svyglm.nb: Survey-weighted negative binomial generalised linear model

Description Usage Arguments Details Value References Examples

View source: R/svyglmnb.R

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

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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:

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

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# ------------------------------------------
# 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
}

Example output

Loading required package: survey
Loading required package: grid
Loading required package: Matrix
Loading required package: survival

Attaching package: 'survey'

The following objects are masked from 'package:sjstats':

    cv, deff

The following object is masked from 'package:graphics':

    dotchart

                                  term    irr std.error conf.low conf.high
2                          (Intercept) 9.8463    0.1556   7.2578   13.3580
3                    factor(RIAGENDR)2 0.4511    0.1805   0.3167    0.6426
4                             log(age) 2.9163    0.2331   1.8467    4.6056
5                    factor(RIDRETH1)2 1.0859    0.1477   0.8130    1.4504
6                    factor(RIDRETH1)3 1.0977    0.1779   0.7746    1.5556
7                    factor(RIDRETH1)4 2.2686    0.2974   1.2665    4.0634
8                    factor(RIDRETH1)5 1.0589    0.3789   0.5039    2.2250
9           factor(RIAGENDR)2:log(age) 0.2947    0.2651   0.1753    0.4955
10 factor(RIAGENDR)2:factor(RIDRETH1)2 0.8314    0.2611   0.4984    1.3870
11 factor(RIAGENDR)2:factor(RIDRETH1)3 1.8285    0.1931   1.2523    2.6698
12 factor(RIAGENDR)2:factor(RIDRETH1)4 1.0668    0.3747   0.5119    2.2232
13 factor(RIAGENDR)2:factor(RIDRETH1)5 1.4564    0.4427   0.6116    3.4680
      p.value
2  <0.001 ***
3  <0.001 ***
4  <0.001 ***
5  0.5769    
6  0.6003    
7  0.0059 ** 
8  0.8800    
9  <0.001 ***
10 0.4795    
11 0.0018 ** 
12 0.8630    
13 0.3957    

Dispersion parameter Theta: 0.8062
   Standard Error of Theta: 0.0216
Showing robust standard errors on link-scale (untransformed).

sjstats documentation built on Jan. 13, 2021, 6:06 p.m.