caret/0000755000175000017500000000000012522435234012026 5ustar rbalintrbalintcaret/.Rinstignore0000644000175000017500000000021112453550064014326 0ustar rbalintrbalintvignettes/rocVarImp.pdf vignettes/plsVarImp.pdf vignettes/filteredPred.pdf vignettes/allPred.pdf vignettes/speedup.pdf vignettes/min.pdf caret/DESCRIPTION0000644000175000017500000000203512522435234013534 0ustar rbalintrbalintPackage: caret Version: 6.0-47 Date: 2015-05-04 Title: Classification and Regression Training Author: Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, and Luca Scrucca. Description: Misc functions for training and plotting classification and regression models. Maintainer: Max Kuhn Depends: R (>= 2.10), stats, lattice (>= 0.20), ggplot2 URL: http://caret.r-forge.r-project.org/ Imports: car, reshape2, foreach, methods, plyr, nlme, BradleyTerry2 Suggests: e1071, earth (>= 2.2-3), fastICA, gam, ipred, kernlab, klaR, MASS, ellipse, mda, mgcv, mlbench, nnet, party (>= 0.9-99992), pls, pROC (>= 1.8), proxy, randomForest, RANN, spls, subselect, pamr, superpc, Cubist, testthat (>= 0.9.1) License: GPL (>= 2) NeedsCompilation: yes Packaged: 2015-05-05 10:50:20 UTC; kuhna03 Repository: CRAN Date/Publication: 2015-05-06 18:00:28 caret/man/0000755000175000017500000000000012520743744012607 5ustar rbalintrbalintcaret/man/diff.resamples.Rd0000644000175000017500000000716412453550065016005 0ustar rbalintrbalint\name{diff.resamples} \alias{diff.resamples} \alias{summary.diff.resamples} \alias{compare_models} \title{Inferential Assessments About Model Performance} \description{Methods for making inferences about differences between models } \usage{ \method{diff}{resamples}(x, models = x$models, metric = x$metrics, test = t.test, confLevel = 0.95, adjustment = "bonferroni", ...) \method{summary}{diff.resamples}(object, digits = max(3, getOption("digits") - 3), ...) compare_models(a, b, metric = a$metric[1]) } \arguments{ \item{x}{an object generated by \code{resamples}} \item{models}{a character string for which models to compare} \item{metric}{a character string for which metrics to compare} \item{test}{a function to compute differences. The output of this function should have scalar outputs called \code{estimate} and \code{p.value}} \item{object}{a object generated by \code{diff.resamples}} \item{adjustment}{any p-value adjustment method to pass to \code{\link[stats]{p.adjust}}. } \item{confLevel}{confidence level to use for \code{\link{dotplot.diff.resamples}}. See Details below.} \item{digits}{the number of significant differences to display when printing} \item{a, b}{two objects of class \code{\link{train}}, \code{\link{sbf}} or \code{\link{rfe}} with a common set of resampling indices in the \code{control} object.} \item{\dots}{further arguments to pass to \code{test}} } \details{ The ideas and methods here are based on Hothorn et al. (2005) and Eugster et al. (2008). For each metric, all pair-wise differences are computed and tested to assess if the difference is equal to zero. When a Bonferroni correction is used, the confidence level is changed from \code{confLevel} to \code{1-((1-confLevel)/p)} here \code{p} is the number of pair-wise comparisons are being made. For other correction methods, no such change is used. \code{compare_models} is a shorthand function to compare two models using a single metric. It returns the results of \code{\link[stats]{t.test}} on the differences. } \value{ An object of class \code{"diff.resamples"} with elements: \item{call }{the call} \item{difs }{a list for each metric being compared. Each list contains a matrix with differences in columns and resamples in rows } \item{statistics }{a list of results generated by \code{test}} \item{adjustment}{the p-value adjustment used} \item{models}{a character string for which models were compared.} \item{metrics }{a character string of performance metrics that were used} or... An object of class \code{"summary.diff.resamples"} with elements: \item{call }{the call} \item{table }{a list of tables that show the differences and p-values } ...or (for \code{compare_models}) an object of class \code{htest} resulting from \code{\link[stats]{t.test}}. } \references{Hothorn et al. The design and analysis of benchmark experiments. Journal of Computational and Graphical Statistics (2005) vol. 14 (3) pp. 675-699 Eugster et al. Exploratory and inferential analysis of benchmark experiments. Ludwigs-Maximilians-Universitat Munchen, Department of Statistics, Tech. Rep (2008) vol. 30} \author{Max Kuhn} \seealso{\code{\link{resamples}}, \code{\link{dotplot.diff.resamples}}, \code{\link{densityplot.diff.resamples}}, \code{\link{bwplot.diff.resamples}}, \code{\link{levelplot.diff.resamples}}} \examples{ \dontrun{ #load(url("http://topepo.github.io/caret/exampleModels.RData")) resamps <- resamples(list(CART = rpartFit, CondInfTree = ctreeFit, MARS = earthFit)) difs <- diff(resamps) difs summary(difs) compare_models(rpartFit, ctreeFit) } } \keyword{ models } caret/man/postResample.Rd0000644000175000017500000001032312520743335015547 0ustar rbalintrbalint\name{postResample} \alias{postResample} \alias{defaultSummary} \alias{twoClassSummary} \alias{getTrainPerf} \alias{mnLogLoss} \alias{R2} \alias{RMSE} \title{Calculates performance across resamples} \description{ Given two numeric vectors of data, the mean squared error and R-squared are calculated. For two factors, the overall agreement rate and Kappa are determined. } \usage{ postResample(pred, obs) defaultSummary(data, lev = NULL, model = NULL) twoClassSummary(data, lev = NULL, model = NULL) mnLogLoss(data, lev = NULL, model = NULL) R2(pred, obs, formula = "corr", na.rm = FALSE) RMSE(pred, obs, na.rm = FALSE) getTrainPerf(x) } \arguments{ \item{pred}{A vector of numeric data (could be a factor)} \item{obs}{A vector of numeric data (could be a factor)} \item{data}{a data frame or matrix with columns \code{obs} and \code{pred} for the observed and predicted outcomes. For \code{twoClassSummary}, columns should also include predicted probabilities for each class. See the \code{classProbs} argument to \code{\link{trainControl}}} \item{lev}{a character vector of factors levels for the response. In regression cases, this would be \code{NULL}.} \item{model}{a character string for the model name (as taken form the \code{method} argument of \code{\link{train}}.} \item{formula}{which \eqn{R^2} formula should be used? Either "corr" or "traditional". See Kvalseth (1985) for a summary of the different equations.} \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.} \item{x}{an object of class \code{\link{train}}}. } \details{ \code{postResample} is meant to be used with \code{apply} across a matrix. For numeric data the code checks to see if the standard deviation of either vector is zero. If so, the correlation between those samples is assigned a value of zero. \code{NA} values are ignored everywhere. Note that many models have more predictors (or parameters) than data points, so the typical mean squared error denominator (n - p) does not apply. Root mean squared error is calculated using \code{sqrt(mean((pred - obs)^2}. Also, \eqn{R^2} is calculated wither using as the square of the correlation between the observed and predicted outcomes when \code{form = "corr"}. when \code{form = "traditional"}, \deqn{ R^2 = 1-\frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y}_i)^2} } For \code{defaultSummary} is the default function to compute performance metrics in \code{\link{train}}. It is a wrapper around \code{postResample}. \code{twoClassSummary} computes sensitivity, specificity and the area under the ROC curve. \code{mnLogLoss} computes the minus log-likelihood of the multinomial distribution (without the constant term): \deqn{ -logLoss = \frac{-1}{n}\sum_{i=1}^n \sum_{j=1}^C y_{ij} \log(p_{ij}) } where the \code{y} values are binary indicators for the classes and \code{p} are the predicted class probabilities. To use \code{twoClassSummary} and/or \code{mnLogLoss}, the \code{classProbs} argument of \code{\link{trainControl}} should be \code{TRUE}. Other functions can be used via the \code{summaryFunction} argument of \code{\link{trainControl}}. Custom functions must have the same arguments as\code{defaultSummary}. The function \code{getTrainPerf} returns a one row data frame with the resampling results for the chosen model. The statistics will have the prefix "\code{Train}" (i.e. "\code{TrainROC}"). There is also a column called "\code{method}" that echoes the argument of the call to \code{\link{trainControl}} of the same name. } \value{ A vector of performance estimates. } \author{Max Kuhn} \references{Kvalseth. Cautionary note about \eqn{R^2}. American Statistician (1985) vol. 39 (4) pp. 279-285} \seealso{\code{\link{trainControl}}} \examples{ predicted <- matrix(rnorm(50), ncol = 5) observed <- rnorm(10) apply(predicted, 2, postResample, obs = observed) classes <- c("class1", "class2") set.seed(1) dat <- data.frame(obs = factor(sample(classes, 50, replace = TRUE)), pred = factor(sample(classes, 50, replace = TRUE)), class1 = runif(50), class2 = runif(50)) defaultSummary(dat, lev = classes) twoClassSummary(dat, lev = classes) mnLogLoss(dat, lev = classes) } \keyword{utilities} caret/man/safs.default.Rd0000644000175000017500000001365112453550065015460 0ustar rbalintrbalint\name{safs.default} \alias{safs.default} \alias{safs} \title{Simulated annealing feature selection} \description{ Supervised feature selection using simulated annealing } \usage{ safs(x, ...) \method{safs}{default}(x, y, iters = 10, differences = TRUE, safsControl = safsControl(), ...) } \arguments{ \item{x}{an object where samples are in rows and features are in columns. This could be a simple matrix, data frame or other type (e.g. sparse matrix). See Details below. } \item{y}{a numeric or factor vector containing the outcome for each sample.} \item{iters}{number of search iterations} \item{differences}{a logical: should the difference in fitness values with and without each predictor be calculated} \item{safsControl}{a list of values that define how this function acts. See \code{\link{safsControl}} and URL. } \item{\ldots}{arguments passed to the classification or regression routine specified in the function \code{safsControl$functions$fit}} } \details{ \code{\link{safs}} conducts a supervised binary search of the predictor space using simulated annealing (SA). See XXX for more information on this search algorithm. This function conducts the search of the feature space repeatedly within resampling iterations. First, the training data are split be whatever resampling method was specified in the control function. For example, if 10-fold cross-validation is selected, the entire simulated annealing search is conducted 10 separate times. For the first fold, nine tenths of the data are used in the search while the remaining tenth is used to estimate the external performance since these data points were not used in the search. During the search, a measure of fitness (i.e. SA energy value) is needed to guide the search. This is the internal measure of performance. During the search, the data that are available are the instances selected by the top-level resampling (e.g. the nine tenths mentioned above). A common approach is to conduct another resampling procedure. Another option is to use a holdout set of samples to determine the internal estimate of performance (see the holdout argument of the control function). While this is faster, it is more likely to cause overfitting of the features and should only be used when a large amount of training data are available. Yet another idea is to use a penalized metric (such as the AIC statistic) but this may not exist for some metrics (e.g. the area under the ROC curve). The internal estimates of performance will eventually overfit the subsets to the data. However, since the external estimate is not used by the search, it is able to make better assessments of overfitting. After resampling, this function determines the optimal number of iterations for the SA. Finally, the entire data set is used in the last execution of the simulated annealing algorithm search and the final model is built on the predictor subset that is associated with the optimal number of iterations determined by resampling (although the update function can be used to manually set the number of iterations). This is an example of the output produced when \code{safsControl(verbose = TRUE)} is used: \preformatted{Fold03 1 0.401 (11) Fold03 2 0.401->0.410 (11+1, 91.7\%) * Fold03 3 0.410->0.396 (12+1, 92.3\%) 0.969 A Fold03 4 0.410->0.370 (12+2, 85.7\%) 0.881 Fold03 5 0.410->0.399 (12+2, 85.7\%) 0.954 A Fold03 6 0.410->0.399 (12+1, 78.6\%) 0.940 A Fold03 7 0.410->0.428 (12+2, 73.3\%) * } The text "Fold03" indicates that this search is for the third cross-validation fold. The initial subset of 11 predictors had a fitness value of 0.401. The next iteration added a single feature the the existing best subset of 11 (as indicated by "11+1") that increased the fitness value to 0.410. This new solution, which has a Jaccard similarity value of 91.7\% to the current best solution, is automatically accepted. The third iteration adds another feature to the current set of 12 but does not improve the fitness. The acceptance probability for this difference is shown to be 95.6\% and the "A" indicates that this new sub-optimal subset is accepted. The fourth iteration does not show an increase and is not accepted. Note that the Jaccard similarity value of 85.7\% is the similarity to the current best solution (from iteration 2) and the "12+2" indicates that there are two additional features added from the current best that contains 12 predictors. The search algorithm can be parallelized in several places: \enumerate{ \item each externally resampled SA can be run independently (controlled by the \code{allowParallel} option of \code{\link{safsControl}}) \item if inner resampling is used, these can be run in parallel (controls depend on the function used. See, for example, \code{\link[caret]{trainControl}}) \item any parallelization of the individual model fits. This is also specific to the modeling function. } It is probably best to pick one of these areas for parallelization and the first is likely to produces the largest decrease in run-time since it is the least likely to incur multiple re-starting of the worker processes. Keep in mind that if multiple levels of parallelization occur, this can effect the number of workers and the amount of memory required exponentially. } \value{ an object of class \code{safs} } \references{\url{http://topepo.github.io/caret/GA.html} \url{http://topepo.github.io/caret/SA.html} Kuhn and Johnson (2013), Applied Predictive Modeling, Springer } \author{Max Kuhn} \seealso{ \code{\link{safsControl}}, \code{\link{predict.safs}} } \examples{ \dontrun{ set.seed(1) train_data <- twoClassSim(100, noiseVars = 10) test_data <- twoClassSim(10, noiseVars = 10) ## A short example ctrl <- safsControl(functions = rfSA, method = "cv", number = 3) rf_search <- safs(x = train_data[, -ncol(train_data)], y = train_data$Class, iters = 3, safsControl = ctrl) rf_search } } \keyword{models} caret/man/ga_functions.Rd0000644000175000017500000000756012453550065015562 0ustar rbalintrbalint\name{gafs_initial} \alias{gafs_initial} \alias{gafs_lrSelection} \alias{gafs_rwSelection} \alias{gafs_tourSelection} \alias{gafs_uCrossover} \alias{gafs_spCrossover} \alias{gafs_raMutation} \alias{caretGA} \alias{rfGA} \alias{treebagGA} \title{ Ancillary genetic algorithm functions } \description{ Built-in functions related to genetic algorithms} \usage{ gafs_initial(vars, popSize, ...) gafs_lrSelection(population, fitness, r = NULL, q = NULL, ...) gafs_rwSelection(population, fitness, ...) gafs_tourSelection(population, fitness, k = 3, ...) gafs_spCrossover(population, fitness, parents, ...) gafs_uCrossover(population, parents, ...) gafs_raMutation(population, parent, ...) caretGA rfGA treebagGA } \arguments{ \item{vars}{number of possible predictors} \item{popSize}{the population size passed into \code{\link{gafs}}} \item{population}{a binary matrix of the current subsets with predictors in columns and individuals in rows} \item{fitness}{a vector of fitness values} \item{parent, parents}{integer(s) for which chromosomes are altered} \item{r, q, k}{tuning parameters for the specific selection operator} \item{\dots}{not currently used} } \details{ These functions are used with the \code{functions} argument of the \code{\link{gafsControl}} function. More information on the details of these functions are at \url{http://topepo.github.io/caret/GA.html}. Most of the \code{gafs_*} functions are based on those from the GA package by Luca Scrucca. These functions here are small re-writes to work outside of the GA package. The objects \code{caretGA}, \code{rfGA} and \code{treebagGA} are example lists that can be used with the \code{functions} argument of \code{\link{gafsControl}}. In the case of \code{caretGA}, the \code{...} structure of \code{\link{gafs}} passes through to the model fitting routine. As a consequence, the \code{\link{train}} function can easily be accessed by passing important arguments belonging to \code{\link{train}} to \code{\link{gafs}}. See the examples below. By default, using \code{caretGA} will used the resampled performance estimates produced by \code{\link{train}} as the internal estimate of fitness. For \code{rfGA} and \code{treebagGA}, the \code{randomForest} and \code{bagging} functions are used directly (i.e. \code{\link{train}} is not used). Arguments to either of these functions can also be passed to them though the \code{\link{gafs}} call (see examples below). For these two functions, the internal fitness is estimated using the out-of-bag estimates naturally produced by those functions. While faster, this limits the user to accuracy or Kappa (for classification) and RMSE and R-squared (for regression). } \value{ The return value depends on the function. } \references{ Scrucca L (2012). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. \url{cran.r-project.org/web/packages/GA/} \url{http://topepo.github.io/caret/GA.html} } \author{ Luca Scrucca, \code{gafs_initial}, \code{caretGA}, \code{rfGA} and \code{treebagGA} by Max Kuhn } \seealso{\code{\link{gafs}}, \code{\link{gafsControl}}} \examples{ pop <- gafs_initial(vars = 10, popSize = 10) pop gafs_lrSelection(population = pop, fitness = 1:10) gafs_spCrossover(population = pop, fitness = 1:10, parents = 1:2) \dontrun{ ## Hypothetical examples lda_ga <- gafs(x = predictors, y = classes, gafsControl = gafsControl(functions = caretGA), ## now pass arguments to `train` method = "lda", metric = "Accuracy" trControl = trainControl(method = "cv", classProbs = TRUE)) rf_ga <- gafs(x = predictors, y = classes, gafsControl = gafsControl(functions = rfGA), ## these are arguments to `randomForest` ntree = 1000, importance = TRUE) } } caret/man/nearZeroVar.Rd0000644000175000017500000000773212453550065015342 0ustar rbalintrbalint\name{nearZeroVar} \alias{nearZeroVar} \alias{nzv} \alias{checkResamples} \alias{checkConditionalX} \title{Identification of near zero variance predictors} \description{ \code{nearZeroVar} diagnoses predictors that have one unique value (i.e. are zero variance predictors) or predictors that are have both of the following characteristics: they have very few unique values relative to the number of samples and the ratio of the frequency of the most common value to the frequency of the second most common value is large. \code{checkConditionalX} looks at the distribution of the columns of \code{x} conditioned on the levels of \code{y} and identifies columns of \code{x} that are sparse within groups of \code{y}.