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

sorry for such a long mail! And for my limited knowledge too! Would you please advise if there is any better way of tuning svm()? or what should i do to obtain a reasonable co-efficients for case (2)? Thank you so much!! Best Regards, Maggie ------- Haitian Wang P

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, > > > >

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

ot;g3106","g4373","g4583") fo <- as.formula(g0 ~ g761 * g2809 * g3106 * g4373 * 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 P

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

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: &

[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 variab

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

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

wrote: > > > 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 <-

[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 =