AUCRFcv {AUCRF} | R Documentation |
Performes a repeated cross validation analysis and computes the probability of selection for each variable.
AUCRFcv(x, nCV = 5, M = 20)
x |
an object of class |
nCV |
number of folds in cross validation. By default a 5-fold cross validation is performed. |
M |
number of cross validation repetitions. |
The results of this repeated cross validation analysis are (1) a corrected estimation
of the predictive accuracy of the selected variables and (2) an estimate of the probability of selection for
each variable.
The AUC-RF algorithm is exhaustively described in Calle et. al.(2011).
The same AUCRF
object passed (see AUCRF
) as argument but updated with the following
components:
cvAUC |
mean of AUC values in test datasets of the optimal sets of predictors. |
Psel |
probability of selection of each variable as the proportion of times that is selected by AUC-RF method. |
Calle ML, Urrea V, Boulesteix A-L, Malats N (2011) "AUC-RF: A new strategy for genomic profiling with Random Forest". Human Heredity. (In press)
OptimalSet
, AUCRF
, randomForest
.
# Next steps take some time # load included AUCRF result example: # data(fit) # call AUCRFcv process: # fitCV <- AUCRFcv(fit) # The result of this example is included: data(fitCV) summary(fitCV) plot(fitCV)