} \usage{ nearZeroVar(x, freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE, foreach = FALSE, allowParallel = TRUE) nzv(x, freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE) checkConditionalX(x, y) checkResamples(index, x, y) } \arguments{ \item{x}{a numeric vector or matrix, or a data frame with all numeric data} \item{freqCut}{the cutoff for the ratio of the most common value to the second most common value } \item{uniqueCut}{ the cutoff for the percentage of distinct values out of the number of total samples} \item{saveMetrics}{a logical. If false, the positions of the zero- or near-zero predictors is returned. If true, a data frame with predictor information is returned. } \item{y}{a factor vector with at least two levels} \item{index}{a list. Each element corresponds to the training set samples in \code{x} for a given resample} \item{foreach}{should the \pkg{foreach} package be used for the computations? If \code{TRUE}, less memory should be used.} \item{allowParallel}{should the parallel processing via the \pkg{foreach} package be used for the computations? If \code{TRUE}, more memory will be used but execution time should be shorter.} } \details{ For example, an example of near zero variance predictor is one that, for 1000 samples, has two distinct values and 999 of them are a single value. To be flagged, first the frequency of the most prevalent value over the second most frequent value (called the ``frequency ratio'') must be above \code{freqCut}. Secondly, the ``percent of unique values,'' the number of unique values divided by the total number of samples (times 100), must also be below \code{uniqueCut}. In the above example, the frequency ratio is 999 and the unique value percentage is 0.0001. Checking the conditional distribution of \code{x} may be needed for some models, such as naive Bayes where the conditional distributions should have at least one data point within a class. \code{nzv} is the original version of the function. } \value{ For \code{nearZeroVar}: if \code{saveMetrics = FALSE}, a vector of integers corresponding to the column positions of the problematic predictors. If \code{saveMetrics = TRUE}, a data frame with columns: \item{freqRatio }{the ratio of frequencies for the most common value over the second most common value} \item{percentUnique }{the percentage of unique data points out of the total number of data points} \item{zeroVar }{a vector of logicals for whether the predictor has only one distinct value} \item{nzv }{a vector of logicals for whether the predictor is a near zero variance predictor} For \code{checkResamples} or \code{checkConditionalX}, a vector of column indicators for predictors with empty conditional distributions in at least one class of \code{y}. } \author{Max Kuhn, with speed improvements to nearZeroVar by Allan Engelhardt} \examples{ nearZeroVar(iris[, -5], saveMetrics = TRUE) data(BloodBrain) nearZeroVar(bbbDescr) set.seed(1) classes <- factor(rep(letters[1:3], each = 30)) x <- data.frame(x1 = rep(c(0, 1), 45), x2 = c(rep(0, 10), rep(1, 80))) lapply(x, table, y = classes) checkConditionalX(x, classes) folds <- createFolds(classes, k = 3, returnTrain = TRUE) x$x3 <- x$x1 x$x3[folds[[1]]] <- 0 checkResamples(folds, x, classes) } \keyword{utilities} caret/man/mdrr.Rd0000644000175000017500000000322412453550065014040 0ustar rbalintrbalint\name{mdrr} \docType{data} \alias{mdrr} \alias{mdrrClass} \alias{mdrrDescr} \title{Multidrug Resistance Reversal (MDRR) Agent Data} \description{ Svetnik et al. (2003) describe these data: "Bakken and Jurs studied a set of compounds originally discussed by Klopman et al., who were interested in multidrug resistance reversal (MDRR) agents. The original response variable is a ratio measuring the ability of a compound to reverse a leukemia cell's resistance to adriamycin. However, the problem was treated as a classification problem, and compounds with the ratio >4.2 were considered active, and those with the ratio <= 2.0 were considered inactive. Compounds with the ratio between these two cutoffs were called moderate and removed from the data for twoclass classification, leaving a set of 528 compounds (298 actives and 230 inactives). (Various other arrangements of these data were examined by Bakken and Jurs, but we will focus on this particular one.) We did not have access to the original descriptors, but we generated a set of 342 descriptors of three different types that should be similar to the original descriptors, using the DRAGON software." The data and R code are in the Supplemental Data file for the article. } \usage{data(mdrr)} \value{ \item{mdrrDescr}{the descriptors} \item{mdrrClass}{the categorical outcome ("Active" or "Inactive")} } \source{Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P. Feuston, B. P (2003). Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling, \emph{Journal of Chemical Information and Computer Sciences}, Vol. 43, pg. 1947-1958. } \keyword{datasets} caret/man/spatialSign.Rd0000644000175000017500000000231512453550065015352 0ustar rbalintrbalint\name{spatialSign} \alias{spatialSign} \alias{spatialSign.default} \alias{spatialSign.matrix} \alias{spatialSign.data.frame} \title{Compute the multivariate spatial sign} \description{ Compute the spatial sign (a projection of a data vector to a unit length circle). The spatial sign of a vector \code{w} is \code{w /norm(w)}. } \usage{ \method{spatialSign}{default}(x) \method{spatialSign}{matrix}(x) \method{spatialSign}{data.frame}(x) } \arguments{ \item{x}{an object full of numeric data (which should probably be scaled). Factors are not allowed. This could be a vector, matrix or data frame.} } \value{A vector, matrix or data frame with the same dim names of the original data.} \author{Max Kuhn} \references{Serneels et al. Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators. J. Chem. Inf. Model (2006) vol. 46 (3) pp. 1402-1409} \examples{ spatialSign(rnorm(5)) spatialSign(matrix(rnorm(12), ncol = 3)) # should fail since the fifth column is a factor try(spatialSign(iris), silent = TRUE) spatialSign(iris[,-5]) trellis.par.set(caretTheme()) featurePlot(iris[,-5], iris[,5], "pairs") featurePlot(spatialSign(scale(iris[,-5])), iris[,5], "pairs") } \keyword{manip} caret/man/models.Rd0000644000175000017500000012564512520743663014376 0ustar rbalintrbalint\name{train_model_list} \alias{train_model_list} \alias{models} \title{A List of Available Models in train} \description{These models are included in the package via wrappers for \code{\link{train}}. Custom models can also be created. See the URL below. \strong{AdaBoost.M1} (\code{method = 'AdaBoost.M1'}) For classification using packages \pkg{adabag} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Trees (\code{mfinal}, numeric) \item Max Tree Depth (\code{maxdepth}, numeric) \item Coefficient Type (\code{coeflearn}, character) } \strong{Adaptive Mixture Discriminant Analysis} (\code{method = 'amdai'}) For classification using package \pkg{adaptDA} with tuning parameters: \itemize{ \item Model Type (\code{model}, character) } \strong{Adaptive-Network-Based Fuzzy Inference System} (\code{method = 'ANFIS'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Max. Iterations (\code{max.iter}, numeric) } \strong{Bagged AdaBoost} (\code{method = 'AdaBag'}) For classification using packages \pkg{adabag} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Trees (\code{mfinal}, numeric) \item Max Tree Depth (\code{maxdepth}, numeric) } \strong{Bagged CART} (\code{method = 'treebag'}) For classification and regression using packages \pkg{ipred} and \pkg{plyr} with no tuning parameters \strong{Bagged FDA using gCV Pruning} (\code{method = 'bagFDAGCV'}) For classification using package \pkg{earth} with tuning parameters: \itemize{ \item Product Degree (\code{degree}, numeric) } \strong{Bagged Flexible Discriminant Analysis} (\code{method = 'bagFDA'}) For classification using packages \pkg{earth} and \pkg{mda} with tuning parameters: \itemize{ \item Product Degree (\code{degree}, numeric) \item Number of Terms (\code{nprune}, numeric) } \strong{Bagged Logic Regression} (\code{method = 'logicBag'}) For classification and regression using package \pkg{logicFS} with tuning parameters: \itemize{ \item Maximum Number of Leaves (\code{nleaves}, numeric) \item Number of Trees (\code{ntrees}, numeric) } \strong{Bagged MARS} (\code{method = 'bagEarth'}) For classification and regression using package \pkg{earth} with tuning parameters: \itemize{ \item Number of Terms (\code{nprune}, numeric) \item Product Degree (\code{degree}, numeric) } \strong{Bagged MARS using gCV Pruning} (\code{method = 'bagEarthGCV'}) For classification and regression using package \pkg{earth} with tuning parameters: \itemize{ \item Product Degree (\code{degree}, numeric) } \strong{Bagged Model} (\code{method = 'bag'}) For classification and regression using package \pkg{caret} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{vars}, numeric) } \strong{Bayesian Generalized Linear Model} (\code{method = 'bayesglm'}) For classification and regression using package \pkg{arm} with no tuning parameters \strong{Bayesian Regularized Neural Networks} (\code{method = 'brnn'}) For regression using package \pkg{brnn} with tuning parameters: \itemize{ \item Number of Neurons (\code{neurons}, numeric) } \strong{Binary Discriminant Analysis} (\code{method = 'binda'}) For classification using package \pkg{binda} with tuning parameters: \itemize{ \item Shrinkage Intensity (\code{lambda.freqs}, numeric) } \strong{Boosted Classification Trees} (\code{method = 'ada'}) For classification using packages \pkg{ada} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Trees (\code{iter}, numeric) \item Max Tree Depth (\code{maxdepth}, numeric) \item Learning Rate (\code{nu}, numeric) } \strong{Boosted Generalized Additive Model} (\code{method = 'gamboost'}) For classification and regression using package \pkg{mboost} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{mstop}, numeric) \item AIC Prune? (\code{prune}, character) } \strong{Boosted Generalized Linear Model} (\code{method = 'glmboost'}) For classification and regression using package \pkg{mboost} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{mstop}, numeric) \item AIC Prune? (\code{prune}, character) } \strong{Boosted Linear Model} (\code{method = 'bstLs'}) For classification and regression using packages \pkg{bst} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{mstop}, numeric) \item Shrinkage (\code{nu}, numeric) } \strong{Boosted Logistic Regression} (\code{method = 'LogitBoost'}) For classification using package \pkg{caTools} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{nIter}, numeric) } \strong{Boosted Smoothing Spline} (\code{method = 'bstSm'}) For classification and regression using packages \pkg{bst} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{mstop}, numeric) \item Shrinkage (\code{nu}, numeric) } \strong{Boosted Tree} (\code{method = 'blackboost'}) For classification and regression using packages \pkg{party}, \pkg{mboost} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Trees (\code{mstop}, numeric) \item Max Tree Depth (\code{maxdepth}, numeric) } \strong{Boosted Tree} (\code{method = 'bstTree'}) For classification and regression using packages \pkg{bst} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{mstop}, numeric) \item Max Tree Depth (\code{maxdepth}, numeric) \item Shrinkage (\code{nu}, numeric) } \strong{C4.5-like Trees} (\code{method = 'J48'}) For classification using package \pkg{RWeka} with tuning parameters: \itemize{ \item Confidence Threshold (\code{C}, numeric) } \strong{C5.0} (\code{method = 'C5.0'}) For classification using packages \pkg{C50} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{trials}, numeric) \item Model Type (\code{model}, character) \item Winnow (\code{winnow}, logical) } \strong{CART} (\code{method = 'rpart'}) For classification and regression using package \pkg{rpart} with tuning parameters: \itemize{ \item Complexity Parameter (\code{cp}, numeric) } \strong{CART} (\code{method = 'rpart2'}) For classification and regression using package \pkg{rpart} with tuning parameters: \itemize{ \item Max Tree Depth (\code{maxdepth}, numeric) } \strong{CHi-squared Automated Interaction Detection} (\code{method = 'chaid'}) For classification using package \pkg{CHAID} with tuning parameters: \itemize{ \item Merging Threshold (\code{alpha2}, numeric) \item Splitting former Merged Threshold (\code{alpha3}, numeric) \item Splitting former Merged Threshold (\code{alpha4}, numeric) } \strong{Conditional Inference Random Forest} (\code{method = 'cforest'}) For classification and regression using package \pkg{party} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Conditional Inference Tree} (\code{method = 'ctree'}) For classification and regression using package \pkg{party} with tuning parameters: \itemize{ \item 1 - P-Value Threshold (\code{mincriterion}, numeric) } \strong{Conditional Inference Tree} (\code{method = 'ctree2'}) For classification and regression using package \pkg{party} with tuning parameters: \itemize{ \item Max Tree Depth (\code{maxdepth}, numeric) } \strong{Cost-Sensitive C5.0} (\code{method = 'C5.0Cost'}) For classification using packages \pkg{C50} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{trials}, numeric) \item Model Type (\code{model}, character) \item Winnow (\code{winnow}, logical) \item Cost (\code{cost}, numeric) } \strong{Cost-Sensitive CART} (\code{method = 'rpartCost'}) For classification using package \pkg{rpart} with tuning parameters: \itemize{ \item Complexity Parameter (\code{cp}, numeric) \item Cost (\code{Cost}, numeric) } \strong{Cubist} (\code{method = 'cubist'}) For regression using package \pkg{Cubist} with tuning parameters: \itemize{ \item Number of Committees (\code{committees}, numeric) \item Number of Instances (\code{neighbors}, numeric) } \strong{Dynamic Evolving Neural-Fuzzy Inference System } (\code{method = 'DENFIS'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Threshold (\code{Dthr}, numeric) \item Max. Iterations (\code{max.iter}, numeric) } \strong{Elasticnet} (\code{method = 'enet'}) For regression using package \pkg{elasticnet} with tuning parameters: \itemize{ \item Fraction of Full Solution (\code{fraction}, numeric) \item Weight Decay (\code{lambda}, numeric) } \strong{Ensemble Partial Least Squares Regression} (\code{method = 'enpls'}) For regression using package \pkg{enpls} with tuning parameters: \itemize{ \item Max. Number of Components (\code{maxcomp}, numeric) } \strong{Ensemble Partial Least Squares Regression with Feature Selection} (\code{method = 'enpls.fs'}) For regression using package \pkg{enpls} with tuning parameters: \itemize{ \item Max. Number of Components (\code{maxcomp}, numeric) \item Importance Cutoff (\code{threshold}, numeric) } \strong{eXtreme Gradient Boosting} (\code{method = 'xgbLinear'}) For classification and regression using package \pkg{xgboost} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{nrounds}, numeric) \item L2 Regularization (\code{lambda}, numeric) \item L2 Regularization (\code{alpha}, numeric) } \strong{eXtreme Gradient Boosting} (\code{method = 'xgbTree'}) For classification and regression using packages \pkg{xgboost} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{nrounds}, numeric) \item Max Tree Depth (\code{max_depth}, numeric) \item Shrinkage (\code{eta}, numeric) } \strong{Extreme Learning Machine} (\code{method = 'elm'}) For classification and regression using package \pkg{elmNN} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{nhid}, numeric) \item Activation Function (\code{actfun}, character) } \strong{Factor-Based Linear Discriminant Analysis} (\code{method = 'RFlda'}) For classification using package \pkg{HiDimDA} with tuning parameters: \itemize{ \item Number of Factors (\code{q}, numeric) } \strong{Flexible Discriminant Analysis} (\code{method = 'fda'}) For classification using packages \pkg{earth} and \pkg{mda} with tuning parameters: \itemize{ \item Product Degree (\code{degree}, numeric) \item Number of Terms (\code{nprune}, numeric) } \strong{Fuzzy Inference Rules by Descent Method} (\code{method = 'FIR.DM'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Max. Iterations (\code{max.iter}, numeric) } \strong{Fuzzy Rules Using Chi's Method} (\code{method = 'FRBCS.CHI'}) For classification using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Membership Function (\code{type.mf}, character) } \strong{Fuzzy Rules Using Genetic Cooperative-Competitive Learning} (\code{method = 'GFS.GCCL'}) For classification using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Population Size (\code{popu.size}, numeric) \item Max. Generations (\code{max.gen}, numeric) } \strong{Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh} (\code{method = 'FH.GBML'}) For classification using package \pkg{frbs} with tuning parameters: \itemize{ \item Max. Number of Rules (\code{max.num.rule}, numeric) \item Population Size (\code{popu.size}, numeric) \item Max. Generations (\code{max.gen}, numeric) } \strong{Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment} (\code{method = 'SLAVE'}) For classification using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Max. Iterations (\code{max.iter}, numeric) \item Max. Generations (\code{max.gen}, numeric) } \strong{Fuzzy Rules via MOGUL} (\code{method = 'GFS.FR.MOGAL'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Max. Generations (\code{max.gen}, numeric) \item Max. Iterations (\code{max.iter}, numeric) \item Max. Tuning Iterations (\code{max.tune}, numeric) } \strong{Fuzzy Rules via Thrift} (\code{method = 'GFS.THRIFT'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Population Size (\code{popu.size}, numeric) \item Number of Fuzzy Labels (\code{num.labels}, numeric) \item Max. Generations (\code{max.gen}, numeric) } \strong{Fuzzy Rules with Weight Factor} (\code{method = 'FRBCS.W'}) For classification using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Membership Function (\code{type.mf}, character) } \strong{Gaussian Process} (\code{method = 'gaussprLinear'}) For classification and regression using package \pkg{kernlab} with no tuning parameters \strong{Gaussian Process with Polynomial Kernel} (\code{method = 'gaussprPoly'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Polynomial Degree (\code{degree}, numeric) \item Scale (\code{scale}, numeric) } \strong{Gaussian Process with Radial Basis Function Kernel} (\code{method = 'gaussprRadial'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Sigma (\code{sigma}, numeric) } \strong{Generalized Additive Model using LOESS} (\code{method = 'gamLoess'}) For classification and regression using package \pkg{gam} with tuning parameters: \itemize{ \item Span (\code{span}, numeric) \item Degree (\code{degree}, numeric) } \strong{Generalized Additive Model using Splines} (\code{method = 'gam'}) For classification and regression using package \pkg{mgcv} with