anova.negbin: Likelihood Ratio Tests for Negative Binomial GLMs

Description Usage Arguments Details Note References See Also Examples

View source: R/negbin.R

Description

Method function to perform sequential likelihood ratio tests for Negative Binomial generalized linear models.

Usage

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## S3 method for class 'negbin'
anova(object, ..., test = "Chisq")

Arguments

object

Fitted model object of class "negbin", inheriting from classes "glm" and "lm", specifying a Negative Binomial fitted GLM. Typically the output of glm.nb().

...

Zero or more additional fitted model objects of class "negbin". They should form a nested sequence of models, but need not be specified in any particular order.

test

Argument to match the test argument of anova.glm. Ignored (with a warning if changed) if a sequence of two or more Negative Binomial fitted model objects is specified, but possibly used if only one object is specified.

Details

This function is a method for the generic function anova() for class "negbin". It can be invoked by calling anova(x) for an object x of the appropriate class, or directly by calling anova.negbin(x) regardless of the class of the object.

Note

If only one fitted model object is specified, a sequential analysis of deviance table is given for the fitted model. The theta parameter is kept fixed. If more than one fitted model object is specified they must all be of class "negbin" and likelihood ratio tests are done of each model within the next. In this case theta is assumed to have been re-estimated for each model.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

glm.nb, negative.binomial, summary.negbin

Examples

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m1 <- glm.nb(Days ~ Eth*Age*Lrn*Sex, quine, link = log)
m2 <- update(m1, . ~ . - Eth:Age:Lrn:Sex)
anova(m2, m1)
anova(m2)

Example output

Likelihood ratio tests of Negative Binomial Models

Response: Days
                                                                                                                                      Model
1 Eth + Age + Lrn + Sex + Eth:Age + Eth:Lrn + Age:Lrn + Eth:Sex + Age:Sex + Lrn:Sex + Eth:Age:Lrn + Eth:Age:Sex + Eth:Lrn:Sex + Age:Lrn:Sex
2                                                                                                                     Eth * Age * Lrn * Sex
    theta Resid. df    2 x log-lik.   Test    df LR stat.   Pr(Chi)
1 1.90799       120       -1040.728                                
2 1.92836       118       -1039.324 1 vs 2     2 1.403843 0.4956319
Analysis of Deviance Table

Model: Negative Binomial(1.908), link: log

Response: Days

Terms added sequentially (first to last)


            Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
NULL                          145     270.03              
Eth          1  19.0989       144     250.93 1.241e-05 ***
Age          3  16.3483       141     234.58  0.000962 ***
Lrn          1   3.5449       140     231.04  0.059730 .  
Sex          1   0.3989       139     230.64  0.527666    
Eth:Age      3  14.6030       136     216.03  0.002189 ** 
Eth:Lrn      1   0.0447       135     215.99  0.832601    
Age:Lrn      2   1.7482       133     214.24  0.417240    
Eth:Sex      1   1.1470       132     213.09  0.284183    
Age:Sex      3  21.9746       129     191.12 6.603e-05 ***
Lrn:Sex      1   0.0277       128     191.09  0.867712    
Eth:Age:Lrn  2   9.0099       126     182.08  0.011054 *  
Eth:Age:Sex  3   4.8218       123     177.26  0.185319    
Eth:Lrn:Sex  1   3.3160       122     173.94  0.068608 .  
Age:Lrn:Sex  2   6.3941       120     167.55  0.040882 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Warning message:
In anova.negbin(m2) : tests made without re-estimating 'theta'

MASS documentation built on May 3, 2021, 5:08 p.m.

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