Usage
aucMCV(data, seed = 1234, ref_level = levels(data[, 2])[1], auc_rank = "MDG", auc_ntree = 500, auc_nfolds = 5, auc_pdel = 0.2, auc_colour = "grey", auc_iterations = 5)
Arguments
data
a n x p dataframe used to execute the AUCRF algorithm and perform a repetead CV of the AUCRF process.
The dependent variable must be a binary variable defined as a factor and codified as 0 for negatives (e.g controls)
and 1 for positivies (e.g. cases)
seed
a numeric value to set the seed of R's random number generator
ref_level
the class assumed as reference for the binary classification
auc_rank
the importance measure provided by randomForest for ranking the variables. There are
two options: MDG (default) and MDA
auc_ntree
the number of tree of each random forest model used
auc_nfolds
the number of folds in cross validation. By default a 5-fold cross validation is performed
auc_pdel
the fraction of variables to be removed at each step. If $auc_pdel = 0$, it will be removed only one variable at each step
auc_colour
the color chosen
auc_iterations
a numeric that represents the number of cross validation repetitions