tuning parameters: \itemize{ \item Feature Selection (\code{select}, logical) \item Method (\code{method}, character) } \strong{Generalized Additive Model using Splines} (\code{method = 'gamSpline'}) For classification and regression using package \pkg{gam} with tuning parameters: \itemize{ \item Degrees of Freedom (\code{df}, numeric) } \strong{Generalized Linear Model} (\code{method = 'glm'}) For classification and regression with no tuning parameters \strong{Generalized Linear Model with Stepwise Feature Selection} (\code{method = 'glmStepAIC'}) For classification and regression using package \pkg{MASS} with no tuning parameters \strong{Generalized Partial Least Squares} (\code{method = 'gpls'}) For classification using package \pkg{gpls} with tuning parameters: \itemize{ \item Number of Components (\code{K.prov}, numeric) } \strong{Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems} (\code{method = 'GFS.LT.RS'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Population Size (\code{popu.size}, numeric) \item Number of Fuzzy Labels (\code{num.labels}, numeric) \item Max. Generations (\code{max.gen}, numeric) } \strong{glmnet} (\code{method = 'glmnet'}) For classification and regression using package \pkg{glmnet} with tuning parameters: \itemize{ \item Mixing Percentage (\code{alpha}, numeric) \item Regularization Parameter (\code{lambda}, numeric) } \strong{Greedy Prototype Selection} (\code{method = 'protoclass'}) For classification using packages \pkg{proxy} and \pkg{protoclass} with tuning parameters: \itemize{ \item Ball Size (\code{eps}, numeric) \item Distance Order (\code{Minkowski}, numeric) } \strong{Heteroscedastic Discriminant Analysis} (\code{method = 'hda'}) For classification using package \pkg{hda} with tuning parameters: \itemize{ \item Gamma (\code{gamma}, numeric) \item Lambda (\code{lambda}, numeric) \item Dimension of the Discriminative Subspace (\code{newdim}, numeric) } \strong{High Dimensional Discriminant Analysis} (\code{method = 'hdda'}) For classification using package \pkg{HDclassif} with tuning parameters: \itemize{ \item Threshold (\code{threshold}, character) \item Model Type (\code{model}, numeric) } \strong{Hybrid Neural Fuzzy Inference System} (\code{method = 'HYFIS'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Max. Iterations (\code{max.iter}, numeric) } \strong{Independent Component Regression} (\code{method = 'icr'}) For regression using package \pkg{fastICA} with tuning parameters: \itemize{ \item Number of Components (\code{n.comp}, numeric) } \strong{k-Nearest Neighbors} (\code{method = 'kknn'}) For classification and regression using package \pkg{kknn} with tuning parameters: \itemize{ \item Max. Number of Neighbors (\code{kmax}, numeric) \item Distance (\code{distance}, numeric) \item Kernel (\code{kernel}, character) } \strong{k-Nearest Neighbors} (\code{method = 'knn'}) For classification and regression with tuning parameters: \itemize{ \item Number of Neighbors (\code{k}, numeric) } \strong{Learning Vector Quantization} (\code{method = 'lvq'}) For classification using package \pkg{class} with tuning parameters: \itemize{ \item Codebook Size (\code{size}, numeric) \item Number of Prototypes (\code{k}, numeric) } \strong{Least Angle Regression} (\code{method = 'lars'}) For regression using package \pkg{lars} with tuning parameters: \itemize{ \item Fraction (\code{fraction}, numeric) } \strong{Least Angle Regression} (\code{method = 'lars2'}) For regression using package \pkg{lars} with tuning parameters: \itemize{ \item Number of Steps (\code{step}, numeric) } \strong{Least Squares Support Vector Machine} (\code{method = 'lssvmLinear'}) For classification using package \pkg{kernlab} with no tuning parameters \strong{Least Squares Support Vector Machine with Polynomial Kernel} (\code{method = 'lssvmPoly'}) For classification using package \pkg{kernlab} with tuning parameters: \itemize{ \item Polynomial Degree (\code{degree}, numeric) \item Scale (\code{scale}, numeric) } \strong{Least Squares Support Vector Machine with Radial Basis Function Kernel} (\code{method = 'lssvmRadial'}) For classification using package \pkg{kernlab} with tuning parameters: \itemize{ \item Sigma (\code{sigma}, numeric) } \strong{Linear Discriminant Analysis} (\code{method = 'lda'}) For classification using package \pkg{MASS} with no tuning parameters \strong{Linear Discriminant Analysis} (\code{method = 'lda2'}) For classification using package \pkg{MASS} with tuning parameters: \itemize{ \item Number of Discriminant Functions (\code{dimen}, numeric) } \strong{Linear Discriminant Analysis with Stepwise Feature Selection} (\code{method = 'stepLDA'}) For classification using packages \pkg{klaR} and \pkg{MASS} with tuning parameters: \itemize{ \item Maximum Number of Variables (\code{maxvar}, numeric) \item Search Direction (\code{direction}, character) } \strong{Linear Regression} (\code{method = 'lm'}) For regression with no tuning parameters \strong{Linear Regression with Backwards Selection} (\code{method = 'leapBackward'}) For regression using package \pkg{leaps} with tuning parameters: \itemize{ \item Maximum Number of Predictors (\code{nvmax}, numeric) } \strong{Linear Regression with Forward Selection} (\code{method = 'leapForward'}) For regression using package \pkg{leaps} with tuning parameters: \itemize{ \item Maximum Number of Predictors (\code{nvmax}, numeric) } \strong{Linear Regression with Stepwise Selection} (\code{method = 'leapSeq'}) For regression using package \pkg{leaps} with tuning parameters: \itemize{ \item Maximum Number of Predictors (\code{nvmax}, numeric) } \strong{Linear Regression with Stepwise Selection} (\code{method = 'lmStepAIC'}) For regression using package \pkg{MASS} with no tuning parameters \strong{Logic Regression} (\code{method = 'logreg'}) For classification and regression using package \pkg{LogicReg} with tuning parameters: \itemize{ \item Maximum Number of Leaves (\code{treesize}, numeric) \item Number of Trees (\code{ntrees}, numeric) } \strong{Logistic Model Trees} (\code{method = 'LMT'}) For classification using package \pkg{RWeka} with tuning parameters: \itemize{ \item Number of Iteratons (\code{iter}, numeric) } \strong{Maximum Uncertainty Linear Discriminant Analysis} (\code{method = 'Mlda'}) For classification using package \pkg{HiDimDA} with no tuning parameters \strong{Mixture Discriminant Analysis} (\code{method = 'mda'}) For classification using package \pkg{mda} with tuning parameters: \itemize{ \item Number of Subclasses Per Class (\code{subclasses}, numeric) } \strong{Model Averaged Neural Network} (\code{method = 'avNNet'}) For classification and regression using package \pkg{nnet} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) \item Weight Decay (\code{decay}, numeric) \item Bagging (\code{bag}, logical) } \strong{Model Rules} (\code{method = 'M5Rules'}) For regression using package \pkg{RWeka} with tuning parameters: \itemize{ \item Pruned (\code{pruned}, character) \item Smoothed (\code{smoothed}, character) } \strong{Model Tree} (\code{method = 'M5'}) For regression using package \pkg{RWeka} with tuning parameters: \itemize{ \item Pruned (\code{pruned}, character) \item Smoothed (\code{smoothed}, character) \item Rules (\code{rules}, character) } \strong{Multi-Layer Perceptron} (\code{method = 'mlp'}) For classification and regression using package \pkg{RSNNS} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) } \strong{Multi-Layer Perceptron} (\code{method = 'mlpWeightDecay'}) For classification and regression using package \pkg{RSNNS} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) \item Weight Decay (\code{decay}, numeric) } \strong{Multivariate Adaptive Regression Spline} (\code{method = 'earth'}) For classification and regression using package \pkg{earth} with tuning parameters: \itemize{ \item Number of Terms (\code{nprune}, numeric) \item Product Degree (\code{degree}, numeric) } \strong{Multivariate Adaptive Regression Splines} (\code{method = 'gcvEarth'}) For classification and regression using package \pkg{earth} with tuning parameters: \itemize{ \item Product Degree (\code{degree}, numeric) } \strong{Naive Bayes} (\code{method = 'nb'}) For classification using package \pkg{klaR} with tuning parameters: \itemize{ \item Laplace Correction (\code{fL}, numeric) \item Distribution Type (\code{usekernel}, logical) } \strong{Nearest Shrunken Centroids} (\code{method = 'pam'}) For classification using package \pkg{pamr} with tuning parameters: \itemize{ \item Shrinkage Threshold (\code{threshold}, numeric) } \strong{Neural Network} (\code{method = 'neuralnet'}) For regression using package \pkg{neuralnet} with tuning parameters: \itemize{ \item Number of Hidden Units in Layer 1 (\code{layer1}, numeric) \item Number of Hidden Units in Layer 2 (\code{layer2}, numeric) \item Number of Hidden Units in Layer 3 (\code{layer3}, numeric) } \strong{Neural Network} (\code{method = 'nnet'}) For classification and regression using package \pkg{nnet} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) \item Weight Decay (\code{decay}, numeric) } \strong{Neural Networks with Feature Extraction} (\code{method = 'pcaNNet'}) For classification and regression using package \pkg{nnet} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) \item Weight Decay (\code{decay}, numeric) } \strong{Oblique Random Forest} (\code{method = 'ORFlog'}) For classification using package \pkg{obliqueRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Oblique Random Forest} (\code{method = 'ORFpls'}) For classification using package \pkg{obliqueRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Oblique Random Forest} (\code{method = 'ORFridge'}) For classification using package \pkg{obliqueRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Oblique Random Forest} (\code{method = 'ORFsvm'}) For classification using package \pkg{obliqueRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Oblique Trees} (\code{method = 'oblique.tree'}) For classification using package \pkg{oblique.tree} with tuning parameters: \itemize{ \item Oblique Splits (\code{oblique.splits}, character) \item Variable Selection Method (\code{variable.selection}, character) } \strong{Ordered Logistic or Probit Regression} (\code{method = 'polr'}) For classification using package \pkg{MASS} with no tuning parameters \strong{Parallel Random Forest} (\code{method = 'parRF'}) For classification and regression using package \pkg{randomForest} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{partDSA} (\code{method = 'partDSA'}) For classification and regression using package \pkg{partDSA} with tuning parameters: \itemize{ \item Number of Terminal Partitions (\code{cut.off.growth}, numeric) \item Minimum Percent Difference (\code{MPD}, numeric) } \strong{Partial Least Squares} (\code{method = 'kernelpls'}) For classification and regression using package \pkg{pls} with tuning parameters: \itemize{ \item Number of Components (\code{ncomp}, numeric) } \strong{Partial Least Squares} (\code{method = 'pls'}) For classification and regression using package \pkg{pls} with tuning parameters: \itemize{ \item Number of Components (\code{ncomp}, numeric) } \strong{Partial Least Squares} (\code{method = 'simpls'}) For classification and regression using package \pkg{pls} with tuning parameters: \itemize{ \item Number of Components (\code{ncomp}, numeric) } \strong{Partial Least Squares} (\code{method = 'widekernelpls'}) For classification and regression using package \pkg{pls} with tuning parameters: \itemize{ \item Number of Components (\code{ncomp}, numeric) } \strong{Partial Least Squares Generalized Linear Models } (\code{method = 'plsRglm'}) For classification and regression using package \pkg{plsRglm} with tuning parameters: \itemize{ \item Number of PLS Components (\code{nt}, numeric) \item p-Value threshold (\code{alpha.pvals.expli}, numeric) } \strong{Penalized Discriminant Analysis} (\code{method = 'pda'}) For classification using package \pkg{mda} with tuning parameters: \itemize{ \item Shrinkage Penalty Coefficient (\code{lambda}, numeric) } \strong{Penalized Discriminant Analysis} (\code{method = 'pda2'}) For classification using package \pkg{mda} with tuning parameters: \itemize{ \item Degrees of Freedom (\code{df}, numeric) } \strong{Penalized Linear Discriminant Analysis} (\code{method = 'PenalizedLDA'}) For classification using packages \pkg{penalizedLDA} and \pkg{plyr} with tuning parameters: \itemize{ \item L1 Penalty (\code{lambda}, numeric) \item Number of Discriminant Functions (\code{K}, numeric) } \strong{Penalized Linear Regression} (\code{method = 'penalized'}) For regression using package \pkg{penalized} with tuning parameters: \itemize{ \item L1 Penalty (\code{lambda1}, numeric) \item L2 Penalty (\code{lambda2}, numeric) } \strong{Penalized Logistic Regression} (\code{method = 'plr'}) For classification using package \pkg{stepPlr} with tuning parameters: \itemize{ \item L2 Penalty (\code{lambda}, numeric) \item Complexity Parameter (\code{cp}, character) } \strong{Penalized Multinomial Regression} (\code{method = 'multinom'}) For classification using package \pkg{nnet} with tuning parameters: \itemize{ \item Weight Decay (\code{decay}, numeric) } \strong{Polynomial Kernel Regularized Least Squares} (\code{method = 'krlsPoly'}) For regression using package \pkg{KRLS} with tuning parameters: \itemize{ \item Regularization Parameter (\code{lambda}, numeric) \item Polynomial Degree (\code{degree}, numeric) } \strong{Principal Component Analysis} (\code{method = 'pcr'}) For regression using package \pkg{pls} with tuning parameters: \itemize{ \item Number of Components (\code{ncomp}, numeric) } \strong{Projection Pursuit Regression} (\code{method = 'ppr'}) For regression with tuning parameters: \itemize{ \item Number of Terms (\code{nterms}, numeric) } \strong{Quadratic Discriminant Analysis} (\code{method = 'qda'}) For classification using package \pkg{MASS} with no tuning parameters \strong{Quadratic Discriminant Analysis with Stepwise Feature Selection} (\code{method = 'stepQDA'}) For classification using packages \pkg{klaR} and \pkg{MASS} with tuning parameters: \itemize{ \item Maximum Number of Variables (\code{maxvar}, numeric) \item Search Direction (\code{direction}, character) } \strong{Quantile Random Forest} (\code{method = 'qrf'}) For regression using package \pkg{quantregForest} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Quantile Regression Neural Network} (\code{method = 'qrnn'}) For regression using package \pkg{qrnn} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{n.hidden}, numeric) \item Weight Decay (\code{penalty}, numeric) \item Bagged Models? (\code{bag}, logical) } \strong{Radial Basis Function Kernel Regularized Least Squares} (\code{method = 'krlsRadial'}) For regression using packages \pkg{KRLS} and \pkg{kernlab} with tuning parameters: \itemize{ \item Regularization Parameter (\code{lambda}, numeric) \item Sigma (\code{sigma}, numeric) } \strong{Radial Basis Function Network} (\code{method = 'rbf'}) For classification and regression using package \pkg{RSNNS} with tuning parameters: \itemize{ \item Number of Hidden Units (\code{size}, numeric) } \strong{Radial Basis Function Network} (\code{method = 'rbfDDA'}) For classification and regression using package \pkg{RSNNS} with tuning parameters: \itemize{ \item Activation Limit for Conflicting Classes (\code{negativeThreshold}, numeric) } \strong{Random Ferns} (\code{method = 'rFerns'}) For classification using package \pkg{rFerns} with tuning parameters: \itemize{ \item Fern Depth (\code{depth}, numeric) } \strong{Random Forest} (\code{method = 'rf'}) For classification and regression using package \pkg{randomForest} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Random Forest by Randomization} (\code{method = 'extraTrees'}) For classification and regression using package \pkg{extraTrees} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) \item Number of Random Cuts (\code{numRandomCuts}, numeric) } \strong{Random Forest with Additional Feature Selection} (\code{method = 'Boruta'}) For classification and regression using packages \pkg{Boruta} and \pkg{randomForest} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Random k-Nearest Neighbors} (\code{method = 'rknn'}) For classification and regression using package \pkg{rknn} with tuning parameters: \itemize{ \item Number of Neighbors (\code{k}, numeric) \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } \strong{Random k-Nearest Neighbors with Feature Selection} (\code{method = 'rknnBel'}) For classification and regression using packages \pkg{rknn} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Neighbors (\code{k}, numeric) \item Number of Randomly Selected Predictors (\code{mtry}, numeric) \item Number of Features Dropped (\code{d}, numeric) } \strong{Regularized Discriminant Analysis} (\code{method = 'rda'}) For classification using package \pkg{klaR} with tuning parameters: \itemize{ \item Gamma (\code{gamma}, numeric) \item Lambda (\code{lambda}, numeric) } \strong{Regularized Random Forest} (\code{method = 'RRF'}) For classification and regression using packages \pkg{randomForest} and \pkg{RRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) \item Regularization Value (\code{coefReg}, numeric) \item Importance Coefficient (\code{coefImp}, numeric) } \strong{Regularized Random Forest} (\code{method = 'RRFglobal'}) For classification and regression using package \pkg{RRF} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) \item Regularization Value (\code{coefReg}, numeric) } \strong{Relaxed Lasso} (\code{method = 'relaxo'}) For regression using packages \pkg{relaxo} and \pkg{plyr} with tuning parameters: \itemize{ \item Penalty Parameter (\code{lambda}, numeric) \item Relaxation Parameter (\code{phi}, numeric) } \strong{Relevance Vector Machines with Linear Kernel} (\code{method = 'rvmLinear'}) For regression using package \pkg{kernlab} with no tuning parameters \strong{Relevance Vector Machines with Polynomial Kernel} (\code{method = 'rvmPoly'}) For regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Scale (\code{scale}, numeric) \item Polynomial Degree (\code{degree}, numeric) } \strong{Relevance Vector Machines with Radial Basis Function Kernel} (\code{method = 'rvmRadial'}) For regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Sigma (\code{sigma}, numeric) } \strong{Ridge Regression} (\code{method = 'ridge'}) For regression using package \pkg{elasticnet} with tuning parameters: \itemize{ \item Weight Decay (\code{lambda}, numeric) } \strong{Ridge Regression with Variable Selection} (\code{method = 'foba'}) For regression using package \pkg{foba} with tuning parameters: \itemize{ \item Number of Variables Retained (\code{k}, numeric) \item L2 Penalty (\code{lambda}, numeric) } \strong{Robust Linear Discriminant Analysis} (\code{method = 'Linda'}) For classification using package \pkg{rrcov} with no tuning parameters \strong{Robust Linear Model} (\code{method = 'rlm'}) For regression using package \pkg{MASS} with no tuning parameters \strong{Robust Mixture Discriminant Analysis} (\code{method = 'rmda'}) For classification using package \pkg{robustDA} with tuning parameters: \itemize{ \item Number of Subclasses Per Class (\code{K}, numeric) \item Model (\code{model}, character) } \strong{Robust Quadratic