svyglm {survey}R Documentation

Survey-weighted generalised linear models.

Description

Fit a generalised linear model to data from a complex survey design, with inverse-probability weighting and with standard errors corrected for cluster sampling.

Usage

svyglm(formula, design, subset=NULL, ...)
svrepglm(formula, design, subset=NULL, ..., rho=NULL,
return.replicates=FALSE, na.action)
## S3 method for class 'svyglm':
summary(object, correlation = FALSE,  ...) 

Arguments

formula Model formula
design Survey design from svydesign or svrepdesign. Must contain all variables in the formula
subset Expression to select a subpopulation
... Other arguments passed to glm or summary.glm
rho For replicate BRR designs, to specify the paramter for Fay's variance method
return.replicates Return the replicates as a component of the result?
object A svyglm object
correlation Include the correlation matrix of parameters?
na.action Handling of NAs

Details

In svyglm, standard errors for cluster-sampled designs are computed using a linearisation estimate (in the absence of strata this is equivalent to the Huber/White sandwich formula in GEEs). Most of these computations are done in svyCprod. In svrepglm, replicate weight methods are used.

There is no anova method for svyglm as the models are not fitted by maximum likelihood. The function regTermTest may be useful for testing sets of regression terms.

Value

An object of class svyglm.

Author(s)

Thomas Lumley

See Also

svydesign, svrepdesign,as.svrepdesign, glm, svyCprod, svy.varcoef,regTermTest

Examples


  data(api)

  glm(api00~ell+meals+mobility, data=apipop)

  dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
  dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2)
  rstrat<-as.svrepdesign(dstrat)
  rclus2<-as.svrepdesign(dclus2)

  summary(svyglm(api00~ell+meals+mobility, design=dstrat))
  summary(svyglm(api00~ell+meals+mobility, design=dclus2))
  summary(svrepglm(api00~ell+meals+mobility, design=rstrat))
  summary(svrepglm(api00~ell+meals+mobility, design=rclus2))

  ## use quasibinomial, quasipoisson to avoid warning messages
  summary(svyglm(sch.wide~ell+meals+mobility, design=dstrat, family=quasibinomial()))

 

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