[R] splitting a factor in an analysis of deviance table (negative binomial model)

[Walmes Zeviani]

Sorry by the delay. You could do:

> my.data <- expand.grid(A=factor(1:4), B=factor(1:4), rep=1:4)
> my.data$y <- rbinom(my.data$A, 10, 0.5)
> 
> model <- glm(cbind(y, 10-y)~A*B, family=binomial, data=my.data)
> anova(model, test="Chisq" )
Analysis of Deviance Table

Model: binomial, link: logit

Response: cbind(y, 10 - y)

Terms added sequentially (first to last)


     Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL                    63     53.528          
A     3   1.6792        60     51.849    0.6416
B     3   2.9380        57     48.911    0.4013
A:B   9  13.8380        48     35.073    0.1282
> 
> X <- model.matrix(~A/B, my.data)
> 
> A <- X[,2:4]
> B.A1 <- X[,grep("A1:", colnames(X))]
> B.A2 <- X[,grep("A2:", colnames(X))]
> B.A3 <- X[,grep("A3:", colnames(X))]
> B.A4 <- X[,grep("A4:", colnames(X))]
> 
> model2 <- glm(cbind(y, 10-y)~A+B.A1+B.A2+B.A3+B.A4, family=binomial,
> data=my.data)
> anova(model2, test="Chisq")
Analysis of Deviance Table

Model: binomial, link: logit

Response: cbind(y, 10 - y)

Terms added sequentially (first to last)


     Df Deviance Resid. Df Resid. Dev P(>|Chi|)  
NULL                    63     53.528            
A     3   1.6792        60     51.849   0.64156  
B.A1  3   1.2924        57     50.556   0.73093  
B.A2  3   4.3126        54     46.244   0.22962  
B.A3  3   1.8865        51     44.357   0.59629  
B.A4  3   9.2844        48     35.073   0.02574 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 

Walmes.

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..(....)... 0ooo...                              Walmes Zeviani
...\..(.....(.....)...     Master in Statistics and Agricultural
Experimentation
....\_)..... )../....       walmeszeviani at hotmail.com, Lavras - MG, Brasil
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(_/............................................................................................
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