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get the wald chi square in binary logistic regression

4 messages · Renaud Lancelot, severine.erhel@free.fr, Spencer Graves

Original
severine.erhel@free.fr
# 
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??verine Erhel





[Previously saved workspace restored]
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
Renaud Lancelot
# 
severine.erhel at free.fr a ??crit :
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

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      severine.erhel@free.fr
    

    

      
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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>:

            

      
      
      
    

        

      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Spencer Graves
    

    

      
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Is this a research question?  If not, I'd like to know why you think 
the Wald test is better.

	  Are you famililiar with Bates and Watts (1988) Nonlinear Regression 
Analysis and Its Applications (Wiley), and with the concepts of 
"intrinsic" and "parameter effects" nonlinearity?  In brief, nonlinear 
regression and maximum likelihood estimation more generally involve 
projection onto a nonlinear manifold, which is subject to intrinsic 
nonlinearity as well as parameter effects nonlinearity.  The Wald test 
suffers from both types of nonlinearity, while the 2*log(likelihood 
ratio) procedure suffers from only the intrinsic nonlinearity. 
Moreover, one of the later chapters in Bates and Watts include a 
comparison intrinsic and parameter effects nonlinearity in several 
published nonlinear regression examples.  I don't remember the details 
now, but in all but a few cases, the parameter effects were at least an 
order of magnitude greater than the intrinsic nonlinearity.

	  If you are not familiar with Bates and Watts, I highly recommend it. 
  If you are, I could see comparing Wald and 2*log(likelihood ratio) to 
decide if I want to use Wald in certain applications where 
2*log(likelihood ratio) may not be feasible.

	  If you have evidence raising questions about the above, I'd like to 
know.

	  spencer graves

        
severine.erhel at free.fr wrote: