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logistic regression by glm

8 messages · tujchl, Uwe Ligges, 屠鞠传礼 +1 more

tujchl
# 
HI 

I use glm in R to do logistic regression. and treat both response and
predictor as factor
In my first try:

*******************************************************************************
Call:
glm(formula = as.factor(diagnostic) ~ as.factor(7161521) + 
as.factor(2281517), family = binomial())

Deviance Residuals: 
Min 1Q Median 3Q Max 
-1.5370 -1.0431 -0.9416 1.3065 1.4331 

Coefficients:
Estimate Std. Error z value Pr(>|z|) 
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
as.factor(7161521)2 1.39811 0.66618 2.099 0.0358 *
as.factor(7161521)3 0.28192 0.83255 0.339 0.7349 
as.factor(2281517)2 -1.11284 0.63692 -1.747 0.0806 .
as.factor(2281517)3 -0.02286 0.80708 -0.028 0.9774 
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 678.55 on 498 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
*******************************************************************************

And I remodel it and *want no intercept*:
*******************************************************************************
Call:
glm(formula = as.factor(diagnostic) ~ as.factor(2281517) + 
as.factor(7161521) - 1, family = binomial())

Deviance Residuals: 
Min 1Q Median 3Q Max 
-1.5370 -1.0431 -0.9416 1.3065 1.4331 

Coefficients:
Estimate Std. Error z value Pr(>|z|) 
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 *
as.factor(2281517)2 -1.6965 0.6751 -2.513 0.0120 *
as.factor(2281517)3 -0.6065 0.8325 -0.728 0.4663 
as.factor(7161521)2 1.3981 0.6662 2.099 0.0358 *
as.factor(7161521)3 0.2819 0.8325 0.339 0.7349 
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 691.76 on 499 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
*******************************************************************************

*As show above in my second model it return no intercept but look this:
Model1:
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
Model2:
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 **

They are exactly the same. Could you please tell me what happen?

Thank you in advance 


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Uwe Ligges
#
On 20.11.2011 12:46, tujchl wrote:
Actually it does not make sense to estimate the model without an 
intercept unless you assume that it is exactly zero for the first levels 
of your factors. Think about the contrasts you are interested in. Looks 
like not the default?

Uwe Ligges
Uwe Ligges
#
On 20.11.2011 16:58, ???? wrote:
Uhh. 4 levels? Than I doubt logistic regression is the right tool for 
you. Please revisit the theory first: It is intended for 2 levels...


Uwe Ligges
Uwe Ligges
#
On 20.11.2011 17:27, ???? wrote:
Depends on your aims - what you want to do with the fitted model.
A multinomial model, some kind of discriminant analysis (lda, qda), tree 
based methods, svm and so son come to mind. You probably want to discuss 
this on some statistics mailing list/forum or among local experts rather 
than on the R list. Since this is actually not that R releated.

Uwe Ligges
David Winsemius
#
On Nov 20, 2011, at 7:26 PM, ???? wrote:

            

      
      
      
    

        
When I looked through the UCLA code it appeared they were using the  
Design package (now superseded by the `rms` package) and that the  
function was `lrm` rather than `glm`. In addition to Harrell's  
excellent text which has a full chapter on this topic you might also  
want to look at Laura Thompson's Companion to Agresti's text:

https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf