[R] logistic regression by glm

[Uwe Ligges]

On 20.11.2011 17:27, 屠鞠传礼 wrote:
> I worried it too, Do you have idear that what tools I can use?


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



>
>
>
>
> 在 2011-11-21 00:13:26，"Uwe Ligges"<ligges at statistik.tu-dortmund.de>  写道：
>>
>>
>> On 20.11.2011 16:58, 屠鞠传礼 wrote:
>>> 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?
>>
>>
>> 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
>>
>>
>>
>>
>>
>>>
>>>
>>>
>>>
>>> 在 2011-11-20 23:45:23，"Uwe Ligges"<ligges at 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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