search for: trianx

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is my data set and I want to do cross validation to see accuracy in classifying Alzheimer and Control Group: str(data) 'data.frame': 499 obs. of...

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is my data set and I want to do cross validation to see accuracy in classifying Alzheimer and Control Group: str(data) 'data.frame': 499 obs. of 606...

...Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is my data set and I want to do cross validation to see accuracy in classifying Alzheimer and Control Group: str(data) 'data.frame': 499 obs. of...