Discriminant Analysis} (\code{method = 'QdaCov'}) For classification using package \pkg{rrcov} with no tuning parameters \strong{Robust Regularized Linear Discriminant Analysis} (\code{method = 'rrlda'}) For classification using package \pkg{rrlda} with tuning parameters: \itemize{ \item Penalty Parameter (\code{lambda}, numeric) \item Robustness Parameter (\code{hp}, numeric) \item Penalty Type (\code{penalty}, character) } \strong{Robust SIMCA} (\code{method = 'RSimca'}) For classification using package \pkg{rrcovHD} with no tuning parameters \strong{ROC-Based Classifier} (\code{method = 'rocc'}) For classification using package \pkg{rocc} with tuning parameters: \itemize{ \item Number of Variables Retained (\code{xgenes}, numeric) } \strong{Rule-Based Classifier} (\code{method = 'JRip'}) For classification using package \pkg{RWeka} with tuning parameters: \itemize{ \item Number of Optimizations (\code{NumOpt}, numeric) } \strong{Rule-Based Classifier} (\code{method = 'PART'}) For classification using package \pkg{RWeka} with tuning parameters: \itemize{ \item Confidence Threshold (\code{threshold}, numeric) \item Confidence Threshold (\code{pruned}, character) } \strong{Self-Organizing Map} (\code{method = 'bdk'}) For classification and regression using package \pkg{kohonen} with tuning parameters: \itemize{ \item Row (\code{xdim}, numeric) \item Columns (\code{ydim}, numeric) \item X Weight (\code{xweight}, numeric) \item Topology (\code{topo}, character) } \strong{Self-Organizing Maps} (\code{method = 'xyf'}) For classification and regression using package \pkg{kohonen} with tuning parameters: \itemize{ \item Row (\code{xdim}, numeric) \item Columns (\code{ydim}, numeric) \item X Weight (\code{xweight}, numeric) \item Topology (\code{topo}, character) } \strong{Shrinkage Discriminant Analysis} (\code{method = 'sda'}) For classification using package \pkg{sda} with tuning parameters: \itemize{ \item Diagonalize (\code{diagonal}, logical) \item shrinkage (\code{lambda}, numeric) } \strong{SIMCA} (\code{method = 'CSimca'}) For classification using package \pkg{rrcovHD} with no tuning parameters \strong{Simplified TSK Fuzzy Rules} (\code{method = 'FS.HGD'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Max. Iterations (\code{max.iter}, numeric) } \strong{Single C5.0 Ruleset} (\code{method = 'C5.0Rules'}) For classification using package \pkg{C50} with no tuning parameters \strong{Single C5.0 Tree} (\code{method = 'C5.0Tree'}) For classification using package \pkg{C50} with no tuning parameters \strong{Single Rule Classification} (\code{method = 'OneR'}) For classification using package \pkg{RWeka} with no tuning parameters \strong{Sparse Linear Discriminant Analysis} (\code{method = 'sparseLDA'}) For classification using package \pkg{sparseLDA} with tuning parameters: \itemize{ \item Number of Predictors (\code{NumVars}, numeric) \item Lambda (\code{lambda}, numeric) } \strong{Sparse Mixture Discriminant Analysis} (\code{method = 'smda'}) For classification using package \pkg{sparseLDA} with tuning parameters: \itemize{ \item Number of Predictors (\code{NumVars}, numeric) \item Lambda (\code{lambda}, numeric) \item Number of Subclasses (\code{R}, numeric) } \strong{Sparse Partial Least Squares} (\code{method = 'spls'}) For classification and regression using package \pkg{spls} with tuning parameters: \itemize{ \item Number of Components (\code{K}, numeric) \item Threshold (\code{eta}, numeric) \item Kappa (\code{kappa}, numeric) } \strong{Stabilized Linear Discriminant Analysis} (\code{method = 'slda'}) For classification using package \pkg{ipred} with no tuning parameters \strong{Stacked AutoEncoder Deep Neural Network} (\code{method = 'dnn'}) For classification and regression using package \pkg{deepnet} with tuning parameters: \itemize{ \item Hidden Layer 1 (\code{layer1}, numeric) \item Hidden Layer 2 (\code{layer2}, numeric) \item Hidden Layer 3 (\code{layer3}, numeric) \item Hidden Dropouts (\code{hidden_dropout}, numeric) \item Visible Dropout (\code{visible_dropout}, numeric) } \strong{Stepwise Diagonal Linear Discriminant Analysis} (\code{method = 'sddaLDA'}) For classification using package \pkg{SDDA} with no tuning parameters \strong{Stepwise Diagonal Quadratic Discriminant Analysis} (\code{method = 'sddaQDA'}) For classification using package \pkg{SDDA} with no tuning parameters \strong{Stochastic Gradient Boosting} (\code{method = 'gbm'}) For classification and regression using packages \pkg{gbm} and \pkg{plyr} with tuning parameters: \itemize{ \item Number of Boosting Iterations (\code{n.trees}, numeric) \item Max Tree Depth (\code{interaction.depth}, numeric) \item Shrinkage (\code{shrinkage}, numeric) \item Min. Terminal Node Size (\code{n.minobsinnode}, numeric) } \strong{Subtractive Clustering and Fuzzy c-Means Rules} (\code{method = 'SBC'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Radius (\code{r.a}, numeric) \item Upper Threshold (\code{eps.high}, numeric) \item Lower Threshold (\code{eps.low}, numeric) } \strong{Supervised Principal Component Analysis} (\code{method = 'superpc'}) For regression using package \pkg{superpc} with tuning parameters: \itemize{ \item Threshold (\code{threshold}, numeric) \item Number of Components (\code{n.components}, numeric) } \strong{Support Vector Machines with Boundrange String Kernel} (\code{method = 'svmBoundrangeString'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item length (\code{length}, numeric) \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Class Weights} (\code{method = 'svmRadialWeights'}) For classification using package \pkg{kernlab} with tuning parameters: \itemize{ \item Sigma (\code{sigma}, numeric) \item Cost (\code{C}, numeric) \item Weight (\code{Weight}, numeric) } \strong{Support Vector Machines with Exponential String Kernel} (\code{method = 'svmExpoString'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item lambda (\code{lambda}, numeric) \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Linear Kernel} (\code{method = 'svmLinear'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Polynomial Kernel} (\code{method = 'svmPoly'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Polynomial Degree (\code{degree}, numeric) \item Scale (\code{scale}, numeric) \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Radial Basis Function Kernel} (\code{method = 'svmRadial'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Sigma (\code{sigma}, numeric) \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Radial Basis Function Kernel} (\code{method = 'svmRadialCost'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item Cost (\code{C}, numeric) } \strong{Support Vector Machines with Spectrum String Kernel} (\code{method = 'svmSpectrumString'}) For classification and regression using package \pkg{kernlab} with tuning parameters: \itemize{ \item length (\code{length}, numeric) \item Cost (\code{C}, numeric) } \strong{The lasso} (\code{method = 'lasso'}) For regression using package \pkg{elasticnet} with tuning parameters: \itemize{ \item Fraction of Full Solution (\code{fraction}, numeric) } \strong{Tree Models from Genetic Algorithms} (\code{method = 'evtree'}) For classification and regression using package \pkg{evtree} with tuning parameters: \itemize{ \item Complexity Parameter (\code{alpha}, numeric) } \strong{Tree-Based Ensembles} (\code{method = 'nodeHarvest'}) For classification and regression using package \pkg{nodeHarvest} with tuning parameters: \itemize{ \item Maximum Interaction Depth (\code{maxinter}, numeric) \item Prediction Mode (\code{mode}, character) } \strong{Variational Bayesian Multinomial Probit Regression} (\code{method = 'vbmpRadial'}) For classification using package \pkg{vbmp} with tuning parameters: \itemize{ \item Theta Estimated (\code{estimateTheta}, character) } \strong{Wang and Mendel Fuzzy Rules} (\code{method = 'WM'}) For regression using package \pkg{frbs} with tuning parameters: \itemize{ \item Number of Fuzzy Terms (\code{num.labels}, numeric) \item Membership Function (\code{type.mf}, character) } \strong{Weighted Subspace Random Forest} (\code{method = 'wsrf'}) For classification using package \pkg{wsrf} with tuning parameters: \itemize{ \item Number of Randomly Selected Predictors (\code{mtry}, numeric) } } \references{``Using your own model in \code{\link{train}}'' (\url{http://caret.r-forge.r-project.org/custom_models.html})} \keyword{models} caret/man/resampleSummary.Rd0000644000175000017500000000236712453550065016271 0ustar rbalintrbalint\name{resampleSummary} \alias{resampleSummary} \title{Summary of resampled performance estimates} \description{ This function uses the out-of-bag predictions to calculate overall performance metrics and returns the observed and predicted data. } \usage{ resampleSummary(obs, resampled, index = NULL, keepData = TRUE) } \arguments{ \item{obs}{A vector (numeric or factor) of the outcome data} \item{resampled}{ For bootstrapping, this is either a matrix (for numeric outcomes) or a data frame (for factors). For cross-validation, a vector is produced. } \item{index}{The list to index of samples in each cross--validation fold (only used for cross-validation).} \item{keepData}{A logical for returning the observed and predicted data.} } \details{ The mean and standard deviation of the values produced by \code{\link{postResample}} are calculated.} \value{ A list with: \item{metrics }{A vector of values describing the bootstrap distribution.} \item{data }{A data frame or \code{NULL}. Columns include \code{obs}, \code{pred} and \code{group} (for tracking cross-validation folds or bootstrap samples)} } \author{Max Kuhn} \seealso{\code{\link{postResample}}} \examples{ resampleSummary(rnorm(10), matrix(rnorm(50), ncol = 5)) } \keyword{utilities} caret/man/plsda.Rd0000644000175000017500000001160312453550065014177 0ustar rbalintrbalint\name{plsda} \alias{plsda.default} \alias{predict.plsda} \alias{plsda} \alias{splsda.default} \alias{predict.splsda} \alias{splsda} \title{Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis} \description{ \code{plsda} is used to fit standard PLS models for classification while \code{splsda} performs sparse PLS that embeds feature selection and regularization for the same purpose. } \usage{ plsda(x, ...) \method{plsda}{default}(x, y, ncomp = 2, probMethod = "softmax", prior = NULL, ...) \method{predict}{plsda}(object, newdata = NULL, ncomp = NULL, type = "class", ...) splsda(x, ...) \method{splsda}{default}(x, y, probMethod = "softmax", prior = NULL, ...) \method{predict}{splsda}(object, newdata = NULL, type = "class", ...) } \arguments{ \item{x}{a matrix or data frame of predictors} \item{y}{a factor or indicator matrix for the discrete outcome. If a matrix, the entries must be either 0 or 1 and rows must sum to one} \item{ncomp}{the number of components to include in the model. Predictions can be made for models with values less than \code{ncomp}. } \item{probMethod}{either "softmax" or "Bayes" (see Details)} \item{prior}{a vector or prior probabilities for the classes (only used for \code{probeMethod = "Bayes"})} \item{\dots}{arguments to pass to \code{\link[pls:mvr]{plsr}} or \code{\link[spls]{spls}}. For \code{splsda}, this is the method for passing tuning parameters specifications (e.g. \code{K}, \code{eta} or \code{kappa})} \item{object}{an object produced by \code{plsda}} \item{newdata}{a matrix or data frame of predictors} \item{type}{either \code{"class"}, \code{"prob"} or \code{"raw"} to produce the predicted class, class probabilities or the raw model scores, respectively.} } \details{ If a factor is supplied, the appropriate indicator matrix is created. A multivariate PLS model is fit to the indicator matrix using the \code{\link[pls:mvr]{plsr}} or \code{\link[spls]{spls}} function. Two prediction methods can be used. The \bold{softmax function} transforms the model predictions to "probability-like" values (e.g. on [0, 1] and sum to 1). The class with the largest class probability is the predicted class. Also, \bold{Bayes rule} can be applied to the model predictions to form posterior probabilities. Here, the model predictions for the training set are used along with the training set outcomes to create conditional distributions for each class. When new samples are predicted, the raw model predictions are run through these conditional distributions to produce a posterior probability for each class (along with the prior). This process is repeated \code{ncomp} times for every possible PLS model. The \code{\link[klaR]{NaiveBayes}} function is used with \code{usekernel = TRUE} for the posterior probability calculations. } \value{ For \code{plsda}, an object of class "plsda" and "mvr". For \code{splsda}, an object of class \code{splsda}. The predict methods produce either a vector, matrix or three-dimensional array, depending on the values of \code{type} of \code{ncomp}. For example, specifying more than one value of \code{ncomp} with \code{type = "class"} with produce a three dimensional array but the default specification would produce a factor vector. } \seealso{\code{\link[pls:mvr]{plsr}}, \code{\link[spls]{spls}}} \examples{ \dontrun{ data(mdrr) set.seed(1) inTrain <- sample(seq(along = mdrrClass), 450) nzv <- nearZeroVar(mdrrDescr) filteredDescr <- mdrrDescr[, -nzv] training <- filteredDescr[inTrain,] test <- filteredDescr[-inTrain,] trainMDRR <- mdrrClass[inTrain] testMDRR <- mdrrClass[-inTrain] preProcValues <- preProcess(training) trainDescr <- predict(preProcValues, training) testDescr <- predict(preProcValues, test) useBayes <- plsda(trainDescr, trainMDRR, ncomp = 5, probMethod = "Bayes") useSoftmax <- plsda(trainDescr, trainMDRR, ncomp = 5) confusionMatrix(predict(useBayes, testDescr), testMDRR) confusionMatrix(predict(useSoftmax, testDescr), testMDRR) histogram(~predict(useBayes, testDescr, type = "prob")[,"Active",] | testMDRR, xlab = "Active Prob", xlim = c(-.1,1.1)) histogram(~predict(useSoftmax, testDescr, type = "prob")[,"Active",] | testMDRR, xlab = "Active Prob", xlim = c(-.1,1.1)) ## different sized objects are returned length(predict(useBayes, testDescr)) dim(predict(useBayes, testDescr, ncomp = 1:3)) dim(predict(useBayes, testDescr, type = "prob")) dim(predict(useBayes, testDescr, type = "prob", ncomp = 1:3)) ## Using spls: ## (As of 11/09, the spls package now has a similar function with ## the same mane. To avoid conflicts, use caret:::splsda to ## get this version) splsFit <- caret:::splsda(trainDescr, trainMDRR, K = 5, eta = .9, probMethod = "Bayes") confusionMatrix(caret:::predict.splsda(splsFit, testDescr), testMDRR) } } \keyword{models} caret/man/panel.needle.Rd0000644000175000017500000000252412453550065015430 0ustar rbalintrbalint\name{panel.needle} \alias{panel.needle} \title{Needle Plot Lattice Panel} \description{ A variation of \code{panel.dotplot} that plots horizontal lines from zero to the data point. } \usage{ panel.needle(x, y, horizontal = TRUE, pch, col, lty, lwd, col.line, levels.fos, groups = NULL, \dots) } \arguments{ \item{x,y}{ variables to be plotted in the panel. Typically y is the 'factor' } \item{horizontal}{ logical. If FALSE, the plot is `transposed' in the sense that the behaviours of x and y are switched. x is now the `factor'. Interpretation of other arguments change accordingly. See documentation of \code{bwplot} for a fuller explanation. } \item{pch, col, lty, lwd, col.line}{ graphical parameters} \item{levels.fos}{ locations where reference lines will be drawn } \item{groups}{ grouping variable (affects graphical parameters)} \item{\dots}{ extra parameters, passed to \code{panel.xyplot} which is responsible for drawing the foreground points (\code{panel.dotplot} only draws the background reference lines). } } \details{ Creates (possibly grouped) needleplot of \code{x} against \code{y} or vice versa } \seealso{\code{\link{dotplot}}} \author{Max Kuhn, based on \code{\link[lattice]{panel.dotplot}} by Deepayan Sarkar} \keyword{graphs} caret/man/cars.Rd0000644000175000017500000000155312453550065014027 0ustar rbalintrbalint\name{cars} \docType{data} \alias{cars} \title{Kelly Blue Book resale data for 2005 model year GM cars} \description{ Kuiper (2008) collected data on Kelly Blue Book resale data for 804 GM cars (2005 model year). } \usage{data(cars)} \value{ \item{cars}{data frame of the suggested retail price (column \code{Price}) and various characteristics of each car (columns \code{Mileage}, \code{Cylinder}, \code{Doors}, \code{Cruise}, \code{Sound}, \code{Leather}, \code{Buick}, \code{Cadillac}, \code{Chevy}, \code{Pontiac}, \code{Saab}, \code{Saturn}, \code{convertible}, \code{coupe}, \code{hatchback}, \code{sedan} and \code{wagon})} } \source{Kuiper, S. (2008). Introduction to Multiple Regression: How Much Is Your Car Worth?, \emph{Journal of Statistics Education}, Vol. 16, \url{www.amstat.org/publications/jse/v16n3/datasets.kuiper.html} } \keyword{datasets} caret/man/dummyVars.Rd0000644000175000017500000001355112516214271015063 0ustar rbalintrbalint\name{dummyVars} \alias{dummyVars} \alias{dummyVars.default} \alias{predict.dummyVars} \alias{contr.dummy} \alias{contr.ltfr} \title{ Create A Full Set of Dummy Variables } \description{ \code{dummyVars} creates a full set of dummy variables (i.e. less than full rank parameterization) } \usage{ dummyVars(formula, ...) \method{dummyVars}{default}(formula, data, sep = ".", levelsOnly = FALSE, fullRank = FALSE, ...) \method{predict}{dummyVars}(object, newdata, na.action = na.pass, ...) contr.dummy(n, ...) ## DEPRECATED contr.ltfr(n, contrasts = TRUE, sparse = FALSE) } \arguments{ \item{formula}{ An appropriate R model formula, see References } \item{data}{ A data frame with the predictors of interest } \item{sep}{ An optional separator between factor variable names and their levels. Use \code{sep = NULL} for no separator (i.e. normal behavior of \code{\link[stats]{model.matrix}} as shown in the Details section) } \item{levelsOnly}{ A logical; \code{TRUE} means to completely remove the variable names from the column names } \item{fullRank}{ A logical; should a full rank or less than full rank parameterization be used? If \code{TRUE}, factors are encoded to be consistent with \code{\link[stats]{model.matrix}} and the resulting there are no linear dependencies induced between the columns. } \item{object}{ An object of class \code{dummyVars} } \item{newdata}{ A data frame with the required columns } \item{na.action}{ A function determining what should be done with missing values in \code{newdata}. The default is to predict \code{NA}. } \item{n}{ A vector of levels for a factor, or the number of levels. } \item{contrasts}{ A logical indicating whether contrasts should be computed. } \item{sparse}{ A logical indicating if the result should be sparse. } \item{...