Hello Everybody, I am reffering David Meyer's Benchmarking Support Vector Machines , Report No.78 (Nov.2002), i am newly working with R but i am not sure how it is handling missing values in the benchmark datasets, I would be very thankful to you if you could let me know how to handle those missing numerical & categorical variables in the data (e.g. BreastCancer).
because, i am getting fewer predictions after trained model than the test observations for SVM, so could not calculate confusion matrix. At the same time, function lda(),fda() , rpart() did give the equal predictions. Then i m confused a lot, how these functions handled the missing values, are those missing values are imputed with mean, median or new category?? I have another problem with Generalized Linear Model (glm) function. I might have commited some error, but i am not sure where i did? The script for glm function i have tried is as: trdata<-data.frame(train,row.names=NULL) attach(trdata) glmmod <- glm(Class~., family= binomial(link = "logit"),data=trdata,maxit=50) tstdata<-data.frame(test,row.names=NULL) attach(tstdata) xtst <- subset(tstdata, select = -Class) ytst <- Class pred<-predict(glmmod,xtst) library(mda) confusion(pred,ytst) can you help me to sort out the problems? Uttam Phulwale Tata Consultancy Services Limited Mailto: [EMAIL PROTECTED] Website: http://www.tcs.com [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
