Re: [R] Extreme AIC in glm(), perfect separation, svm() tuning

--- Haitian Wang PhD Student in Statistics ISOM Department, HKUST, Hong Kong On Fri, Mar 20, 2009 at 4:44 PM, Gavin Simpson gavin.simp...@ucl.ac.ukwrote: On Fri, 2009-03-20 at 12:39 +1100, Gad Abraham wrote: Maggie Wang wrote: Hi, Dieter, Gad, and all, Thank you very much for your

Re: [R] Extreme AIC or BIC values in glm(), logistic regression

Abraham wrote: Maggie Wang wrote: Hi, Dieter, Gad, and all, Thank you very much for your reply! So here is my data, you can copy it into a file names sample.txt Hi Maggie, With this data (allowing for more iterations) I get: lr - glm(fo, family=binomial(link=logit

Re: [R] Extreme AIC or BIC values in glm(), logistic regression

...@u.washington.edu wrote: With 30 variables and only 55 residual degrees of freedom you probably have perfect separation due to not having enough data.  Look at the coefficients -- they are infinite, implying perfect overfitting.      -thomas On Wed, 18 Mar 2009, Maggie Wang wrote: Dear R-users

Re: [R] Extreme AIC or BIC values in glm(), logistic regression

* g4583) lr - glm(fo, family=binomial(link=logit), data=matrix) if look into: summary(lr) you'll see my problem. Thanks a lot! Best Regards, Maggie On Wed, Mar 18, 2009 at 3:30 PM, Dieter Menne dieter.me...@menne-biomed.de wrote: Maggie Wang haitian at ust.hk writes: I use glm() to do logistic

[R] Extreme AIC or BIC values in glm(), logistic regression

Dear R-users, I use glm() to do logistic regression and use stepAIC() to do stepwise model selection. The common AIC value comes out is about 100, a good fit is as low as around 70. But for some model, the AIC went to extreme values like 1000. When I check the P-values, All the independent

[R] (package e1071) SVM tune for best parameters: why they are different everytime i run?

Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] svm.tune - tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y, ranges = list(gamma =

Re: [R] (package e1071) SVM tune for best parameters: why they are different everytime i run?

Thank you so much! I will have a try!! ~ maggie On Dec 27, 2007 6:43 PM, Uwe Ligges [EMAIL PROTECTED] wrote: Maggie Wang wrote: Hi, Uwe, Thanks for the reply!! I have 87 observations in total. If this amount causes the different best.parameters, is there a better way than cross

Re: [R] (package e1071) SVM tune for best parameters: why they are different everytime i run?

: Maggie Wang wrote: Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] svm.tune - tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y