search for: cforest_control

...mtry, rownumber=rownumber, ntrees=ntrees, shuffled=shuffled) { require(party) require(epiR) test<-rownumber[[i]] train<-shuffled[shuffled!=test] cv.train <- ml.frame[train,] cv.evaluate <- ml.frame[test,] cv.model <- cforest(as.factor(remitter)~., data=cv.train, control=cforest_control(mtry=mtry, ntree=ntrees, mincriterion=0.95)) cv.evaluate$prediction <- predict(cv.model, newdata=cv.evaluate) obj.pred<-predict(cv.model) pred.table<-table(cv.evaluate$prediction, cv.evaluate$remitter) truepos<-pred.table[1,1] trueneg<-pred.table[2,2] falsepos<-pre...

...sep = " ", na.string = "X") #arabidopsis <- subset(arabidopsis, complete.cases(arabidopsis)) arabidopsis <- subset(arabidopsis, is.na(arabidopsis$edit)==FALSE) arabidopsis <- arabidopsis[, !(names(arabidopsis) %in% c("X0", "loc"))] my_cforest_control <- cforest_control(teststat = "quad", testtype = "Univ", mincriterion = 0, ntree = 50, mtry = 3, replace = TRUE) my_cforest <- cforest(edit ~ ., data = arabidopsis, controls = my_cforest_control) varimp_cforest <- varimp(my_cforest) By t...

...ed # of samples in my testing dataset? The resulting analysis will be applied to my specified testing dataset to assess misclassification and accuracy. I am aware that the options of cforest_unbiased do not allow allow the fraction value to be altered so my second question is is data.controls <- cforest_control(teststat = "quad", testtype = "Univ", replace = FALSE, ntree=2000, mtry=2, fraction=.99) the equivalent to data.controls <- cforest_unbiased(ntree=2000, mtry=2) except I have eliminated the default testing dataset? Thank you for your help -- View this message in context:...