survey: svyratio – R documentation

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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 class 'survey.design2'
svyratio(numerator=formula, denominator,
   design,separate=FALSE, na.rm=FALSE,formula, covmat=FALSE,
   deff=FALSE,influence=FALSE,...)
## S3 method for class 'svyrep.design'
svyratio(numerator=formula, denominator, design,
   na.rm=FALSE,formula, covmat=FALSE,return.replicates=FALSE,deff=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,...)
## S3 method for class 'svyratio'
SE(object,...,drop=TRUE)
## S3 method for class 'svyratio'
coef(object,...,drop=TRUE)
## S3 method for class '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

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.

Author(s)

Thomas Lumley

References

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

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)))

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

survey

Analysis of Complex Survey Samples

v4.0

GPL-2 | GPL-3

Authors