Thank you very much :) I search on net and find sometimes response value in logistic model can have more than 2 values, and the way of this kinds of regression is called "Ordinal Logistic Regression". and even we can caculate it by the same way I mean glm in R. here are some references: 1. http://en.wikipedia.org/wiki/Ordered_logit 2. http://www.stat.ubc.ca/~rollin/teach/643w04/lec/node62.html above two tell us what is "Ordinal Logistic Regression". 3. http://www.ats.ucla.edu/stat/r/dae/ologit.htm this show that we can use glm to model it
ÔÚ 2011-11-21 00:56:33£¬"Uwe Ligges" <lig...@statistik.tu-dortmund.de> дµÀ£º > > >On 20.11.2011 17:27, ÍÀ¾Ï´«Àñ 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 > > > >> >> >> >> >> ÔÚ 2011-11-21 00:13:26£¬"Uwe Ligges"<lig...@statistik.tu-dortmund.de> дµÀ£º >>> >>> >>> On 20.11.2011 16:58, ÍÀ¾Ï´«Àñ 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 >>> >>> >>> >>> >>> >>>> >>>> >>>> >>>> >>>> ÔÚ 2011-11-20 23:45:23£¬"Uwe Ligges"<lig...@statistik.tu-dortmund.de> >>>> дµÀ£º >>>>> >>>>> >>>>> 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@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. [[alternative HTML version deleted]]
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