# caret v3.45

0

Monthly downloads

by Max Kuhn

## Classification and Regression Training

Misc functions for training and plotting classification and regression models

## Functions in caret

Name | Description | |

createGrid | Tuning Parameter Grid | |

format.bagEarth | Format 'bagEarth' objects | |

Alternate Affy Gene Expression Summary Methods. | Generate Expression Values from Probes | |

dotPlot | Create a dotplot of variable importance values | |

bagEarth | Bagged Earth | |

predict.bagEarth | Predicted values based on bagged Earth and FDA models | |

confusionMatrix | Create a confusion matrix | |

mdrr | Multidrug Resistance Reversal (MDRR) Agent Data | |

pcaNNet.default | Neural Networks with a Principal Component Step | |

maxDissim | Maximum Dissimilarity Sampling | |

pottery | Pottery from Pre-Classical Sites in Italy | |

filterVarImp | Calculation of filter-based variable importance | |

plot.train | Plot Method for the train Class | |

applyProcessing | Data Processing on Predictor Variables (Deprecated) | |

caret-internal | Internal Functions | |

panel.needle | Needle Plot Lattice Panel | |

plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |

preProcess | Pre-Processing of Predictors | |

roc | Compute the points for an ROC curve | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

bagFDA | Bagged FDA | |

oneSE | Selecting tuning Parameters | |

predictors | List predictors used in the model | |

train | Fit Predictive Models over Different Tuning Parameters | |

sensitivity | Calculate Sensitivity, Specificity and predictive values | |

predict.knn3 | Predictions from k-Nearest Neighbors | |

tecator | Fat, Water and Protein Content of Maat Samples | |

aucRoc | Compute the area under an ROC curve | |

print.confusionMatrix | Print method for confusionMatrix | |

trainControl | Control parameters for train | |

oil | Fatty acid composition of commercial oils | |

normalize.AffyBatch.normalize2Reference | Quantile Normalization to a Reference Distribution | |

BloodBrain | Blood Brain Barrier Data | |

predict.train | Extract predictions and class probabilities from train objects | |

nearZeroVar | Identification of near zero variance predictors | |

plsda | Partial Least Squares Discriminant Analysis | |

postResample | Calculates performance across resamples | |

findCorrelation | Determine highly correlated variables | |

varImp | Calculation of variable importance for regression and classification models | |

knn3 | k-Nearest Neighbour Classification | |

summary.bagEarth | Summarize a bagged earth or FDA fit | |

resampleSummary | Summary of resampled performance estimates | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

plot.varImp.train | Plotting variable importance measures | |

findLinearCombos | Determine linear combinations in a matrix | |

histogram.train | Lattice functions for plotting resampling results | |

cox2 | COX-2 Activity Data | |

normalize2Reference | Quantile Normalize Columns of a Matrix Based on a Reference Distribution | |

createDataPartition | Data Splitting functions | |

resampleHist | Plot the resampling distribution of the model statistics | |

spatialSign | Compute the multivariate spatial sign | |

print.train | Print Method for the train Class | |

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## Last month downloads

## Details

Date | 2008-10-17 |

License | GPL-2 |

Packaged | Sat Oct 18 04:45:49 2008; theussl |

suggests | ada , affy , caTools , class , e1071 , earth (>= 2.0-0) , elasticnet , ellipse , gbm , gpls , grid , ipred , kernlab , klaR , MASS , mboost , mda , mgcv , mlbench , nnet , pamr , party , penalized , pls , proxy , randomForest , rpart , RWeka (>= 0.3-14) , SDDA , sparseLDA (>= 0.1-1) , superpc |

depends | base (>= 2.5.1) , lattice , R (>= 2.5.1) |

Contributors | Max Contributions from Jed Wing, Steve Weston, Andre Williams |

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