svyrecvar {survey} | R Documentation |
Compute the variance of a total under multistage sampling, using a recursive descent algorithm.
svyrecvar(x, clusters, stratas,fpcs, postStrata = NULL, lonely.psu = getOption("survey.lonely.psu"), one.stage=getOption("survey.ultimate.cluster"))
x |
Matrix of data or estimating functions |
clusters |
Data frame or matrix with cluster ids for each stage |
stratas |
Strata for each stage |
fpcs |
Information on population and sample size for each stage,
created by as.fpc |
postStrata |
post-stratification information as created by
postStratify or calibrate |
lonely.psu |
How to handle strata with a single PSU |
one.stage |
If TRUE , compute a one-stage
(ultimate-cluster) estimator |
The main use of this function is to compute the variance of the sum of a set of estimating functions under multistage sampling. The sampling is assumed to be simple or stratified random sampling within clusters at each stage except perhaps the last stage. The variance of a statistic is computed from the variance of estimating functions as described by Binder (1983).
Use one.stage=FALSE
for compatibility with other software that
does not perform multi-stage calculations, and set
options(survey.ultimate.cluster=TRUE)
to make this the default.
The idea of a recursive algorithm is due to Bellhouse (1985). Texts such as Cochran (1977) and Sarndal et al (1991) describe the decomposition of the variance into a single-stage between-cluster estimator and a within-cluster estimator, and this is applied recursively.
If one.stage
is a positive integer it specifies the number of
stages of sampling to use in the recursive estimator.
A covariance matrix
A simple set of finite population corrections will only be exactly correct when each successive stage uses simple or stratified random sampling without replacement. A correction under general unequal probability sampling (eg PPS) would require joint inclusion probabilities (or, at least, sampling probabilities for units not included in the sample), information not generally available.
For a PPS survey the best option is probably to treat the survey as sampled with replacement by omitting the fpc argument. This appears to be the most widely used solution with other software.
Bellhouse DR (1985) Computing Methods for Variance Estimation in Complex Surveys. Journal of Official Statistics. Vol.1, No.3, 1985
Binder, David A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review, 51, 279-292.
Cochran, W. (1977) Sampling Techniques. 3rd edition. Wiley.
Sarndal C-E, Swensson B, Wretman J (1991) Model Assisted Survey Sampling. Springer.
svrVar
for replicate weight designs
svyCprod
for a description of how variances are
estimated at each stage
data(mu284) dmu284<-svydesign(id=~id1+id2,fpc=~n1+n2, data=mu284) svytotal(~y1, dmu284) data(api) # two-stage cluster sample dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) summary(dclus2) svymean(~api00, dclus2) svytotal(~enroll, dclus2,na.rm=TRUE) # two-stage `with replacement' dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) summary(dclus2wr) svymean(~api00, dclus2wr) svytotal(~enroll, dclus2wr,na.rm=TRUE)