}{additional arguments to be passed to other methods} } \details{ Most of the \code{\link[stats]{contrasts}} functions in R produce full rank parameterizations of the predictor data. For example, \code{\link[stats]{contr.treatment}} creates a reference cell in the data and defines dummy variables for all factor levels except those in the reference cell. For example, if a factor with 5 levels is used in a model formula alone, \code{\link[stats]{contr.treatment}} creates columns for the intercept and all the factor levels except the first level of the factor. For the data in the Example section below, this would produce: \preformatted{ (Intercept) dayTue dayWed dayThu dayFri daySat daySun 1 1 1 0 0 0 0 0 2 1 1 0 0 0 0 0 3 1 1 0 0 0 0 0 4 1 0 0 1 0 0 0 5 1 0 0 1 0 0 0 6 1 0 0 0 0 0 0 7 1 0 1 0 0 0 0 8 1 0 1 0 0 0 0 9 1 0 0 0 0 0 0 } In some situations, there may be a need for dummy variables for all the levels of the factor. For the same example: \preformatted{ dayMon dayTue dayWed dayThu dayFri daySat daySun 1 0 1 0 0 0 0 0 2 0 1 0 0 0 0 0 3 0 1 0 0 0 0 0 4 0 0 0 1 0 0 0 5 0 0 0 1 0 0 0 6 1 0 0 0 0 0 0 7 0 0 1 0 0 0 0 8 0 0 1 0 0 0 0 9 1 0 0 0 0 0 0 } Given a formula and initial data set, the class \code{dummyVars} gathers all the information needed to produce a full set of dummy variables for any data set. It uses \code{contr.ltfr} as the base function to do this. } \value{ The output of \code{dummyVars} is a list of class 'dummyVars' with elements \item{call }{the function call} \item{form }{the model formula} \item{vars }{names of all the variables in the model} \item{facVars }{names of all the factor variables in the model} \item{lvls }{levels of any factor variables} \item{sep }{\code{NULL} or a character separator} \item{terms }{the \code{\link[stats]{terms.formula}} object} \item{levelsOnly }{a logical} The \code{predict} function produces a data frame. \code{contr.ltfr} generates a design matrix. } \author{ \code{contr.ltfr} is a small modification of \code{\link[stats]{contr.treatment}} by Max Kuhn } \references{ \url{http://cran.r-project.org/doc/manuals/R-intro.html#Formulae-for-statistical-models} } \seealso{ \code{\link[stats]{model.matrix}}, \code{\link[stats]{contrasts}}, \code{\link[stats]{formula}} } \examples{ when <- data.frame(time = c("afternoon", "night", "afternoon", "morning", "morning", "morning", "morning", "afternoon", "afternoon"), day = c("Mon", "Mon", "Mon", "Wed", "Wed", "Fri", "Sat", "Sat", "Fri")) levels(when$time) <- list(morning="morning", afternoon="afternoon", night="night") levels(when$day) <- list(Mon="Mon", Tue="Tue", Wed="Wed", Thu="Thu", Fri="Fri", Sat="Sat", Sun="Sun") ## Default behavior: model.matrix(~day, when) mainEffects <- dummyVars(~ day + time, data = when) mainEffects predict(mainEffects, when[1:3,]) when2 <- when when2[1, 1] <- NA predict(mainEffects, when2[1:3,]) predict(mainEffects, when2[1:3,], na.action = na.omit) interactionModel <- dummyVars(~ day + time + day:time, data = when, sep = ".") predict(interactionModel, when[1:3,]) noNames <- dummyVars(~ day + time + day:time, data = when, levelsOnly = TRUE) predict(noNames, when) } \keyword{ models} caret/man/dhfr.Rd0000644000175000017500000000164012453550065014017 0ustar rbalintrbalint\name{dhfr} \docType{data} \alias{dhfr} \title{Dihydrofolate Reductase Inhibitors Data} \description{ Sutherland and Weaver (2004) discuss QSAR models for dihydrofolate reductase (DHFR) inhibition. This data set contains values for 325 compounds. For each compound, 228 molecular descriptors have been calculated. Additionally, each samples is designated as "active" or "inactive". The data frame \code{dhfr} contains a column called \code{Y} with the outcome classification. The remainder of the columns are molecular descriptor values. } \usage{data(dhfr)} \value{ \item{dhfr}{data frame of chemical descriptors and the activity values} } \source{Sutherland, J.J. and Weaver, D.F. (2004). Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors, \emph{Journal of Computer-Aided Molecular Design}, Vol. 18, pg. 309--331. } \keyword{datasets} caret/man/plot.train.Rd0000644000175000017500000000637712453550065015202 0ustar rbalintrbalint\name{plot.train} \alias{plot.train} \alias{ggplot.train} \title{Plot Method for the train Class} \description{ This function takes the output of a \code{\link{train}} object and creates a line or level plot using the \pkg{lattice} or \pkg{ggplot2} libraries. } \usage{ \method{plot}{train}(x, plotType = "scatter", metric = x$metric[1], digits = getOption("digits") - 3, xTrans = NULL, nameInStrip = FALSE, ...) \method{ggplot}{train}(data = NULL, metric = data$metric[1], plotType = "scatter", output = "layered", nameInStrip = FALSE, ...) } \arguments{ \item{x}{an object of class \code{\link{train}}.} \item{metric}{What measure of performance to plot. Examples of possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be used depending on what metrics have been calculated.} \item{plotType}{a string describing the type of plot (\code{"scatter"}, \code{"level"} or \code{"line"} (\code{plot} only))} \item{digits}{an integer specifying the number of significant digits used to label the parameter value.} \item{xTrans}{a function that will be used to scale the x-axis in scatter plots.} \item{data}{an object of class \code{\link{train}}.} \item{output}{either "data", "ggplot" or "layered". The first returns a data frame while the second returns a simple \code{ggplot} object with no layers. The third value returns a plot with a set of layers. } \item{nameInStrip}{a logical: if there are more than 2 tuning parameters, should the name and value be included in the panel title?} \item{\dots}{\code{plot} only: specifications to be passed to \code{\link[lattice]{levelplot}}, \code{\link[lattice]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}} (for line plots). The function automatically sets some arguments (e.g. axis labels) but passing in values here will over-ride the defaults } } \details{ If there are no tuning parameters, or none were varied, an error is produced. If the model has one tuning parameter with multiple candidate values, a plot is produced showing the profile of the results over the parameter. Also, a plot can be produced if there are multiple tuning parameters but only one is varied. If there are two tuning parameters with different values, a plot can be produced where a different line is shown for each value of of the other parameter. For three parameters, the same line plot is created within conditioning panels/facets of the other parameter. Also, with two tuning parameters (with different values), a levelplot (i.e. un-clustered heatmap) can be created. For more than two parameters, this plot is created inside conditioning panels/facets. } \author{Max Kuhn} \references{Kuhn (2008), ``Building Predictive Models in R Using the caret'' (\url{http://www.jstatsoft.org/v28/i05/})} \seealso{ \code{\link{train}}, \code{\link[lattice]{levelplot}}, \code{\link[lattice]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}}, \code{\link[ggplot2]{ggplot}}} \examples{ \dontrun{ library(klaR) rdaFit <- train(Species ~ ., data = iris, method = "rda", control = trainControl(method = "cv")) plot(rdaFit) plot(rdaFit, plotType = "level") ggplot(rdaFit) + theme_bw() } } \keyword{hplot} caret/man/sbfControl.Rd0000644000175000017500000001464412453550065015217 0ustar rbalintrbalint\name{sbfControl} \alias{sbfControl} \title{ Control Object for Selection By Filtering (SBF) } \description{ Controls the execution of models with simple filters for feature selection } \usage{ sbfControl(functions = NULL, method = "boot", saveDetails = FALSE, number = ifelse(method \%in\% c("cv", "repeatedcv"), 10, 25), repeats = ifelse(method \%in\% c("cv", "repeatedcv"), 1, number), verbose = FALSE, returnResamp = "final", p = 0.75, index = NULL, indexOut = NULL, timingSamps = 0, seeds = NA, allowParallel = TRUE, multivariate = FALSE) } \arguments{ \item{functions}{a list of functions for model fitting, prediction and variable filtering (see Details below)} \item{method}{The external resampling method: \code{boot}, \code{cv}, \code{LOOCV} or \code{LGOCV} (for repeated training/test splits} \item{number}{Either the number of folds or number of resampling iterations} \item{repeats}{For repeated k-fold cross-validation only: the number of complete sets of folds to compute} \item{saveDetails}{a logical to save the predictions and variable importances from the selection process} \item{verbose}{a logical to print a log for each external resampling iteration} \item{returnResamp}{A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'' or ``none''} \item{p}{For leave-group out cross-validation: the training percentage} \item{index}{a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.} \item{indexOut}{a list (the same length as \code{index}) that dictates which sample are held-out for each resample. If \code{NULL}, then the unique set of samples not contained in \code{index} is used.} \item{timingSamps}{the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated).} \item{seeds}{an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of \code{NA} will stop the seed from being set within the worker processes while a value of \code{NULL} will set the seeds using a random set of integers. Alternatively, a vector of integers can be used. The vector should have \code{B+1} elements where \code{B} is the number of resamples. See the Examples section below. } \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?} \item{multivariate}{a logical; should all the columns of \code{x} be exposed to the \code{score} function at once?} } \details{ More details on this function can be found at \url{http://topepo.github.io/caret/featureselection.html#filter}. Simple filter-based feature selection requires function to be specified for some operations. The \code{fit} function builds the model based on the current data set. The arguments for the function must be: \itemize{ \item{\code{x}}{ the current training set of predictor data with the appropriate subset of variables (i.e. after filtering)} \item{\code{y}}{ the current outcome data (either a numeric or factor vector)} \item{\code{...}}{ optional arguments to pass to the fit function in the call to \code{sbf}} } The function should return a model object that can be used to generate predictions. The \code{pred} function returns a vector of predictions (numeric or factors) from the current model. The arguments are: \itemize{ \item{\code{object}}{ the model generated by the \code{fit} function} \item{\code{x}}{ the current set of predictor set for the held-back samples} } The \code{score} function is used to return scores with names for each predictor (such as a p-value). Inputs are: \itemize{ \item{\code{x}}{ the predictors for the training samples. If \code{sbfControl()$multivariate} is \code{TRUE}, this will be the full predictor matrix. Otherwise it is a vector for a specific predictor. } \item{\code{y}}{ the current training outcomes} } When \code{sbfControl()$multivariate} is \code{TRUE}, the \code{score} function should return a named vector where \code{length(scores) == ncol(x)}. Otherwise, the function's output should be a single value. Univariate examples are give by \code{\link{anovaScores}} for classification and \code{\link{gamScores}} for regression and the example below. The \code{filter} function is used to return a logical vector with names for each predictor (\code{TRUE} indicates that the prediction should be retained). Inputs are: \itemize{ \item{\code{score}}{ the output of the \code{score} function} \item{\code{x}}{ the predictors for the training samples} \item{\code{y}}{ the current training outcomes} } The function should return a named logical vector. Examples of these functions are included in the package: \code{\link{caretSBF}}, \code{\link{lmSBF}}, \code{\link{rfSBF}}, \code{\link{treebagSBF}}, \code{\link{ldaSBF}} and \code{\link{nbSBF}}. The web page \url{http://topepo.github.io/caret/} has more details and examples related to this function. } \value{ a list that echos the specified arguments } \author{ Max Kuhn } \seealso{ \code{\link{sbf}}, \code{\link{caretSBF}}, \code{\link{lmSBF}}, \code{\link{rfSBF}}, \code{\link{treebagSBF}}, \code{\link{ldaSBF}} and \code{\link{nbSBF}} } \examples{ \dontrun{ data(BloodBrain) ## Use a GAM is the filter, then fit a random forest model set.seed(1) RFwithGAM <- sbf(bbbDescr, logBBB, sbfControl = sbfControl(functions = rfSBF, verbose = FALSE, seeds = sample.int(100000, 11), method = "cv")) RFwithGAM ## A simple example for multivariate scoring rfSBF2 <- rfSBF rfSBF2$score <- function(x, y) apply(x, 2, rfSBF$score, y = y) set.seed(1) RFwithGAM2 <- sbf(bbbDescr, logBBB, sbfControl = sbfControl(functions = rfSBF2, verbose = FALSE, seeds = sample.int(100000, 11), method = "cv", multivariate = TRUE)) RFwithGAM2 }} \keyword{ utilities } caret/man/lift.Rd0000644000175000017500000001275712516214271014041 0ustar rbalintrbalint\name{lift} \alias{lift} \alias{lift.formula} \alias{lift.default} \alias{xyplot.lift} \title{ Lift Plot } \description{ For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class } \usage{ lift(x, ...) \method{lift}{formula}(x, data = NULL, class = NULL, subset = TRUE, lattice.options = NULL, cuts = NULL, labels = NULL, ...) \method{xyplot}{lift}(x, data, plot = "gain", values = NULL, ...) } \arguments{ \item{x}{ a \code{lattice} formula (see \code{\link[lattice:xyplot]{xyplot}} for syntax) where the left-hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model columns corresponding to a numeric ranking variable for a model (e.g. class probabilities). The classification variable should have two levels. } \item{data}{ For \code{lift.formula}, a data frame (or more precisely, anything that is a valid \code{envir} argument in \code{eval}, e.g., a list or an environment) containing values for any variables in the formula, as well as \code{groups} and \code{subset} if applicable. If not found in \code{data}, or if \code{data} is unspecified, the variables are looked for in the environment of the formula. This argument is not used for \code{xyplot.lift}. } \item{class}{ a character string for the class of interest } \item{subset}{ An expression that evaluates to a logical or integer indexing vector. It is evaluated in \code{data}. Only the resulting rows of \code{data} are used for the plot. } \item{lattice.options}{ A list that could be supplied to \code{\link[lattice:lattice.options]{lattice.options}} } \item{cuts}{ If a single value is given, a sequence of values between 0 and 1 are created with length \code{cuts}. If a vector, these values are used as the cuts. If \code{NULL}, each unique value of the model prediction is used. This is helpful when the data set is large. } \item{labels}{ A named list of labels for keys. The list should have an element for each term on the right-hand side of the formula and the names should match the names of the models. } \item{plot}{ Either "gain" (the default) or "lift". The former plots the number of samples called events versus the event rate while the latter shows the event cut-off versus the lift statistic.} \item{values}{ A vector of numbers between 0 and 100 specifying reference values for the percentage of samples found (i.e. the y-axis). Corresponding points on the x-axis are found via interpolation and line segments are shown to indicate how many samples must be tested before these percentages are found. The lines use either the \code{plot.line} or \code{superpose.line} component of the current lattice theme to draw the lines (depending on whether groups were used. These values are only used when \code{type = "gain"}.} \item{\dots}{ options to pass through to \code{\link[lattice:xyplot]{xyplot}} or the panel function (not used in \code{lift.formula}). } } \details{ \code{lift.formula} is used to process the data and \code{xyplot.lift} is used to create the plot. To construct data for the the lift and gain plots, the following steps are used for each model: \enumerate{ \item The data are ordered by the numeric model prediction used on the right-hand side of the model formula \item Each unique value of the score is treated as a cut point \item The number of samples with true results equal to \code{class} are determined \item The lift is calculated as the ratio of the percentage of samples in each split corresponding to \code{class} over the same percentage in the entire data set} \code{lift} with \code{plot = "gain"} produces a plot of the cumulative lift values by the percentage of samples evaluated while \code{plot = "lift"} shows the cut point value versus the lift statistic. This implementation uses the \pkg{lattice} function \code{\link[lattice:xyplot]{xyplot}}, so plot elements can be changed via panel functions, \code{\link[lattice:trellis.par.get]{trellis.par.set}} or other means. \code{lift} uses the panel function \code{\link{panel.lift2}} by default, but it can be changes using \code{\link[lattice:update.trellis]{update.trellis}} (see the examples in \code{\link{panel.lift2}}). The following elements are set by default in the plot but can be changed by passing new values into \code{xyplot.lift}: \code{xlab = "\% Samples Tested"}, \code{ylab = "\% Samples Found"}, \code{type = "S"}, \code{ylim = extendrange(c(0, 100))} and \code{xlim = extendrange(c(0, 100))}. } \value{ \code{lift.formula} returns a list with elements: \item{data}{the data used for plotting} \item{cuts}{the number of cuts} \item{class}{the event class} \item{probNames}{the names of the model probabilities} \item{pct}{the baseline event rate} \code{xyplot.lift} returns a \pkg{lattice} object } \author{ Max Kuhn, some \pkg{lattice} code and documentation by Deepayan Sarkar } \seealso{ \code{\link[lattice:xyplot]{xyplot}}, \code{\link[lattice:trellis.par.get]{trellis.par.set}}} \examples{ set.seed(1) simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)), perfect = sort(runif(200), decreasing = TRUE), random = runif(200)) lift1 <- lift(obs ~ random, data = simulated) lift1 xyplot(lift1) lift2 <- lift(obs ~ random + perfect, data = simulated) lift2 xyplot(lift2, auto.key = list(columns = 2)) xyplot(lift2, auto.key = list(columns = 2), value = c(10, 30)) xyplot(lift2, plot = "lift", auto.key = list(columns = 2)) } \keyword{hplot} caret/man/sa_functions.Rd0000644000175000017500000001160212453550065015566 0ustar rbalintrbalint\name{safs_initial} \alias{safs_initial} \alias{safs_perturb} \alias{safs_prob} \alias{caretSA} \alias{rfSA} \alias{treebagSA} \title{ Ancillary simulated annealing functions } \description{ Built-in functions related to simulated annealing } \usage{ safs_initial(vars, prob = 0.2, ...) safs_perturb(x, vars, number = floor(vars*.01) + 1) safs_prob(old, new, iteration = 1) caretSA rfSA treebagSA } \arguments{ \item{vars}{the total number of possible predictor variables} \item{prob}{The probability that an individual predictor is included in the initial predictor set} \item{x}{the integer index vector for the current subset} \item{old, new}{fitness values associated with the current and new subset} \item{iteration}{the number of iterations overall or the number of iterations since restart (if \code{improve} is used in \code{\link{safsControl}})} \item{number}{the number of predictor variables to perturb} \item{\dots}{not currently used} } \details{ These functions are used with the \code{functions} argument of the \code{\link{safsControl}} function. More information on the details of these functions are at \url{http://topepo.github.io/caret/SA.html}. The \code{initial} function is used to create the first predictor subset. The function \code{safs_initial} randomly selects 20\% of the predictors. Note that, instead of a function, \code{\link{safs}} can also accept a vector of column numbers as the initial subset. \code{safs_perturb} is an example of the operation that changes the subset configuration at the start of each new iteration. By default, it will change roughly 1\% of the variables in the current subset. The \code{prob} function defines the acceptance probability at each iteration, given the old and new fitness (i.e. energy values). It assumes that smaller values are better. The default probability function computed the percentage difference between the current and new fitness value and using an exponential function to compute a probability: \preformatted{ prob = exp[(current-new)/current*iteration] } } \value{ The return value depends on the function. Note that the SA code encodes the subsets as a vector of integers that are included in the subset (which is different than the encoding used for GAs). The objects \code{caretSA}, \code{rfSA} and \code{treebagSA} are example lists that can be used with the \code{functions} argument of \code{\link{safsControl}}. In the case of \code{caretSA}, the \code{...