# caret v4.60

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 | |

bagEarth | Bagged Earth | |

bagFDA | Bagged FDA | |

oil | Fatty acid composition of commercial oils | |

knn3 | k-Nearest Neighbour Classification | |

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

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

rfeControl | Controlling the Feature Selection Algorithms | |

dotPlot | Create a dotplot of variable importance values | |

confusionMatrix | Create a confusion matrix | |

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

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

nullModel | Fit a simple, non-informative model | |

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

createDataPartition | Data Splitting functions | |

resamples | Collation and Visualization of Resampling Results | |

caretFuncs | Backwards Feature Selection Helper Functions | |

caretSBF | Selection By Filtering (SBF) Helper Functions | |

sbfControl | Control Object for Selection By Filtering (SBF) | |

caret-internal | Internal Functions | |

knnreg | k-Nearest Neighbour Regression | |

print.confusionMatrix | Print method for confusionMatrix | |

createGrid | Tuning Parameter Grid | |

GermanCredit | German Credit Data | |

findCorrelation | Determine highly correlated variables | |

findLinearCombos | Determine linear combinations in a matrix | |

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

lattice.rfe | Lattice functions for plotting resampling results of recursive feature selection | |

train | Fit Predictive Models over Different Tuning Parameters | |

format.bagEarth | Format 'bagEarth' objects | |

modelLookup | Descriptions Of Models Available in train() | |

nearZeroVar | Identification of near zero variance predictors | |

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

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

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

as.table.confusionMatrix | Save Confusion Table Results | |

diff.resamples | Inferential Assessments About Model Performance | |

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

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

oneSE | Selecting tuning Parameters | |

spatialSign | Compute the multivariate spatial sign | |

BloodBrain | Blood Brain Barrier Data | |

cars | Kelly Blue Book resale data for 2005 model year GM cars | |

dhfr | Dihydrofolate Reductase Inhibitors Data | |

filterVarImp | Calculation of filter-based variable importance | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

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

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |

preProcess | Pre-Processing of Predictors | |

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

sensitivity | Calculate sensitivity, specificity and predictive values | |

trainControl | Control parameters for train | |

classDist | Compute and predict the distances to class centroids | |

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

icr.formula | Independent Component Regression | |

dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |

xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |

maxDissim | Maximum Dissimilarity Sampling | |

predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |

sbf | Selection By Filtering (SBF) | |

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

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

bag.default | A General Framework For Bagging | |

prcomp.resamples | Principal Components Analysis of Resampling Results | |

predictors | List predictors used in the model | |

panel.needle | Needle Plot Lattice Panel | |

aucRoc | Compute the area under an ROC curve | |

cox2 | COX-2 Activity Data | |

postResample | Calculates performance across resamples | |

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

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

rfe | Backwards Feature Selection | |

resampleSummary | Summary of resampled performance estimates | |

roc | Compute the points for an ROC curve | |

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

No Results! |

## Last month downloads

## Details

Date | 2010-09-02 |

URL | http://caret.r-forge.r-project.org/ |

License | GPL-2 |

Packaged | 2010-09-02 20:52:46 UTC; kuhna03 |

Repository | CRAN |

Date/Publication | 2010-09-03 07:31:33 |

suggests | ada , affy , caTools , class , e1071 , earth (>= 2.2-3) , elasticnet , ellipse , fastICA , foba , foreach , gam , GAMens (>= 1.1.1) , gbm , glmnet , gpls , grid , hda , HDclassif , ipred , kernlab , klaR , lars , LogicForest , logicFS , LogicReg , MASS , mboost , mda , mgcv , mlbench , neuralnet , nnet , nodeHarvest , pamr , partDSA , party , penalized , pls , proxy , quantregForest , randomForest , rda , relaxo , rocc , rpart , rrcov , RWeka (>= 0.4-1) , sda , SDDA , sparseLDA (>= 0.1-1) , spls , stepPlr , superpc , vbmp |

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

Contributors | Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt |

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