Thank you Ligges :)
one more question:
my response value "diagnostic" have 4 levels (0, 1, 2 and 3), so I use it like this:
"as.factor(diagnostic) ~ as.factor(7161521) +as.factor(2281517) "
Is it all right?
 



在 2011-11-20 23:45:23，"Uwe Ligges" <ligges@statistik.tu-dortmund.de> 写道：
>
>
>On 20.11.2011 12:46, tujchl wrote:
>> 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?
>
>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
>
>
>>
>> Thank you in advance
>>
>>
>> --
>> View this message in context: http://r.789695.n4.nabble.com/logistic-regression-by-glm-tp4088471p4088471.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
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