} structure of \code{\link{safs}} passes through to the model fitting routine. As a consequence, the \code{\link{train}} function can easily be accessed by passing important arguments belonging to \code{\link{train}} to \code{\link{safs}}. See the examples below. By default, using \code{caretSA} will used the resampled performance estimates produced by \code{\link{train}} as the internal estimate of fitness. For \code{rfSA} and \code{treebagSA}, the \code{randomForest} and \code{bagging} functions are used directly (i.e. \code{\link{train}} is not used). Arguments to either of these functions can also be passed to them though the \code{\link{safs}} call (see examples below). For these two functions, the internal fitness is estimated using the out-of-bag estimates naturally produced by those functions. While faster, this limits the user to accuracy or Kappa (for classification) and RMSE and R-squared (for regression). } \author{ Max Kuhn } \references{\url{http://topepo.github.io/caret/SA.html}} \seealso{ \code{\link{safs}}, \code{\link{safsControl}} } \examples{ selected_vars <- safs_initial(vars = 10 , prob = 0.2) selected_vars ### safs_perturb(selected_vars, vars = 10, number = 1) ### safs_prob(old = .8, new = .9, iteration = 1) safs_prob(old = .5, new = .6, iteration = 1) grid <- expand.grid(old = c(4, 3.5), new = c(4.5, 4, 3.5) + 1, iter = 1:40) grid <- subset(grid, old < new) grid$prob <- apply(grid, 1, function(x) safs_prob(new = x["new"], old= x["old"], iteration = x["iter"])) grid$Difference <- factor(grid$new - grid$old) grid$Group <- factor(paste("Current Value", grid$old)) ggplot(grid, aes(x = iter, y = prob, color = Difference)) + geom_line() + facet_wrap(~Group) + theme_bw() + ylab("Probability") + xlab("Iteration") \dontrun{ ### ## Hypothetical examples lda_sa <- safs(x = predictors, y = classes, safsControl = safsControl(functions = caretSA), ## now pass arguments to `train` method = "lda", metric = "Accuracy" trControl = trainControl(method = "cv", classProbs = TRUE)) rf_sa <- safs(x = predictors, y = classes, safsControl = safsControl(functions = rfSA), ## these are arguments to `randomForest` ntree = 1000, importance = TRUE) } } caret/man/train.Rd0000644000175000017500000002343112453550065014213 0ustar rbalintrbalint\name{train} \alias{train} \alias{train.default} \alias{train.formula} \newcommand{\cpkg}{\href{http://CRAN.R-project.org/package=#1}{\pkg{#1}}} \newcommand{\bpkg}{\href{http://www.bioconductor.org/packages/release/bioc/html/#1.html}{\pkg{#1}}} \title{Fit Predictive Models over Different Tuning Parameters} \description{ This function sets up a grid of tuning parameters for a number of classification and regression routines, fits each model and calculates a resampling based performance measure. } \usage{ train(x, ...) \method{train}{default}(x, y, method = "rf", preProcess = NULL, ..., weights = NULL, metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), trControl = trainControl(), tuneGrid = NULL, tuneLength = 3) \method{train}{formula}(form, data, ..., weights, subset, na.action, contrasts = NULL) } \arguments{ \item{x}{an object where samples are in rows and features are in columns. This could be a simple matrix, data frame or other type (e.g. sparse matrix). See Details below. } \item{y}{a numeric or factor vector containing the outcome for each sample.} \item{form}{A formula of the form \code{y ~ x1 + x2 + ...}} \item{data}{Data frame from which variables specified in \code{formula} are preferentially to be taken.} \item{weights}{a numeric vector of case weights. This argument will only affect models that allow case weights.} \item{subset}{An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)} \item{na.action}{A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is \code{na.omit}, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)} \item{contrasts}{a list of contrasts to be used for some or all the factors appearing as variables in the model formula.} \item{method}{a string specifying which classification or regression model to use. Possible values are found using \code{names(getModelInfo())}. See \url{http://topepo.github.io/caret/bytag.html}. A list of functions can also be passed for a custom model function. See \url{http://topepo.github.io/caret/custom_models.html} for details.} \item{\ldots}{ arguments passed to the classification or regression routine (such as \code{\link[randomForest]{randomForest}}). Errors will occur if values for tuning parameters are passed here.} \item{preProcess}{a string vector that defines a pre-processing of the predictor data. Current possibilities are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica" and "spatialSign". The default is no pre-processing. See \code{\link{preProcess}} and \code{\link{trainControl}} on the procedures and how to adjust them. Pre-processing code is only designed to work when \code{x} is a simple matrix or data frame. } \item{metric}{a string that specifies what summary metric will be used to select the optimal model. By default, possible values are "RMSE" and "Rsquared" for regression and "Accuracy" and "Kappa" for classification. If custom performance metrics are used (via the \code{summaryFunction} argument in \code{\link{trainControl}}, the value of \code{metric} should match one of the arguments. If it does not, a warning is issued and the first metric given by the \code{summaryFunction} is used. (NOTE: If given, this argument must be named.)} \item{maximize}{a logical: should the metric be maximized or minimized?} \item{trControl}{a list of values that define how this function acts. See \code{\link{trainControl}} and \url{http://topepo.github.io/caret/training.html#custom}. (NOTE: If given, this argument must be named.)} \item{tuneGrid}{a data frame with possible tuning values. The columns are named the same as the tuning parameters. Use \code{\link{getModelInfo}} to get a list of tuning parameters for each model or see \url{http://topepo.github.io/caret/modelList.html}. (NOTE: If given, this argument must be named.)} \item{tuneLength}{an integer denoting the number of levels for each tuning parameters that should be generated by \code{\link{train}}. (NOTE: If given, this argument must be named.)} } \details{ \code{train} can be used to tune models by picking the complexity parameters that are associated with the optimal resampling statistics. For particular model, a grid of parameters (if any) is created and the model is trained on slightly different data for each candidate combination of tuning parameters. Across each data set, the performance of held-out samples is calculated and the mean and standard deviation is summarized for each combination. The combination with the optimal resampling statistic is chosen as the final model and the entire training set is used to fit a final model. The predictors in \code{x} can be most any object as long as the underlying model fit function can deal with the object class. The function was designed to work with simple matrices and data frame inputs, so some functionality may not work (e.g. pre-processing). When using string kernels, the vector of character strings should be converted to a matrix with a single column. More details on this function can be found at \url{http://topepo.github.io/caret/training.html}. A variety of models are currently available and are enumerated by tag (i.e. their model characteristics) at \url{http://topepo.github.io/caret/bytag.html}. } \value{ A list is returned of class \code{train} containing: \item{method }{the chosen model.} \item{modelType }{an identifier of the model type.} \item{results }{a data frame the training error rate and values of the tuning parameters.} \item{bestTune }{a data frame with the final parameters.} \item{call }{the (matched) function call with dots expanded} \item{dots}{a list containing any ... values passed to the original call} \item{metric}{a string that specifies what summary metric will be used to select the optimal model.} \item{control}{the list of control parameters.} \item{preProcess }{either \code{NULL} or an object of class \code{\link{preProcess}}} \item{finalModel}{an fit object using the best parameters} \item{trainingData}{a data frame} \item{resample}{A data frame with columns for each performance metric. Each row corresponds to each resample. If leave-one-out cross-validation or out-of-bag estimation methods are requested, this will be \code{NULL}. The \code{returnResamp} argument of \code{\link{trainControl}} controls how much of the resampled results are saved.} \item{perfNames}{a character vector of performance metrics that are produced by the summary function} \item{maximize}{a logical recycled from the function arguments.} \item{yLimits}{the range of the training set outcomes.} \item{times}{a list of execution times: \code{everything} is for the entire call to \code{train}, \code{final} for the final model fit and, optionally, \code{prediction} for the time to predict new samples (see \code{\link{trainControl}})} } \author{Max Kuhn (the guts of \code{train.formula} were based on Ripley's \code{nnet.formula})} \references{ \url{http://topepo.github.io/caret/training.html} Kuhn (2008), ``Building Predictive Models in R Using the caret'' (\url{http://www.jstatsoft.org/v28/i05/})} \seealso{\code{\link{models}}, \code{\link{trainControl}}, \code{\link{update.train}}, \code{\link{modelLookup}}, \code{\link{createFolds}}} \examples{ \dontrun{ ####################################### ## Classification Example data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] knnFit1 <- train(TrainData, TrainClasses, method = "knn", preProcess = c("center", "scale"), tuneLength = 10, trControl = trainControl(method = "cv")) knnFit2 <- train(TrainData, TrainClasses, method = "knn", preProcess = c("center", "scale"), tuneLength = 10, trControl = trainControl(method = "boot")) library(MASS) nnetFit <- train(TrainData, TrainClasses, method = "nnet", preProcess = "range", tuneLength = 2, trace = FALSE, maxit = 100) ####################################### ## Regression Example library(mlbench) data(BostonHousing) lmFit <- train(medv ~ . + rm:lstat, data = BostonHousing, method = "lm") library(rpart) rpartFit <- train(medv ~ ., data = BostonHousing, method = "rpart", tuneLength = 9) ####################################### ## Example with a custom metric madSummary <- function (data, lev = NULL, model = NULL) { out <- mad(data$obs - data$pred, na.rm = TRUE) names(out) <- "MAD" out } robustControl <- trainControl(summaryFunction = madSummary) marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2) earthFit <- train(medv ~ ., data = BostonHousing, method = "earth", tuneGrid = marsGrid, metric = "MAD", maximize = FALSE, trControl = robustControl) ####################################### ## Parallel Processing Example via multicore package ## library(doMC) ## registerDoMC(2) ## NOTE: don't run models form RWeka when using ### multicore. The session will crash. ## The code for train() does not change: set.seed(1) usingMC <- train(medv ~ ., data = BostonHousing, method = "glmboost") ## or use: ## library(doMPI) or ## library(doParallel) or ## library(doSMP) and so on } } \keyword{models} caret/man/avNNet.Rd0000644000175000017500000000657512453550065014303 0ustar rbalintrbalint\name{avNNet.default} \alias{avNNet.default} \alias{predict.avNNet} \alias{avNNet.formula} \alias{avNNet} \title{Neural Networks Using Model Averaging} \description{ Aggregate several neural network models } \usage{ \method{avNNet}{default}(x, y, repeats = 5, bag = FALSE, allowParallel = TRUE, ...) \method{avNNet}{formula}(formula, data, weights, ..., repeats = 5, bag = FALSE, allowParallel = TRUE, subset, na.action, contrasts = NULL) \method{predict}{avNNet}(object, newdata, type = c("raw", "class", "prob"), ...) } \arguments{ \item{formula}{ A formula of the form \code{class ~ x1 + x2 + \dots} } \item{x}{ matrix or data frame of \code{x} values for examples. } \item{y}{ matrix or data frame of target values for examples. } \item{weights}{ (case) weights for each example -- if missing defaults to 1. } \item{repeats}{the number of neural networks with different random number seeds} \item{bag}{a logical for bagging for each repeat} \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?} \item{data}{ Data frame from which variables specified in \code{formula} are preferentially to be taken. } \item{subset}{ An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) } \item{na.action}{ A function to specify the action to be taken if \code{NA}s are found. The default action is for the procedure to fail. An alternative is \code{na.omit}, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) } \item{contrasts}{ a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. } \item{object}{ an object of class \code{avNNet} as returned by \code{avNNet}. } \item{newdata}{ matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case. } \item{type}{ Type of output, either: \code{raw} for the raw outputs, \code{code} for the predicted class or \code{prob} for the class probabilities. } \item{\dots}{ arguments passed to \code{\link[nnet]{nnet}} } } \details{ Following Ripley (1996), the same neural network model is fit using different random number seeds. All the resulting models are used for prediction. For regression, the output from each network are averaged. For classification, the model scores are first averaged, then translated to predicted classes. Bagging can also be used to create the models. If a parallel backend is registered, the \pkg{foreach} package is used to train the networks in parallel. } \value{ For \code{avNNet}, an object of \code{"avNNet"} or \code{"avNNet.formula"}. Items of interest in the output are: \item{model }{a list of the models generated from \code{\link[nnet]{nnet}}} \item{repeats }{an echo of the model input} \item{names }{if any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of \code{NULL}} } \references{ Ripley, B. D. (1996) \emph{Pattern Recognition and Neural Networks.} Cambridge. } \author{These are heavily based on the \code{nnet} code from Brian Ripley.} \seealso{ \code{\link[nnet]{nnet}}, \code{\link{preProcess}}} \examples{ data(BloodBrain) \dontrun{ modelFit <- avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE) modelFit predict(modelFit, bbbDescr) } } \keyword{ neural } caret/man/oil.Rd0000644000175000017500000000206612453550065013662 0ustar rbalintrbalint\name{oil} \docType{data} \alias{oil} \alias{oilType} \alias{fattyAcids} \title{Fatty acid composition of commercial oils} \description{ Fatty acid concentrations of commercial oils were measured using gas chromatography. The data is used to predict the type of oil. Note that only the known oils are in the data set. Also, the authors state that there are 95 samples of known oils. However, we count 96 in Table 1 (pgs. 33-35). } \usage{data(oil)} \value{ \item{fattyAcids}{data frame of fatty acid compositions: Palmitic, Stearic, Oleic, Linoleic, Linolenic, Eicosanoic and Eicosenoic. When values fell below the lower limit of the assay (denoted as , modifications by Max Kuhn and Andre Williams } \examples{ irisFit1 <- knn3(Species ~ ., iris) irisFit2 <- knn3(as.matrix(iris[, -5]), iris[,5]) data(iris3) train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3]) test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) knn3Train(train, test, cl, k = 5, prob = TRUE) } \keyword{multivariate} caret/man/tecator.Rd0000644000175000017500000000416712453550065014544 0ustar rbalintrbalint\name{tecator} \docType{data} \alias{tecator} \alias{absorp} \alias{endpoints} \title{Fat, Water and Protein Content of Meat Samples} \description{ "These data are recorded on a Tecator Infratec Food and Feed Analyzer working in the wavelength range 850 - 1050 nm by the Near Infrared Transmission (NIT) principle. Each sample contains finely chopped pure meat with different moisture, fat and protein contents. If results from these data are used in a publication we want you to mention the instrument and company name (Tecator) in the publication. In addition, please send a preprint of your article to Karin Thente, Tecator AB, Box 70, S-263 21 Hoganas, Sweden The data are available in the public domain with no responsibility from the original data source. The data can be redistributed as long as this permission note is attached." "For each meat sample the data consists of a 100 channel spectrum of absorbances and the contents of moisture (water), fat and protein. The absorbance is -log10 of the transmittance measured by the spectrometer. The three contents, measured in percent, are determined by analytic chemistry." Included here are the traning, monitoring and test sets. } \usage{data(tecator)} \value{ \item{absorp}{absorbance data for 215 samples. The first 129 were originally used as a training set} \item{endpoints}{the percentages of water, fat and protein} } \examples{ data(tecator) splom(~endpoints) # plot 10 random spectra set.seed(1) inSubset <- sample(1:dim(endpoints)[1], 10) absorpSubset <- absorp[inSubset,] endpointSubset <- endpoints[inSubset, 3] newOrder <- order(absorpSubset[,1]) absorpSubset <- absorpSubset[newOrder,] endpointSubset <- endpointSubset[newOrder] plotColors <- rainbow(10) plot(absorpSubset[1,], type = "n", ylim = range(absorpSubset), xlim = c(0, 105), xlab = "Wavelength Index", ylab = "Absorption") for(i in 1:10) { points(absorpSubset[i,], type = "l", col = plotColors[i], lwd = 2) text(105, absorpSubset[i,100], endpointSubset[i], col = plotColors[i]) } title("Predictor Profiles for 10 Random Samples") } \keyword{datasets} caret/man/nullModel.Rd0000644000175000017500000000421512453550065015030 0ustar rbalintrbalint\name{nullModel} \alias{nullModel} \alias{nullModel.default} \alias{predict.nullModel} \title{ Fit a simple, non-informative model } \description{ Fit a single mean or largest class model } \usage{ nullModel(x, ...) \method{nullModel}{default}(x = NULL, y, ...) \method{predict}{nullModel}(object, newdata = NULL, type = NULL, ...) } \arguments{ \item{x}{ An optional matrix or data frame of predictors. These values are not used in the model fit } \item{y}{ A numeric vector (for regression) or factor (for classification) of outcomes} \item{\dots}{ Optional arguments (not yet used) } \item{object}{ An object of class \code{nullModel} } \item{newdata}{ A matrix or data frame of predictors (only used to determine the number of predictions to return) } \item{type}{ Either "raw" (for