
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: uttam.phulwale@tcs.com
Website: http://www.tcs.com


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