## S3 method for class 'default': sbf(x, y, sbfControl = sbfControl(), ...)
## S3 method for class 'formula': sbf(form, data, ..., subset, na.action, contrasts = NULL)
## S3 method for class 'sbf': predict(object, newdata = NULL, ...)
y ~ x1 + x2 + ...
formulaare preferentially to be taken.
sbfControl. (NOTE: If given, this argument must be named.)
sbf: arguments passed to the classification or regression routine (such as
predict.sbf: augments cannot be passed to the prediction function
sbf, an object of class
TRUE, this is a list of predictions for the hold-out samples at each resampling iteration. Otherwise it is
sbfControl$returnResampis "all", a data frame of the resampled performance measures. Otherwise,
predict.sbf, a vector of predictions.
For each iteration of resampling, the predictor variables are univariately filtered prior to modeling. Performance of this approach is estimated using resampling. The same filter and model are then applied to the entire training set and the final model (and final features) are saved.
data(BloodBrain) ## Use a GAM is the filter, then fit a random forest model RFwithGAM <- sbf(bbbDescr, logBBB, sbfControl = sbfControl(functions = rfSBF, verbose = FALSE, method = "cv")) RFwithGAM predict(RFwithGAM, bbbDescr[1:10,])