[R] get the wald chi square in binary logistic regression

[severine.erhel@free.fr]

th,ks for your help,

i don't have this package on my R, do you know an other package that have this
test...thanks




Selon Renaud Lancelot <renaud.lancelot at cirad.fr>:

> severine.erhel at free.fr a écrit :
> > hello,
> >
> > I work since a few time on R and i wanted to know how to obtain the Wald
> chi
> > square value when you make a binary logistic regression. In fact, i have
> the z
> > value and the signification but is there a script to see what is the value
> of
> > Wald chi square. You can see my model below,
> > Best regards,
> >
> > Séverine Erhel
> >
> If you want a global test for several coeff associated with the same
> variable (e.g., form or criter2 in your example), you can fit the model
> without the variable and compare the 2 models with a likelihood ratio
> test (function anova): it is safer than the Wald test.
>
> If you really want the Wald test, it is available in different packages:
> see for example the function wald.test in package aod.
>
> Best,
>
> Renaud
>
>
> >
> >
> >
> >
> > [Previously saved workspace restored]
> >
> >
> >
> >>m3 = glm(reponse2 ~ form + factor(critere2)
> ,family=binomial,data=mes.donnees)
> >> summary (m3)
> >
> >
> > Call:
> > glm(formula = reponse2 ~ form + factor(critere2), family = binomial,
> >     data = mes.donnees)
> >
> > Deviance Residuals:
> >     Min       1Q   Median       3Q      Max
> > -2.5402   0.2064   0.3354   0.4833   1.4177
> >
> > Coefficients:
> >                          Estimate Std. Error z value Pr(>|z|)
> > (Intercept)                0.5482     0.3930   1.395   0.1631
> > form   Illustration        3.2904     0.6478   5.080 3.78e-07 ***
> > form  Texte+illustration   2.6375     0.4746   5.557 2.74e-08 ***
> > factor(critere2)2         -1.0973     0.5103  -2.150   0.0315 *
> > factor(critere2)3         -0.9891     0.5107  -1.937   0.0528 .
> > ---
> > Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> >
> > (Dispersion parameter for binomial family taken to be 1)
> >
> >     Null deviance: 227.76  on 218  degrees of freedom
> > Residual deviance: 162.11  on 214  degrees of freedom
> > AIC: 172.11
> >
> > Number of Fisher Scoring iterations: 5
> >
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>
>
> --
> Dr Renaud Lancelot, vétérinaire
> Projet FSP régional épidémiologie vétérinaire
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>
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