search for: bestg

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0 0 3 0 0 5 0 0 6 0 0 7 0...

...prediction on new data set hh. dim(all_h) [1] 2034 24 dim(hh) # it contains all the variables besides the variables in all_h data set. [1] 640 415 require(e1071) svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h, ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters. bestg<-svm.tune$best.parameters[[1]] bestc<-svm.tune$best.parameters[[2]] svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", probability = TRUE, cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decisi...

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,] 16 23 43 52 1.08157648 0.51241842 0.06234319 1.20656580 > attr(pred, "...

Hi All: I would like to generate a binomial random variable that correlates with a normal random variables with a specified correlation. Off course, the correlation coefficient would not be same at each run because of randomness. I greatly appreciate your input. Ashraf