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 design-based standard errors.

### Usage

    ## S3 method for class 'survey.design':
    svyglm(formula, design, subset=NULL, ...)
    ## S3 method for class 'svyrep.design':
    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':
    predict(object,newdata=NULL,total=NULL,
                             type=c("link","response","terms"),
                             se.fit=(type != "terms"),vcov=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 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
`se.fit` | if `TRUE`, return variances of predictions
`vcov` | if `TRUE` and `se=TRUE` return full variance-covariance matrix of predictions

### Details

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`.

### Value

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

### Author(s)

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.

### Examples

      data(api)

      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(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,
            family=quasibinomial()))

      ## 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,
                        family=quasi(link="identity",var="mu"))
      predict(api.reg2, newdata=pop, total=npop)

      ## higher efficiency by modelling variance better
      api.reg3 <- svyglm(api.stu~enroll-1, design=dstrat,
                        family=quasi(link="identity",var="mu^3"))
      predict(api.reg3, newdata=pop, total=npop)
      ## true value
      sum(apipop$api.stu)

[Package survey version 3.19 Index]