svyratio {survey} R Documentation

## 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.

Thomas Lumley

### 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.

### 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)


[Package survey version 4.1-1 Index]