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4 messages · Val, Mohamed Lajnef, Peter Ehlers

Val
# 
Hi all

Assume I have a data set xx;

Group: 1=group1 ?, 2=group2

IQ: ?1= High, 0 =low

fit <- glm(IQ ~group, data = xx, family = binomial())

summary(fit)

Results

?????? ????????????Estimate Std. Error z value Pr(>|z|)

(Intercept) -2.55456??? 0.210 -12.273? < 5e-16 ***

 group????????? 0.36180 ?????0.076?? 3.952 ????5.24e-05 ***

the odd ratio = exp(0.36180 )= 1.435912

My question is that the log-odd ?estimate 0.3618 ?is it for group1 or group2?

What does the odd ratio 1.43359 is interpreted?

Thanks in advance
Mohamed Lajnef
# 
Hi val,


Val a ?crit :
normally 1vs2, glm takes 2 as reference, in the group1 the IQ increase 
by 0.3618compared to group 2
in the group1 the IQ score  increase by 1.43359  compared to group 2
Regards
ML

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Val
    

    

      
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Thanks for your response.

Do you mean that both the log-odds and odd ratio have the same meaning?





My question is that the log-odd  estimate 0.3618  is it for group1 or group2?


normally 1vs2, glm takes 2 as reference, in the group1 the IQ increase
by 0.3618compared to group 2


    What does the odd ratio 1.43359 is interpreted?


in the group1 the IQ score  increase by 1.43359  compared to group 2

On Mon, Jan 25, 2010 at 10:05 AM, Mohamed Lajnef

        
<Mohamed.lajnef at inserm.fr> wrote:

            

      
      
      
    

        

      
      
    

            

      
      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Peter Ehlers
    

    

      
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Val wrote:

            

      
      
      
    

        
Val,
   Before using R's model fitting functions, it helps
   to understand your model. See any introductory text
   on logistic regression.

   Despite what you claim, it appears that your data 'set'
   may be a data.frame with variables 'IQ' and 'group',
   something like this:

set.seed(34)
xx <- data.frame(IQ = sample(0:1, 10, TRUE), group = gl(2, 5))
xx

   The summary you show was not produced by R, at least
   not as you show it. Here's the result for the above
   data:

fit <- glm(IQ ~ group, data=xx, family=binomial())
summary(fit)

## snipped R output
Coefficients:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.4055     0.9129  -0.444    0.657
group2        0.8109     1.2910   0.628    0.530

## end R output

   Note the '2' in 'group2'. R is smart. It let's you
   know which level of factor 'group' should get the
   added 0.8109 in its log-odds estimate. From your
   reported output it would be impossible to tell that.
   You might have set 'group' to have level '2' as the
   reference level, in which case R would show a
   'group1' row.

   For more on logistic regression, you could consult
   Wikipedia, but here's a brief explanation of your
   simple case:
   Consider two models, one for each group:

   log(Pr(IQ=1)/Pr(IQ=0)) = const_1  (group 1)
   log(Pr(IQ=1)/Pr(IQ=0)) = const_2  (group 2)

   Combine these into a single model, using an
   indicator variable to signal the group:

   log(Pr(IQ=1)/Pr(IQ=0)) = beta_0 + beta_1 * Indic(group 2)

   where Indic(group 2) = 1 for group 2 and 0 otherwise and
   beta_0 = const_1,
   beta_0 + beta_1 = const_2.

This should help you answer your questions yourself.

  - Peter Ehlers