predictors(x, ...)## S3 method for class 'train':
predictors(x,  ...)
## S3 method for class 'terms':
predictors(x,  ...)
## S3 method for class 'formula':
predictors(x,  ...)
## S3 method for class 'list':
predictors(x,  ...)
## S3 method for class 'mvr':
predictors(x,  ...)
## S3 method for class 'gbm':
predictors(x,  ...)
## S3 method for class 'Weka_classifier':
predictors(x,  ...)
## S3 method for class 'fda':
predictors(x,  ...)
## S3 method for class 'earth':
predictors(x,  ...)
## S3 method for class 'gausspr':
predictors(x,  ...)
## S3 method for class 'ksvm':
predictors(x,  ...)
## S3 method for class 'lssvm':
predictors(x,  ...)
## S3 method for class 'rvm':
predictors(x,  ...)
## S3 method for class 'train':
predictors(x,  ...)
## S3 method for class 'gpls':
predictors(x,  ...)
## S3 method for class 'knn3':
predictors(x,  ...)
## S3 method for class 'LogitBoost':
predictors(x,  ...)
## S3 method for class 'lda':
predictors(x,  ...)
## S3 method for class 'rda':
predictors(x,  ...)
## S3 method for class 'multinom':
predictors(x,  ...)
## S3 method for class 'nnet':
predictors(x,  ...)
## S3 method for class 'pcaNNet':
predictors(x,  ...)
## S3 method for class 'NaiveBayes':
predictors(x,  ...)
## S3 method for class 'randomForest':
predictors(x,  ...)
## S3 method for class 'pamrtrained':
predictors(x, newdata = NULL, threshold = NULL, ...)
## S3 method for class 'superpc':
predictors(x, newdata = NULL, threshold = NULL, n.components = NULL, ...)
## S3 method for class 'slda':
predictors(x,  ...)
## S3 method for class 'rpart':
predictors(x, surrogate = TRUE,  ...)
## S3 method for class 'regbagg':
predictors(x, surrogate = TRUE,  ...)
## S3 method for class 'classbagg':
predictors(x, surrogate = TRUE,  ...)
## S3 method for class 'glmboost':
predictors(x,  ...)
## S3 method for class 'gamboost':
predictors(x,  ...)
## S3 method for class 'blackboost':
predictors(x,  ...)
## S3 method for class 'BinaryTree':
predictors(x, surrogate = TRUE,  ...)
## S3 method for class 'RandomForest':
predictors(x, surrogate = TRUE,  ...)
## S3 method for class 'bagEarth':
predictors(x,  ...)
## S3 method for class 'bagFDA':
predictors(x,  ...)
## S3 method for class 'ppr':
predictors(x,  ...)
## S3 method for class 'rfe':
predictors(x,  ...)
pamr.train and superpc.train: the training datapamr.train and superpc.train: the feature selection thresholdsuperpc.train: the number of PCA components usedNA.randomForest, cforest, ctree, rpart, ipredbagg,  bagging,  earth,  fda,  pamr.train,   superpc.train,  bagEarth and  bagFDA, an attempt was made to report the predictors that were actually used in the final model. In cases where the predictors cannot be determined, NA is returned. For example, nnet may retrun missing values form predictors.