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?
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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