svyglm.nb {sjstats}

Survey-weighted negative binomial generalised linear model

Description

svyglm.nb() is an extension to the survey-package to fit survey-weighted negative binomial models. It uses svymle to fit sampling-weighted maximum likelihood estimates, based on starting values provided by glm.nb, as proposed by Lumley (2010, pp249).

Usage

svyglm.nb(formula, design, ...)

Arguments

formula

An object of class formula, i.e. a symbolic description of the model to be fitted. See 'Details' in glm.

design

An object of class svydesign, providing a specification of the survey design.

...

Other arguments passed down to glm.nb.

Details

For details on the computation method, see Lumley (2010), Appendix E (especially 254ff.)

sjstats implements following S3-methods for svyglm.nb-objects: family(), model.frame(), formula(), print(), predict() and residuals(). However, these functions have some limitations:

• family() simply returns the family-object from the underlying glm.nb-model.

• The predict()-method just re-fits the svyglm.nb-model with glm.nb, overwrites the $coefficients from this model-object with the coefficients from the returned svymle-object and finally calls predict.glm to compute the predicted values.

• residuals() re-fits the svyglm.nb-model with glm.nb and then computes the Pearson-residuals from the glm.nb-object.

Value

An object of class svymle and svyglm.nb, with some additional information about the model.

References

Lumley T (2010). Complex Surveys: a guide to analysis using R. Wiley

Examples

# ------------------------------------------
# This example reproduces the results from
# Lumley 2010, figure E.7 (Appendix E, p256)
# ------------------------------------------
if (require("survey")) {
  data(nhanes_sample)

  # create survey design
  des <- svydesign(
    id = ~SDMVPSU,
    strat = ~SDMVSTRA,
    weights = ~WTINT2YR,
    nest = TRUE,
    data = nhanes_sample
  )

  # fit negative binomial regression
  fit <- svyglm.nb(total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)), des)

  # print coefficients and standard errors
  fit
}