logistic regression by glm
On 20.11.2011 17:27, ???? 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
? 2011-11-21 00:13:26?"Uwe Ligges"<ligges at statistik.tu-dortmund.de> ???
On 20.11.2011 16:58, ???? 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
? 2011-11-20 23:45:23?"Uwe Ligges"<ligges at statistik.tu-dortmund.de> ???
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
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