regression), "class" or "prob" (for classification) } } \details{ \code{nullModel} emulates other model building functions, but returns the simplest model possible given a training set: a single mean for numeric outcomes and the most prevalent class for factor outcomes. When class probabilities are requested, the percentage of the training set samples with the most prevalent class is returned. } \value{ The output of \code{nullModel} is a list of class \code{nullModel} with elements \item{call }{the function call} \item{value }{the mean of \code{y} or the most prevalent class} \item{levels }{when \code{y} is a factor, a vector of levels. \code{NULL} otherwise} \item{pct }{when \code{y} is a factor, a data frame with a column for each class (\code{NULL} otherwise). The column for the most prevalent class has the proportion of the training samples with that class (the other columns are zero). } \item{n }{the number of elements in \code{y}} \code{predict.nullModel} returns a either a factor or numeric vector depending on the class of \code{y}. All predictions are always the same. } \examples{ outcome <- factor(sample(letters[1:2], size = 100, prob = c(.1, .9), replace = TRUE)) useless <- nullModel(y = outcome) useless predict(useless, matrix(NA, nrow = 10)) } \keyword{models} caret/man/segmentationData.Rd0000644000175000017500000000306012453550065016361 0ustar rbalintrbalint\name{segmentationData} \docType{data} \alias{segmentationData} \title{Cell Body Segmentation} \description{ Hill, LaPan, Li and Haney (2007) develop models to predict which cells in a high content screen were well segmented. The data consists of 119 imaging measurements on 2019. The original analysis used 1009 for training and 1010 as a test set (see the column called \code{Case}). The outcome class is contained in a factor variable called \code{Class} with levels "PS" for poorly segmented and "WS" for well segmented. The raw data used in the paper can be found at the Biomedcentral website. Versions of caret < 4.98 contained the original data. The version now contained in \code{segmentationData} is modified. First, several discrete versions of some of the predictors (with the suffix "Status") were removed. Second, there are several skewed predictors with minimum values of zero (that would benefit from some transformation, such as the log). A constant value of 1 was added to these fields: \code{AvgIntenCh2}, \code{FiberAlign2Ch3}, \code{FiberAlign2Ch4}, \code{SpotFiberCountCh4} and \code{TotalIntenCh2}. A binary version of the original data is at \url{http://topepo.github.io/caret/segmentationOriginal.RData}. } \usage{data(segmentationData)} \value{ \item{segmentationData}{data frame of cells} } \source{Hill, LaPan, Li and Haney (2007). Impact of image segmentation on high-content screening data quality for SK-BR-3 cells, \emph{BMC Bioinformatics}, Vol. 8, pg. 340, \url{http://www.biomedcentral.com/1471-2105/8/340}. } \keyword{datasets} caret/man/sensitivity.Rd0000644000175000017500000001414512453550065015472 0ustar rbalintrbalint\name{sensitivity} \alias{sensitivity} \alias{sensitivity.default} \alias{sensitivity.table} \alias{sensitivity.matrix} \alias{specificity} \alias{specificity.default} \alias{specificity.table} \alias{specificity.matrix} \alias{posPredValue} \alias{posPredValue.default} \alias{posPredValue.table} \alias{posPredValue.matrix} \alias{negPredValue} \alias{negPredValue.default} \alias{negPredValue.table} \alias{negPredValue.matrix} \title{Calculate sensitivity, specificity and predictive values} \description{ These functions calculate the sensitivity, specificity or predictive values of a measurement system compared to a reference results (the truth or a gold standard). The measurement and "truth" data must have the same two possible outcomes and one of the outcomes must be thought of as a "positive" results. The sensitivity is defined as the proportion of positive results out of the number of samples which were actually positive. When there are no positive results, sensitivity is not defined and a value of \code{NA} is returned. Similarly, when there are no negative results, specificity is not defined and a value of \code{NA} is returned. Similar statements are true for predictive values. The positive predictive value is defined as the percent of predicted positives that are actually positive while the negative predictive value is defined as the percent of negative positives that are actually negative. } \usage{ sensitivity(data, ...) \method{sensitivity}{default}(data, reference, positive = levels(reference)[1], na.rm = TRUE, ...) \method{sensitivity}{table}(data, positive = rownames(data)[1], ...) \method{sensitivity}{matrix}(data, positive = rownames(data)[1], ...) specificity(data, ...) \method{specificity}{default}(data, reference, negative = levels(reference)[-1], na.rm = TRUE, ...) \method{specificity}{table}(data, negative = rownames(data)[-1], ...) \method{specificity}{matrix}(data, negative = rownames(data)[-1], ...) posPredValue(data, ...) \method{posPredValue}{default}(data, reference, positive = levels(reference)[1], prevalence = NULL, ...) \method{posPredValue}{table}(data, positive = rownames(data)[1], prevalence = NULL, ...) \method{posPredValue}{matrix}(data, positive = rownames(data)[1], prevalence = NULL, ...) negPredValue(data, ...) \method{negPredValue}{default}(data, reference, negative = levels(reference)[2], prevalence = NULL, ...) \method{negPredValue}{table}(data, negative = rownames(data)[-1], prevalence = NULL, ...) \method{negPredValue}{matrix}(data, negative = rownames(data)[-1], prevalence = NULL, ...) } \arguments{ \item{data}{for the default functions, a factor containing the discrete measurements. For the \code{table} or \code{matrix} functions, a table or matric object, respectively.} \item{reference}{a factor containing the reference values} \item{positive}{a character string that defines the factor level corresponding to the "positive" results} \item{negative}{a character string that defines the factor level corresponding to the "negative" results} \item{prevalence}{a numeric value for the rate of the "positive" class of the data} \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds} \item{...}{not currently used} } \details{ Suppose a 2x2 table with notation \tabular{rcc}{ \tab Reference \tab \cr Predicted \tab Event \tab No Event \cr Event \tab A \tab B \cr No Event \tab C \tab D \cr } The formulas used here are: \deqn{Sensitivity = A/(A+C)} \deqn{Specificity = D/(B+D)} \deqn{Prevalence = (A+C)/(A+B+C+D)} \deqn{PPV = (sensitivity * Prevalence)/((sensitivity*Prevalence) + ((1-specificity)*(1-Prevalence)))} \deqn{NPV = (specificity * (1-Prevalence))/(((1-sensitivity)*Prevalence) + ((specificity)*(1-Prevalence)))} See the references for discussions of the statistics. } \value{ A number between 0 and 1 (or NA). } \author{Max Kuhn} \references{Kuhn, M. (2008), ``Building predictive models in R using the caret package, '' \emph{Journal of Statistical Software}, (\url{http://www.jstatsoft.org/v28/i05/}). Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 1: sensitivity and specificity,'' \emph{British Medical Journal}, vol 308, 1552. Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 2: predictive values,'' \emph{British Medical Journal}, vol 309, 102. } \seealso{\code{\link{confusionMatrix}}} \examples{ \dontrun{ ################### ## 2 class example lvs <- c("normal", "abnormal") truth <- factor(rep(lvs, times = c(86, 258)), levels = rev(lvs)) pred <- factor( c( rep(lvs, times = c(54, 32)), rep(lvs, times = c(27, 231))), levels = rev(lvs)) xtab <- table(pred, truth) sensitivity(pred, truth) sensitivity(xtab) posPredValue(pred, truth) posPredValue(pred, truth, prevalence = 0.25) specificity(pred, truth) negPredValue(pred, truth) negPredValue(xtab) negPredValue(pred, truth, prevalence = 0.25) prev <- seq(0.001, .99, length = 20) npvVals <- ppvVals <- prev * NA for(i in seq(along = prev)) { ppvVals[i] <- posPredValue(pred, truth, prevalence = prev[i]) npvVals[i] <- negPredValue(pred, truth, prevalence = prev[i]) } plot(prev, ppvVals, ylim = c(0, 1), type = "l", ylab = "", xlab = "Prevalence (i.e. prior)") points(prev, npvVals, type = "l", col = "red") abline(h=sensitivity(pred, truth), lty = 2) abline(h=specificity(pred, truth), lty = 2, col = "red") legend(.5, .5, c("ppv", "npv", "sens", "spec"), col = c("black", "red", "black", "red"), lty = c(1, 1, 2, 2)) ################### ## 3 class example library(MASS) fit <- lda(Species ~ ., data = iris) model <- predict(fit)$class irisTabs <- table(model, iris$Species) ## When passing factors, an error occurs with more ## than two levels sensitivity(model, iris$Species) ## When passing a table, more than two levels can ## be used sensitivity(irisTabs, "versicolor") specificity(irisTabs, c("setosa", "virginica")) } } \keyword{manip} caret/man/lattice.Rd0000644000175000017500000000503512453550065014523 0ustar rbalintrbalint\name{histogram.train} \alias{stripplot.train} \alias{xyplot.train} \alias{densityplot.train} \alias{histogram.train} \title{Lattice functions for plotting resampling results} \description{ A set of lattice functions are provided to plot the resampled performance estimates (e.g. classification accuracy, RMSE) over tuning parameters (if any). } \usage{ \method{histogram}{train}(x, data = NULL, metric = x$metric, ...) \method{densityplot}{train}(x, data = NULL, metric = x$metric, ...) \method{xyplot}{train}(x, data = NULL, metric = x$metric, ...) \method{stripplot}{train}(x, data = NULL, metric = x$metric, ...) } \arguments{ \item{x}{ An object produced by \code{\link{train}} } \item{data}{This argument is not used } \item{metric}{A character string specifying the single performance metric that will be plotted } \item{\dots}{arguments to pass to either \code{\link[lattice:histogram]{histogram}}, \code{\link[lattice:histogram]{densityplot}}, \code{\link[lattice:xyplot]{xyplot}} or \code{\link[lattice:xyplot]{stripplot}}} } \details{ By default, only the resampling results for the optimal model are saved in the \code{train} object. The function \code{\link{trainControl}} can be used to save all the results (see the example below). If leave-one-out or out-of-bag resampling was specified, plots cannot be produced (see the \code{method} argument of \code{\link{trainControl}}) For \code{xyplot} and \code{stripplot}, the tuning parameter with the most unique values will be plotted on the x-axis. The remaining parameters (if any) will be used as conditioning variables. For \code{densityplot} and \code{histogram}, all tuning parameters are used for conditioning. Using \code{horizontal = FALSE} in \code{stripplot} works. } \value{A lattice plot object} \author{Max Kuhn} \seealso{\code{\link{train}}, \code{\link{trainControl}}, \code{\link[lattice:histogram]{histogram}}, \code{\link[lattice:histogram]{densityplot}}, \code{\link[lattice:xyplot]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}}} \examples{ \dontrun{ library(mlbench) data(BostonHousing) library(rpart) rpartFit <- train(medv ~ ., data = BostonHousing, "rpart", tuneLength = 9, trControl = trainControl( method = "boot", returnResamp = "all")) densityplot(rpartFit, adjust = 1.25) xyplot(rpartFit, metric = "Rsquared", type = c("p", "a")) stripplot(rpartFit, horizontal = FALSE, jitter = TRUE) } } \keyword{hplot} caret/man/format.bagEarth.Rd0000644000175000017500000000252312453550065016101 0ustar rbalintrbalint\name{format.bagEarth} \alias{format.bagEarth} \title{Format 'bagEarth' objects} \description{ Return a string representing the \sQuote{bagEarth} expression. } \usage{ \method{format}{bagEarth}(x, file = "", cat = TRUE, \dots) } \arguments{ \item{x}{ An \code{\link{bagEarth}} object. This is the only required argument. } \item{file}{A connection, or a character string naming the file to print to. If "" (the default), the output prints to the standard output connection. See \code{\link[base]{cat}}.} \item{cat}{a logical; should the equation be printed?} \item{\dots}{ Arguments to \code{\link[earth]{format.earth}}. } } \value{ A character representation of the bagged earth object. } \seealso{ \code{\link[earth]{earth}} } \examples{ a <- bagEarth(Volume ~ ., data = trees, B= 3) format(a) # yields: # ( # 31.61075 # + 6.587273 * pmax(0, Girth - 14.2) # - 3.229363 * pmax(0, 14.2 - Girth) # - 0.3167140 * pmax(0, 79 - Height) # + # 22.80225 # + 5.309866 * pmax(0, Girth - 12) # - 2.378658 * pmax(0, 12 - Girth) # + 0.793045 * pmax(0, Height - 80) # - 0.3411915 * pmax(0, 80 - Height) # + # 31.39772 # + 6.18193 * pmax(0, Girth - 14.2) # - 3.660456 * pmax(0, 14.2 - Girth) # + 0.6489774 * pmax(0, Height - 80) # )/3 } \keyword{models} caret/man/sbfFunctions.Rd0000644000175000017500000000306312453550065015540 0ustar rbalintrbalint\name{caretSBF} \alias{caretSBF} \alias{lmSBF} \alias{rfSBF} \alias{treebagSBF} \alias{ldaSBF} \alias{nbSBF} \alias{gamScores} \alias{anovaScores} \title{Selection By Filtering (SBF) Helper Functions} \description{ Ancillary functions for univariate feature selection } \usage{ anovaScores(x, y) gamScores(x, y) caretSBF lmSBF rfSBF treebagSBF ldaSBF nbSBF } \arguments{ \item{x}{a matrix or data frame of numeric predictors} \item{y}{a numeric or factor vector of outcomes} } \details{ More details on these functions can be found at \url{http://topepo.github.io/caret/featureselection.html#filter}. This page documents the functions that are used in selection by filtering (SBF). The functions described here are passed to the algorithm via the \code{functions} argument of \code{\link{sbfControl}}. See \code{\link{sbfControl}} for details on how these functions should be defined. \code{anovaScores} and \code{gamScores} are two examples of univariate filtering functions. \code{anovaScores} fits a simple linear model between a single feature and the outcome, then the p-value for the whole model F-test is returned. \code{gamScores} fits a generalized additive model between a single predictor and the outcome using a smoothing spline basis function. A p-value is generated using the whole model test from \code{\link[gam]{summary.gam}} and is returned. If a particular model fails for \code{lm} or \code{gam}, a p-value of 1 is returned. } \author{Max Kuhn} \seealso{\code{\link{sbfControl}}, \code{\link{sbf}}, \code{\link[gam]{summary.gam}}} \keyword{models} caret/man/prcomp.resamples.Rd0000644000175000017500000000736112453550065016374 0ustar rbalintrbalint\name{prcomp.resamples} \alias{prcomp.resamples} \alias{cluster.resamples} \alias{cluster} \alias{plot.prcomp.resamples} \title{ Principal Components Analysis of Resampling Results } \description{ Performs a principal components analysis on an object of class \code{\link{resamples}} and returns the results as an object with classes \code{prcomp.resamples} and \code{prcomp}. } \usage{ \method{prcomp}{resamples}(x, metric = x$metrics[1], ...) cluster(x, ...) \method{cluster}{resamples}(x, metric = x$metrics[1], ...) \method{plot}{prcomp.resamples}(x, what = "scree", dims = max(2, ncol(x$rotation)), ...) } \arguments{ \item{x}{ For \code{prcomp}, an object of class \code{\link{resamples}} and for \code{plot.prcomp.resamples}, an object of class \code{plot.prcomp.resamples} } \item{metric}{ a performance metric that was estimated for every resample } \item{what}{ the type of plot: \code{"scree"} produces a bar chart of standard deviations, \code{"cumulative"} produces a bar chart of the cumulative percent of variance, \code{"loadings"} produces a scatterplot matrix of the loading values and \code{"components"} produces a scatterplot matrix of the PCA components } \item{dims}{ The number of dimensions to plot when \code{what = "loadings"} or \code{what = "components"} } \item{\dots}{ For \code{prcomp.resamples}, options to pass to \code{\link[stats]{prcomp}}, for \code{plot.prcomp.resamples}, options to pass to Lattice objects (see Details below) and, for \code{cluster.resamples}, options to pass to \code{hclust}. } } \details{ The principal components analysis treats the models as variables and the resamples are realizations of the variables. In this way, we can use PCA to "cluster" the assays and look for similarities. Most of the methods for \code{\link[stats]{prcomp}} can be used, although custom \code{print} and \code{plot} methods are used. The plot method uses lattice graphics. When \code{what = "scree"} or \code{what = "cumulative"}, \code{\link[lattice:xyplot]{barchart}} is used. When \code{what = "loadings"} or \code{what = "components"}, either \code{\link[lattice:xyplot]{xyplot}} or \code{\link[lattice:splom]{splom}} are used (the latter when \code{dims} > 2). Options can be passed to these methods using \code{...}. When \code{what = "loadings"} or \code{what = "components"}, the plots are put on a common scale so that later components are less likely to be over-interpreted. See Geladi et al. (2003) for examples of why this can be important. For clustering, \code{\link[stats]{hclust}} is used to determine clusters of models based on the resampled performance values. } \value{ For \code{prcomp.resamples}, an object with classes \code{prcomp.resamples} and \code{prcomp}. This object is the same as the object produced by \code{prcomp}, but with additional elements: \item{metric }{the value for the \code{metric} argument} \item{call }{the call} For \code{plot.prcomp.resamples}, a Lattice object (see Details above) } \references{Geladi, P.; Manley, M.; and Lestander, T. (2003), "Scatter plotting in multivariate data analysis," J. Chemometrics, 17: 503-511} \author{ Max Kuhn} \seealso{ \code{\link{resamples}}, \code{\link[lattice:xyplot]{barchart}}, \code{\link[lattice:xyplot]{xyplot}}, \code{\link[lattice:splom]{splom}}, \code{\link[stats]{hclust}}} \examples{ \dontrun{ #load(url("http://topepo.github.io/caret/exampleModels.RData")) resamps <- resamples(list(CART = rpartFit, CondInfTree = ctreeFit, MARS = earthFit)) resampPCA <- prcomp(resamps) resampPCA plot(resampPCA, what = "scree") plot(resampPCA, what = "components") plot(resampPCA, what = "components", dims = 2, auto.key = list(columns = 3)) clustered <- cluster(resamps) plot(clustered) } } \keyword{hplot} caret/man/downSample.Rd0000644000175000017500000000253512453550065015211 0ustar rbalintrbalint\name{downSample} \alias{downSample} \alias{upSample} \title{ Down- and Up-Sampling Imbalanced Data } \description{ \code{downSample} will randomly sample a data set so that all classes have the same frequency as the minority class. \code{upSample} samples with replacement to make the class distributions equal } \usage{ downSample(x, y, list = FALSE, yname = "Class") upSample(x, y, list = FALSE, yname = "Class") } \arguments{ \item{x}{ a matrix or data frame of predictor variables } \item{y}{ a factor variable with the class memberships } \item{list}{ should the function return \code{list(x, y)} or bind \code{x} and \code{y} together? If \code{TRUE}, the output will be coerced to a data frame. } \item{yname}{ if \code{list = FALSE}, a label for the class column } } \details{ Simple random sampling is used to down-sample for the majority class(es). Note that the minority class data are left intact and that the samples will be re-ordered in the down-sampled version. For up-sampling, all the original data are left intact and additional samples are added to the minority classes with replacement. } \value{ Either a data frame or a list with elements \code{x} and \code{y}. } \author{ Max Kuhn } \examples{ ## A ridiculous example... data(oil) table(oilType) downSample(fattyAcids, oilType) upSample(fattyAcids, oilType) } \keyword{ utilities } caret/man/plotClassProbs.Rd0000644000175000017500000000433712453550065016054 0ustar rbalintrbalint\name{plotClassProbs} \alias{plotClassProbs} \title{Plot Predicted Probabilities in Classification Models} \description{ This function takes an object (preferably from the function \code{\link{extractProb}}) and creates a lattice plot. If the call to \code{\link{extractProb}} included test data, these data are shown, but if unknowns were also included, these are not plotted } \usage{ plotClassProbs(object, plotType = "histogram", useObjects = FALSE, ...) } \arguments{ \item{object}{an object (preferably from the function \code{\link{extractProb}}. There should be columns for each level of the class factor and columns named \code{obs}, \code{pred}, \code{model} (e.g. "rpart", "nnet" etc), \code{dataType} (e.g. "Training", "Test" etc) and optionally \code{objects} (for giving names to objects with the same model type).