svyquantile {survey}R Documentation

Quantiles for sample surveys

Description

Compute quantiles for data from complex surveys.

Usage

svyquantile(x, design, quantiles, alpha=0.05, ci=FALSE,method = "linear", f = 1)
svrepquantile(x, design, quantiles, method = "linear", f = 1, return.replicates=FALSE)

Arguments

x A formula, vector or matrix
design survey.design or svyrep.design object
quantiles Quantiles to estimate
method see approxfun
f see approxfun
ci Compute a confidence interval (relatively slow)?
alpha Level for confidence interval
return.replicates Return the replicate means?

Details

Interval estimation for quantiles is complicated, because the influence function is not continuous. Linearisation cannot be used, and only some replication weight designs give valid results.

For svyrepquantile we use the method of Francisco-Fuller, which corresponds to inverting a robust score test. At the upper and lower limits of the confidence interval, a test of the null hypothesis that the cumulative distribution function is equal to the target quantile just rejects.

For svrepquantile ordinary replication-based standard errors are computed. These are not valid for the JK1 and JKn jackknife designs. They are valid for BRR and Fay's method, and for some bootstrap-based designs.

Value

svyquantile returns a list whose first component is the quantiles and second component is the confidence intervals. svrepquantile returns an object of class svyrepstat.

Author(s)

Thomas Lumley

References

Binder DA (1991) Use of estimating functions for interval estimation from complex surveys. Journal of Official Statistics 1991: 34-42 Shao J, Tu D (1995) The Jackknife and Bootstrap. Springer.

See Also

svydesign, svymean, as.svrepdesign, svrepdesign

Examples


  data(api)
  ## population
  quantile(apipop$api00,c(.25,.5,.75))

  ## one-stage cluster sample
  dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
  svyquantile(~api00, dclus1, c(.25,.5,.75),ci=TRUE)

  #stratified sample
  dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
  svyquantile(~api00, dstrat, c(.25,.5,.75),ci=TRUE)

  # 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)
  svrepquantile(~arrests+alive, design=scdrep, quantile=0.5)
[Package Contents]