I worried it too, Do you have idear that what tools I can use?
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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