svyglm {survey}R Documentation

Survey-weighted generalised linear models.


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


## S3 method for class '':
svyglm(formula, design, subset=NULL, ...)
## S3 method for class '':
svyglm(formula, design, subset=NULL, ..., rho=NULL,
return.replicates=FALSE, na.action,multicore=getOption("survey.multicore"))
## S3 method for class 'svyglm':
summary(object, correlation = FALSE, df.resid=NULL,
## S3 method for class 'svyglm':
                != "terms"),vcov=FALSE,...)


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 parameter for Fay's variance method, giving weights of rho and 2-rho
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
multicore Use the multicore package to distribute replicates across processors?
df.resid Optional denominator degrees of freedom for Wald tests
newdata new data frame for prediction
total population size when predicting population total
type linear predictor (link) or response if TRUE, return variances of predictions
vcov if TRUE and se=TRUE return full variance-covariance matrix of predictions


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.

For binomial and Poisson families use family=quasibinomial() and family=quasipoisson() to avoid a warning about non-integer numbers of successes. The `quasi' versions of the family objects give the same point estimates and standard errors and do not give the warning.

If df.resid is not specified the df for the null model is computed by degf and the residual df computed by subtraction. It's not that these are particularly good approximations in a regression model but they are relatively standard. To get tests based on a Normal distribution use df.resid=Inf.

Parallel processing with multicore=TRUE is helpful only for fairly large data sets and on computers with sufficient memory. It may be incompatible with GUIs, although the Mac Aqua GUI appears to be safe.

predict gives fitted values and sampling variability for specific new values of covariates. When newdata are the population mean it gives the regression estimator of the mean, and when newdata are the population totals and total is specified it gives the regression estimator of the population total. Regression estimators of mean and total can also be obtained with calibrate.


svyglm returns an object of class svyglm. The predict method returns an object of class svystat


Thomas Lumley

See Also

glm, which is used to do most of the work.

regTermTest, for multiparameter tests

calibrate, for an alternative way to specify regression estimators of population totals or means

svyttest for one-sample and two-sample t-tests.



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

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

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

  ## Compare regression and ratio estimation of totals
  api.ratio <- svyratio(~api.stu,~enroll, design=dstrat)
  pop<-data.frame(enroll=sum(apipop$enroll, na.rm=TRUE))
  npop <- nrow(apipop)
  predict(api.ratio, pop$enroll)

  ## regression estimator is less efficient
  api.reg <- svyglm(api.stu~enroll, design=dstrat)
  predict(api.reg, newdata=pop, total=npop)
  ## same as calibration estimator
  svytotal(~api.stu, calibrate(dstrat, ~enroll, pop=c(npop, pop$enroll)))

  ## svyglm can also reproduce the ratio estimator
  api.reg2 <- svyglm(api.stu~enroll-1, design=dstrat,
  predict(api.reg2, newdata=pop, total=npop)

  ## higher efficiency by modelling variance better
  api.reg3 <- svyglm(api.stu~enroll-1, design=dstrat,
  predict(api.reg3, newdata=pop, total=npop)
  ## true value


[Package survey version 3.18-1 Index]