search for: mytraindata

Hi all, Could anybody tell me what happened to my logistic regression in R? mylog=glm(mytraindata$V1 ~ ., data=mytraindata, family=binomial("logit")) It generated the following error message: Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor 'state1' has new level(s) AP Thank you!

Here is my code: mygbm<-gbm.fit(y=mytraindata[, 1], x=mytraindata[, -1], interaction.depth=4, shrinkage=0.001, n.trees=20000, bag.fraction=1, distribution="bernoulli") Here is the error: Error in gbm.fit(y = mytraindata[, 1], x = mytraindata[, -1], interaction.depth = 4, : The dataset size is too small or subsampling rate is too...

Hi all, I've got the following error message in using e1071 svm routine... Could anybody please help me? Thank you! --------------------------------- model <- svm(y=factor(mytraindata[, 1]), x=mytraindata[, -1], probability=T) Error in if (any(co)) { : missing value where TRUE/FALSE needed In addition: Warning message: In FUN(newX[, i], ...) : NAs introduced by coercion

What's wrong? Very sad about this... model <- ksvm(x=mytraindata[, -1], y=factor(mytraindata[, 1]), prob.model=T) Error in .local(x, ...) : x and y don't match.

...: $ operator is invalid for atomic vectors On Tue, Jul 7, 2009 at 6:45 PM, Steve Lianoglou<mailinglist.honeypot at gmail.com> wrote: > Hi, > > On Jul 7, 2009, at 6:44 PM, Michael wrote: > >> What's wrong? Very sad about this... >> >> model <- ksvm(x=mytraindata[, -1], y=factor(mytraindata[, 1]), >> prob.model=T) >> Error in .local(x, ...) : x and y don't match. > > Same problem you're having with the svm in e1071: I bet your data is wonky. > > split your data into the x and y vars first and inspect it to make sure that >...

...parameters Gseq <- seq(-15,3,2); G <- rep(2, length(Gseq)); G <- G^Gseq Cseq <- seq(-5,13,2); C <- rep(2, length(Cseq)); C <- C^Cseq mygrid <- expand.grid(C=C, G=G) ### Train models svm.models <- lapply(1:nrow(mygrid), function(i) { svm(churn.form, data = mytraindata, method = "C-classification", kernel = "radial", cost = mygrid[i,1], gamma = mygrid[i,2], probability=TRUE) }) ### Predict on test set pred.step3 <- numeric(length(svm.models)) for (i in 1:length(svm.models)) { pred.step1 &...