Description Usage Arguments Value Note
Fits iterative random forest algorithm. Returns data.frame with variable importances and top rated features. For now this is an internal function that I've used to explore how recursive feature elimination works in simulations. It may be exported at a later time.
1 2  iterative_RF(X, y, drop_fraction, keep_fraction, mtry_factor,
ntree_factor = 10, min_ntree = 5000, num_processors = 1, nodesize)

X 
A data.frame. Each column corresponds to a feature vectors. 
y 
Response vector. 
drop_fraction 
A number between 0 and 1. Percentage of features dropped at each iteration. 
keep_fraction 
A number between 0 and 1. Proportion features from each module to retain at screening step. 
mtry_factor 
A positive number. Mtry for each random forest
is set to

ntree_factor 
A number greater than 1. 
min_ntree 
Minimum number of trees grown in each random forest. 
num_processors 
Number of processors used to fit random forests. 
nodesize 
Minimum nodesize. 
A data.frame with the top ranked features.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
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