# caret v6.0-29

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 | |

calibration | Probability Calibration Plot | |

BoxCoxTrans.default | Box-Cox and Exponential Transformations | |

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

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

dhfr | Dihydrofolate Reductase Inhibitors Data | |

modelLookup | Tools for Models Available in train | |

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

confusionMatrix.train | Estimate a Resampled Confusion Matrix | |

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

BloodBrain | Blood Brain Barrier Data | |

cox2 | COX-2 Activity Data | |

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

diff.resamples | Inferential Assessments About Model Performance | |

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

caret-internal | Internal Functions | |

panel.needle | Needle Plot Lattice Panel | |

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

findCorrelation | Determine highly correlated variables | |

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

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

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

lift | Lift Plot | |

nearZeroVar | Identification of near zero variance predictors | |

confusionMatrix | Create a confusion matrix | |

dotPlot | Create a dotplot of variable importance values | |

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

downSample | Down- and Up-Sampling Imbalanced Data | |

twoClassSim | Two-Class Simulations | |

dummyVars | Create A Full Set of Dummy Variables | |

rfe | Backwards Feature Selection | |

postResample | Calculates performance across resamples | |

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

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

oil | Fatty acid composition of commercial oils | |

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

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

knn3 | k-Nearest Neighbour Classification | |

resampleSummary | Summary of resampled performance estimates | |

train_model_list | A List of Available Models in train | |

sbf | Selection By Filtering (SBF) | |

spatialSign | Compute the multivariate spatial sign | |

trainControl | Control parameters for train | |

avNNet.default | Neural Networks Using Model Averaging | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

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

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

bagFDA | Bagged FDA | |

sensitivity | Calculate sensitivity, specificity and predictive values | |

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

preProcess | Pre-Processing of Predictors | |

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

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

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

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

bagEarth | Bagged Earth | |

plot.rfe | Plot RFE Performance Profiles | |

findLinearCombos | Determine linear combinations in a matrix | |

resamples | Collation and Visualization of Resampling Results | |

knnreg | k-Nearest Neighbour Regression | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

oneSE | Selecting tuning Parameters | |

print.confusionMatrix | Print method for confusionMatrix | |

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

maxDissim | Maximum Dissimilarity Sampling | |

icr.formula | Independent Component Regression | |

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

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

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

segmentationData | Cell Body Segmentation | |

panel.lift2 | Lattice Panel Functions for Lift Plots | |

bag.default | A General Framework For Bagging | |

format.bagEarth | Format 'bagEarth' objects | |

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

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

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

caretFuncs | Backwards Feature Selection Helper Functions | |

rfeControl | Controlling the Feature Selection Algorithms | |

predictors | List predictors used in the model | |

filterVarImp | Calculation of filter-based variable importance | |

createDataPartition | Data Splitting functions | |

GermanCredit | German Credit Data | |

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

update.train | Update or Re-fit a Model | |

train | Fit Predictive Models over Different Tuning Parameters | |

No Results! |

## Last month downloads

## Details

Date | 2014-05-28 |

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

License | GPL-2 |

Packaged | 2014-05-28 10:57:39 UTC; kuhna03 |

NeedsCompilation | yes |

Repository | CRAN |

Date/Publication | 2014-05-29 00:41:51 |

suggests | affy , e1071 , earth (>= 2.2-3) , ellipse , fastICA , gam , ipred , kernlab , klaR , MASS , mda , mgcv , mlbench , nnet , party (>= 0.9-99992) , pls , pROC , proxy , randomForest , RANN , spls |

depends | base (>= 2.10) , ggplot2 , lattice (>= 0.20) , R (>= 2.10) , stats |

imports | BradleyTerry2 , car , foreach , methods , nlme , plyr , reshape2 |

Contributors | the Core team, Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer |

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