survey (version 2.8-1)

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,deff=FALSE) 
svrepmean(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE, deff=FALSE) 
svyvar(x, design, na.rm=FALSE) 
svrepvar(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE) 
svytotal(x, design, na.rm=FALSE,deff=FALSE) 
svreptotal(x, design, na.rm=FALSE, rho=NULL, return.replicates=FALSE,
deff=FALSE)
cv(object,...)
## S3 method for class 'svystat':
coef(object,...)
## S3 method for class 'svrepstat':
coef(object,...)
## S3 method for class 'svystat':
vcov(object,...)
## S3 method for class 'svrepstat':
vcov(object,...)

Arguments

x

A formula, vector or matrix

design

survey.design or svyrep.design object

na.rm

Should cases with missing values be dropped?

rho

parameter for Fay's variance estimator in a BRR design

return.replicates

Return the replicate means?

deff

Return the design effect

object

The result of one of the other survey summary functions

...

additional arguments to cv methods,not currently used

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.

Factor variables are converted to sets of indicator variables for each category in computing means and totals.

With na.rm=TRUE, all cases with missing data are removed. With na.rm=FALSE cases with missing data are not removed and so will produce missing results. When using replicate weights and na.rm=FALSE it may be useful to set options(na.action="na.pass"), otherwise all replicates with any missing results will be discarded.

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

The design effect compares the variance of a mean or total to the variance of a simple random sample of the same size. Although strictly speaking this should be a simple random sample without replacement, we compute as if it were a simple random sample with replacement.

The cv function computes the coefficient of variation of a statistic such as ratio, mean or total.

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, deff=TRUE)
  svymean(~factor(stype),dclus1)
  svyquantile(~api00, dclus1, c(.25,.5,.75))
  svyvar(~api00, dclus1)
  svytotal(~enroll, dclus1, deff=TRUE)
  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)
  svrepmean(~factor(stype),jkstrat)
  svrepvar(~api00,jkstrat)
  svrepquantile(~api00, jkstrat, c(.25,.5,.75))
  svreptotal(~enroll, jkstrat)
  svrepratio(~api.stu, ~enroll, jkstrat)

  # coefficients of variation
  cv(svytotal(~enroll,dstrat))
  cv(svrepratio(~api.stu, ~enroll, jkstrat))

  # extracting statistic and variance
  coef(svytotal(~enroll,dstrat))
  vcov(svrepmean(~api00+api99,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)