| svyglm.nb {sjstats} | R Documentation |
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).
svyglm.nb(formula, design, ...)
formula |
An object of class |
design |
An object of class |
... |
Other arguments passed down to |
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.
An object of class svymle and svyglm.nb,
with some additional information about the model.
Lumley T (2010). Complex Surveys: a guide to analysis using R. Wiley
# ------------------------------------------ # This example reproduces the results from # Lumley 2010, figure E.7 (Appendix E, p256) # ------------------------------------------ library(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