survey (version 2.4)

surveysummary: Summary statistics for sample surveys

Description

Compute means, variances, ratios and totals for data from complex surveys.

Usage

svymean(x, design, na.rm=FALSE) 
svrepmean(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE) 
svyvar(x, design, na.rm=FALSE) 
svytotal(x, design, na.rm=FALSE) 
svreptotal(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE)

Arguments

x

A formula, vector or matrix

design

survey.design or svyrep.design object

na.rm

Should missing values be removed?

rho

parameter for Fay's variance estimator in a BRR design

return.replicates

Return the replicate means?

Value

• Objects of class "svystat" or "svrepstat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.

Details

These functions perform weighted estimation, with each observation being weighted by the inverse of its sampling probability. Except for the table functions, these also give precision estimates that incorporate the effects of stratification and clustering.

The svytotal and svreptotal functions estimate a population total. Use predict on svyratio, svrepratio, svyglm, svrepglm to get ratio or regression estimates of totals.

See Also

svydesign, as.svrepdesign, svrepdesign, svyCprod, mean, var, svyquantile

Examples

data(api)
  ## population
  mean(apipop$api00)
  quantile(apipop$api00,c(.25,.5,.75))
  var(apipop$api00)
  sum(apipop$enroll)
  sum(apipop$api.stu)/sum(apipop$enroll)

  ## one-stage cluster sample
  dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
  summary(dclus1)
  svymean(~api00, dclus1)
  svyquantile(~api00, dclus1, c(.25,.5,.75))
  svyvar(~api00, dclus1)
  svytotal(~enroll, dclus1)
  svyratio(~api.stu, ~enroll, dclus1)

  #stratified sample
  dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
  summary(dstrat)
  svymean(~api00, dstrat)
  svyquantile(~api00, dstrat, c(.25,.5,.75))
  svyvar(~api00, dstrat)
  svytotal(~enroll, dstrat)
  svyratio(~api.stu, ~enroll, dstrat)

  # replicate weights - jackknife (this is slow)
  jkstrat<-as.svrepdesign(dstrat)
  summary(jkstrat)
  svrepmean(~api00, jkstrat)
  svrepquantile(~api00, jkstrat, c(.25,.5,.75))
  svreptotal(~enroll, jkstrat)
  svrepratio(~api.stu, ~enroll, jkstrat)

  # BRR method
  data(scd)
  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)
  svrepmean(~arrests+alive, design=scdrep)