef(com)
coef(com, drop=FALSE)
confint(com)
# }