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...uot;, paste(c(fNames[1:2], predTempNames), collapse = "+"))), data)) 3: as.data.frame(model.matrix(as.formula(paste("~ -1 +", paste(c(fNames[1:2], predTempNames), collapse = "+"))), data)) 4: rsf.default(formula = formula, data = dataSet, ntree, mtry, nodesize, splitrule = splitrule[j], importance = importance, forest = forest, do.trace = do.trace, proximity = proximity, ntime = ntime, seed = seed, add.noise = add.noise, predictorWt = predictorWt) 5: rsf(formula = formula, data = dataSet, ntree, mtry, nodesize, splitrule = splitrule[j], importance = importa...

...e G-mean, but misclassification error can be requested. Also, the new parameter get.tree in predict.rfsrc() allows users to extract the ensembles for a single tree or subset of trees over the forest. The default nodesize for survival and competing risk has been changed to 15. We've added new splitrules "auc" and "entropy" for classification. A new variable importance methodology called Holdout VIMP has been implemented. Here, we exclude a variable from a subset of trees and compare the error rates between those trees in which the variables was included against those in which...

...e G-mean, but misclassification error can be requested. Also, the new parameter get.tree in predict.rfsrc() allows users to extract the ensembles for a single tree or subset of trees over the forest. The default nodesize for survival and competing risk has been changed to 15. We've added new splitrules "auc" and "entropy" for classification. A new variable importance methodology called Holdout VIMP has been implemented. Here, we exclude a variable from a subset of trees and compare the error rates between those trees in which the variables was included against those in which...