The caret Package - NexTag Supports Open Source Initiatives

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16 featurePlot featurePlot Wrapper for Lattice Plotting of Predictor Variables Description A shortcut to produce lattice graphs Usage featurePlot(x, y, plot = if(is.factor(y)) "strip" else "scatter", labels = c("Feature", ""), ...) Arguments x y Details plot labels a matrix or data frame of continuous feature/probe/spectra data. a factor indicating class membership. the type of plot. For classification: box, strip, density, pairs or ellipse. For regression, pairs or scatter a bad attempt at pre-defined axis labels ... options passed to lattice calls. This function “stacks” data to get it into a form compatible with lattice and creates the plots Value An object of class “trellis”. The ‘update’ method can be used to update components of the object and the ‘print’ method (usually called by default) will plot it on an appropriate plotting device. Author(s) Max Kuhn Examples x
filterVarImp 17 filterVarImp Calculation of filter-based variable importance Description Specific engines for variable importance on a model by model basis. Usage filterVarImp(x, y, nonpara = FALSE, ...) Arguments x y nonpara A matrix or data frame of predictor data A vector (numeric or factor) of outcomes) should nonparametric methods be used to assess the relationship between the features and response (only used with useModel = FALSE ... options to pass to either lm or loess Details The importance of each predictor is evaluated individually using a “filter” approach. For classification, ROC curve analysis is conducted on each predictor. For two class problems, a series of cutoffs is applied to the predictor data to predict the class. The sensitivity and specificity are computed for each cutoff and the ROC curve is computed. The trapezoidal rule is used to compute the area under the ROC curve. This area is used as the measure of variable importance. For multi–class outcomes, the problem is decomposed into all pair-wise problems and the area under the curve is calculated for each class pair (i.e class 1 vs. class 2, class 2 vs. class 3 etc.). For a specific class, the maximum area under the curve across the relevant pair–wise AUC’s is used as the variable importance measure. For regression, the relationship between each predictor and the outcome is evaluated. An argument, nonpara, is used to pick the model fitting technique. When nonpara = FALSE, a linear model is fit and the absolute value of the t–value for the slope of the predictor is used. Otherwise, a loess smoother is fit between the outcome and the predictor. The R 2 statistic is calculated for this model against the intercept only null model. Value A data frame with variable importances. Column names depend on the problem type. For regression, the data frame contains one column: "Overall" for the importance values. Author(s) Max Kuhn
Page 1 and 2: The caret Package March 28, 2008 Ve
Page 3 and 4: applyProcessing 3 Usage Value data(
Page 5 and 6: agEarth 5 Examples set.seed(6) test
Page 7 and 8: agFDA 7 Usage bagFDA(x, ...) ## S3
Page 9 and 10: confusionMatrix 9 Author(s) Max Kuh
Page 11 and 12: createDataPartition 11 createDataPa
Page 13 and 14: dotPlot 13 Value A data frame Autho
Page 15: extractPrediction 15 Value For extr
Page 19 and 20: findLinearCombos 19 Examples corrMa
Page 21 and 22: Alternate Affy Gene Expression Summ
Page 23 and 24: knn3 23 Arguments formula data subs
Page 25 and 26: maxDissim 25 Author(s) Max Kuhn 〈
Page 27 and 28: nearZeroVar 27 nearZeroVar Identifi
Page 29 and 30: normalize2Reference 29 Value A norm
Page 31 and 32: oil 31 oil Fatty acid composition o
Page 33 and 34: plot.train 33 Arguments x Details m
Page 35 and 36: plotClassProbs 35 plotClassProbs Pl
Page 37 and 38: plsda 37 plotObsVsPred(predHomeValu
Page 39 and 40: postResample 39 postResample Calcul
Page 41 and 42: preProcess 41 Arguments x Details V
Page 43 and 44: predict.knn3 43 Examples data(trees
Page 45 and 46: esampleHist 45 Arguments x Details
Page 47 and 48: oc 47 Arguments obs resampled index
Page 49 and 50: sensitivity 49 Usage NA is returned
Page 51 and 52: summary.bagEarth 51 spatialSign(iri
Page 53 and 54: train 53 plot(absorpSubset[1,], typ
Page 55 and 56: train 55 k nearest neighbors knn3 c
Page 57 and 58: varImp 57 number verboseIter return
Page 59 and 60: varImp 59 Value Boosted Trees: varI
Page 61 and 62: INDEX 61 cox2Class (cox2), 9 cox2De

The caret Package - NexTag Supports Open Source Initiatives

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