[R] logistic regression by glm

[Uwe Ligges]

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.
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.