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 |
total |
vector of population totals for the denominator variables in
|
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 |
... |
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
See Also
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)