} \item{plotType}{either "histogram" or "densityplot"} \item{useObjects}{a logical; should the object name (if any) be used as a conditioning variable?} \item{\dots}{parameters to pass to \code{\link[lattice]{histogram}} or \code{\link[lattice]{densityplot}}} } \value{ A lattice object. Note that the plot has to be printed to be displayed (especially in a loop). } \author{Max Kuhn} \examples{ \dontrun{ data(mdrr) set.seed(90) inTrain <- createDataPartition(mdrrClass, p = .5)[[1]] trainData <- mdrrDescr[inTrain,1:20] testData <- mdrrDescr[-inTrain,1:20] trainY <- mdrrClass[inTrain] testY <- mdrrClass[-inTrain] ctrl <- trainControl(method = "cv") nbFit1 <- train(trainData, trainY, "nb", trControl = ctrl, tuneGrid = data.frame(usekernel = TRUE, fL = 0)) nbFit2 <- train(trainData, trainY, "nb", trControl = ctrl, tuneGrid = data.frame(usekernel = FALSE, fL = 0)) models <- list(para = nbFit2, nonpara = nbFit1) predProbs <- extractProb(models, testX = testData, testY = testY) plotClassProbs(predProbs, useObjects = TRUE) plotClassProbs(predProbs, subset = object == "para" & dataType == "Test") plotClassProbs(predProbs, useObjects = TRUE, plotType = "densityplot", auto.key = list(columns = 2)) } } \keyword{hplot} caret/man/plot.varImp.train.Rd0000644000175000017500000000210512453550065016420 0ustar rbalintrbalint\name{plot.varImp.train} \alias{plot.varImp.train} \title{Plotting variable importance measures} \description{ This function produces lattice plots of objects with class "varImp.train". More info will be forthcoming. } \usage{ \method{plot}{varImp.train}(x, top = dim(x$importance)[1], ...) } \arguments{ \item{x}{an object with class \code{varImp}. } \item{top}{a scalar numeric that specifies the number of variables to be displayed (in order of importance)} \item{\dots}{arguments to pass to the lattice plot function (\code{\link[lattice:xyplot]{dotplot}} and \code{\link{panel.needle}})} } \details{ For models where there is only one importance value, such a regression models, a "Pareto-type" plot is produced where the variables are ranked by their importance and a needle-plot is used to show the top variables. When there is more than one importance value per predictor, the same plot is produced within conditioning panels for each class. The top predictors are sorted by their average importance. } \value{ a lattice plot object } \author{Max Kuhn} \keyword{hplot} caret/man/print.confusionMatrix.Rd0000644000175000017500000000124412453550065017417 0ustar rbalintrbalint\name{print.confusionMatrix} \alias{print.confusionMatrix} \title{Print method for confusionMatrix} \description{ a print method for \code{confusionMatrix} } \usage{ \method{print}{confusionMatrix}(x, digits = max(3, getOption("digits") - 3), printStats = TRUE, ...) } \arguments{ \item{x}{an object of class \code{confusionMatrix}} \item{digits}{number of significant digits when printed} \item{printStats}{a logical: if \code{TRUE} then table statistics are also printed} \item{\dots}{optional arguments to pass to \code{print.table}} } \value{ \code{x} is invisibly returned } \author{Max Kuhn} \seealso{\code{\link{confusionMatrix}}} \keyword{utilities} caret/man/maxDissim.Rd0000644000175000017500000000612412453550065015034 0ustar rbalintrbalint\name{maxDissim} \alias{maxDissim} \alias{minDiss} \alias{sumDiss} \title{Maximum Dissimilarity Sampling} \description{ Functions to create a sub-sample by maximizing the dissimilarity between new samples and the existing subset. } \usage{ maxDissim(a, b, n = 2, obj = minDiss, useNames = FALSE, randomFrac = 1, verbose = FALSE, ...) minDiss(u) sumDiss(u) } \arguments{ \item{a}{a matrix or data frame of samples to start} \item{b}{a matrix or data frame of samples to sample from} \item{n}{the size of the sub-sample} \item{obj}{an objective function to measure overall dissimilarity} \item{useNames}{a logical: should the function return the row names (as opposed ot the row index)} \item{randomFrac}{a number in (0, 1] that can be used to sub-sample from the remaining candidate values} \item{verbose}{a logical; should each step be printed?} \item{\dots}{optional arguments to pass to dist} \item{u}{a vector of dissimilarities} } \details{ Given an initial set of m samples and a larger pool of n samples, this function iteratively adds points to the smaller set by finding with of the n samples is most dissimilar to the initial set. The argument \code{obj} measures the overall dissimilarity between the initial set and a candidate point. For example, maximizing the minimum or the sum of the m dissimilarities are two common approaches. This algorithm tends to select points on the edge of the data mainstream and will reliably select outliers. To select more samples towards the interior of the data set, set \code{randomFrac} to be small (see the examples below). } \value{ a vector of integers or row names (depending on \code{useNames}) corresponding to the rows of \code{b} that comprise the sub-sample. } \references{Willett, P. (1999), "Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds," \emph{Journal of Computational Biology}, 6, 447-457.} \author{Max Kuhn \email{max.kuhn@pfizer.com}} \seealso{\code{\link{dist}}} \examples{ example <- function(pct = 1, obj = minDiss, ...) { tmp <- matrix(rnorm(200 * 2), nrow = 200) ## start with 15 data points start <- sample(1:dim(tmp)[1], 15) base <- tmp[start,] pool <- tmp[-start,] ## select 9 for addition newSamp <- maxDissim( base, pool, n = 9, randomFrac = pct, obj = obj, ...) allSamp <- c(start, newSamp) plot( tmp[-newSamp,], xlim = extendrange(tmp[,1]), ylim = extendrange(tmp[,2]), col = "darkgrey", xlab = "variable 1", ylab = "variable 2") points(base, pch = 16, cex = .7) for(i in seq(along = newSamp)) points( pool[newSamp[i],1], pool[newSamp[i],2], pch = paste(i), col = "darkred") } par(mfrow=c(2,2)) set.seed(414) example(1, minDiss) title("No Random Sampling, Min Score") set.seed(414) example(.1, minDiss) title("10 Pct Random Sampling, Min Score") set.seed(414) example(1, sumDiss) title("No Random Sampling, Sum Score") set.seed(414) example(.1, sumDiss) title("10 Pct Random Sampling, Sum Score") } \keyword{utilities} caret/man/summary.bagEarth.Rd0000644000175000017500000000165112453550065016307 0ustar rbalintrbalint\name{summary.bagEarth} \alias{summary.bagEarth} \alias{summary.bagFDA} \title{Summarize a bagged earth or FDA fit} \description{ The function shows a summary of the results from a bagged earth model } \usage{ \method{summary}{bagEarth}(object, ...) \method{summary}{bagFDA}(object, ...) } \arguments{ \item{object}{an object of class "bagEarth" or "bagFDA"} \item{\dots}{optional arguments (not used)} } \details{ The out-of-bag statistics are summarized, as well as the distribution of the number of model terms and number of variables used across all the bootstrap samples. } \value{ a list with elements \item{modelInfo}{a matrix with the number of model terms and variables used} \item{oobStat }{a summary of the out-of-bag statistics} \item{bmarsCall }{the original call to \code{bagEarth}} } \author{Max Kuhn} \examples{ \dontrun{ data(trees) fit <- bagEarth(trees[,-3], trees[3]) summary(fit) } } \keyword{manip } caret/man/preProcess.Rd0000644000175000017500000001513012453550065015220 0ustar rbalintrbalint\name{preProcess} \alias{preProcess} \alias{preProcess.default} \alias{predict.preProcess} \title{Pre-Processing of Predictors } \description{ Pre-processing transformation (centering, scaling etc.) can be estimated from the training data and applied to any data set with the same variables. } \usage{ preProcess(x, ...) \method{preProcess}{default}(x, method = c("center", "scale"), thresh = 0.95, pcaComp = NULL, na.remove = TRUE, k = 5, knnSummary = mean, outcome = NULL, fudge = .2, numUnique = 3, verbose = FALSE, ...) \method{predict}{preProcess}(object, newdata, ...) } \arguments{ \item{x}{a matrix or data frame. All variables must be numeric.} \item{method}{a character vector specifying the type of processing. Possible values are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", pca", "ica" and "spatialSign" (see Details below)} \item{thresh}{a cutoff for the cumulative percent of variance to be retained by PCA} \item{pcaComp}{the specific number of PCA components to keep. If specified, this over-rides \code{thresh}} \item{na.remove}{a logical; should missing values be removed from the calculations?} \item{object}{an object of class \code{preProcess}} \item{newdata}{a matrix or data frame of new data to be pre-processed} \item{k}{the number of nearest neighbors from the training set to use for imputation } \item{knnSummary}{function to average the neighbor values per column during imputation} \item{outcome}{a numeric or factor vector for the training set outcomes. This can be used to help estimate the Box-Cox transformation of the predictor variables (see Details below)} \item{fudge}{ a tolerance value: Box-Cox transformation lambda values within +/-fudge will be coerced to 0 and within 1+/-fudge will be coerced to 1 } \item{numUnique}{ how many unique values should \code{y} have to estimate the Box-Cox transformation? } \item{verbose}{a logical: prints a log as the computations proceed} \item{\dots}{additional arguments to pass to \code{\link[fastICA]{fastICA}}, such as \code{n.comp}} } \details{ The Box-Cox, Yeo-Johnson and exponential transformations have been "repurposed" here: they are being used to transform the predictor variables. The Box-Cox transformation was developed for transforming the response variable while another method, the Box-Tidwell transformation, was created to estimate transformations of predictor data. However, the Box-Cox method is simpler, more computationally efficient and is equally effective for estimating power transformations. The Yeo-Johnson transformation is similar to the Box-Cox model but can accommodate predictors with zero and/or negative values (while the predictors values for the Box-Cox transformation must be strictly positive.) The exponential transformation of Manly (1976) can also be used for positive or negative data. The "range" transformation scales the data to be within [0, 1]. If new samples have values larger or smaller than those in the training set, values will be outside of this range. The operations are applied in this order: Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign. This is a departure from versions of \pkg{caret} prior to version 4.76 (where imputation was done first) and is not backwards compatible if bagging was used for imputation. If PCA is requested but centering and scaling are not, the values will still be centered and scaled. Similarly, when ICA is requested, the data are automatically centered and scaled. k-nearest neighbor imputation is carried out by finding the k closest samples (Euclidian distance) in the training set. Imputation via bagging fits a bagged tree model for each predictor (as a function of all the others). This method is simple, accurate and accepts missing values, but it has much higher computational cost. Imputation via medians takes the median of each predictor in the training set, and uses them to fill missing values. This method is simple, fast, and accepts missing values, but treats each predictor independently, and may be inaccurate. A warning is thrown if both PCA and ICA are requested. ICA, as implemented by the \code{\link[fastICA]{fastICA}} package automatically does a PCA decomposition prior to finding the ICA scores. The function will throw an error of any variables in \code{x} has less than two unique values. } \value{ \code{preProcess} results in a list with elements \item{call}{the function call} \item{dim}{the dimensions of \code{x}} \item{bc}{Box-Cox transformation values, see \code{\link{BoxCoxTrans}}} \item{mean}{a vector of means (if centering was requested)} \item{std}{a vector of standard deviations (if scaling or PCA was requested)} \item{rotation}{a matrix of eigenvectors if PCA was requested} \item{method}{the value of\code{method}} \item{thresh}{the value of\code{thresh}} \item{ranges}{a matrix of min and max values for each predictor when \code{method} includes "range" (and \code{NULL} otherwise)} \item{numComp}{the number of principal components required of capture the specified amount of variance} \item{ica}{contains values for the \code{W} and \code{K} matrix of the decomposition} \item{median}{a vector of medians (if median imputation was requested)} } \author{ Max Kuhn, median imputation by Zachary Mayer } \references{ \url{http://topepo.github.io/caret/preprocess.html} Kuhn and Johnson (2013), Applied Predictive Modeling, Springer, New York (chapter 4) Kuhn (2008), Building predictive models in R using the caret (\url{http://www.jstatsoft.org/v28/i05/}) Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations (with discussion). Journal of the Royal Statistical Society B, 26, 211-252. Box, G. E. P. and Tidwell, P. W. (1962) Transformation of the independent variables. Technometrics 4, 531-550. Manly, B. L. (1976) Exponential data transformations. The Statistician, 25, 37 - 42. Yeo, I-K. and Johnson, R. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954-959. } \seealso{\code{\link{BoxCoxTrans}}, \code{\link{expoTrans}} \code{\link[MASS]{boxcox}}, \code{\link[stats]{prcomp}}, \code{\link[fastICA]{fastICA}}, \code{\link{spatialSign}}} \examples{ data(BloodBrain) # one variable has one unique value \dontrun{ preProc <- preProcess(bbbDescr) preProc <- preProcess(bbbDescr[1:100,-3]) training <- predict(preProc, bbbDescr[1:100,-3]) test <- predict(preProc, bbbDescr[101:208,-3]) } } \keyword{ utilities } caret/man/predictors.Rd0000644000175000017500000000316512453550065015256 0ustar rbalintrbalint\name{predictors} \alias{predictors} \alias{predictors.formula} \alias{predictors.terms} \alias{predictors.train} \alias{predictors.default} \alias{predictors.list} \alias{predictors.rfe} \alias{predictors.sbf} \title{ List predictors used in the model} \description{ This class uses a model fit to determine which predictors were used in the final model. } \usage{ predictors(x, ...) \method{predictors}{default}(x, ...) \method{predictors}{formula}(x, ...) \method{predictors}{list}(x, ...) \method{predictors}{rfe}(x, ...) \method{predictors}{sbf}(x, ...) \method{predictors}{terms}(x, ...) \method{predictors}{train}(x, ...) } \arguments{ \item{x}{ a model object, list or terms} \item{\dots}{ not currently used } } \details{ For \code{\link[randomForest]{randomForest}}, \code{\link[party]{cforest}}, \code{\link[party]{ctree}}, \code{\link[rpart]{rpart}}, \code{\link[ipred:bagging]{ipredbagg}}, \code{\link[ipred]{bagging}}, \code{\link[earth]{earth}}, \code{\link[mda]{fda}}, \code{\link[pamr]{pamr.train}}, \code{\link[superpc]{superpc.train}}, \code{\link{bagEarth}} and \code{\link{bagFDA}}, an attempt was made to report the predictors that were actually used in the final model. The \code{predictors} function can be called on the model object (as opposed to the \code{\link{train}}) object) and the package will try to find the appropriate coed (if it exists). In cases where the predictors cannot be determined, \code{NA} is returned. For example, \code{\link[nnet]{nnet}} may return missing values from \code{predictors}. } \value{ a character string of predictors or \code{NA}. } \keyword{models } caret/man/plot.rfe.Rd0000644000175000017500000000365312453550065014633 0ustar rbalintrbalint\name{plot.rfe} \alias{plot.rfe} \alias{ggplot.rfe} \title{ Plot RFE Performance Profiles } \description{ These functions plot the resampling results for the candidate subset sizes evaluated during the recursive feature elimination (RFE) process } \usage{ \method{plot}{rfe}(x, metric = x$metric, ...) \method{ggplot}{rfe}(data = NULL, metric = data$metric[1], output = "layered", ...) } \arguments{ \item{x}{ an object of class \code{\link{rfe}}. } \item{metric}{What measure of performance to plot. Examples of possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be used depending on what metrics have been calculated.} \item{\dots}{\code{plot} only: specifications to be passed to \code{\link[lattice]{xyplot}}. The function automatically sets some arguments (e.g. axis labels) but passing in values here will over-ride the defaults. } \item{data}{ an object of class \code{\link{rfe}}. } \item{output}{either "data", "ggplot" or "layered". The first returns a data frame while the second returns a simple \code{ggplot} object with no layers. The third value returns a plot with a set of layers. } } \details{ These plots show the average performance versus the subset sizes. } \value{ a lattice or ggplot object } \author{Max Kuhn} \references{Kuhn (2008), ``Building Predictive Models in R Using the caret'' (\url{http://www.jstatsoft.org/v28/i05/})} \seealso{ \code{\link{rfe}}, \code{\link[lattice]{xyplot}}, \code{\link[ggplot2]{ggplot}}} \examples{ \dontrun{ data(BloodBrain) x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)]) x <- x[, -findCorrelation(cor(x), .8)] x <- as.data.frame(x) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) plot(lmProfile) plot(lmProfile, metric = "Rsquared") ggplot(lmProfile) } } \keyword{hplot} caret/man/varImp.Rd0000644000175000017500000002436212453550065014340 0ustar rbalintrbalint\name{varImp} \alias{varImp} \alias{varImp.train} \alias{varImp.earth} \alias{varImp.rpart} \alias{varImp.randomForest} \alias{varImp.gbm} \alias{varImp.regbagg} \alias{varImp.classbagg} \alias{varImp.pamrtrained} \alias{varImp.lm} \alias{varImp.mvr} \alias{varImp.bagEarth} \alias{varImp.bagFDA} \alias{varImp.RandomForest} \alias{varImp.rfe} \alias{varImp.dsa} \alias{varImp.fda} \alias{varImp.multinom} %\alias{varImp.gam} \alias{varImp.cubist} \alias{varImp.plsda} \alias{varImp.JRip} \alias{varImp.PART} \alias{varImp.nnet} \alias{varImp.C5.0} \alias{varImp.glmnet} \title{Calculation of variable importance for regression and classification models} \description{ A generic method for calculating variable importance for objects produced by \code{train} and method specific methods } \usage{ \method{varImp}{train}(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...) \method{varImp}{earth}(object, value = "gcv", ...) \method{varImp}{fda}(object, value = "gcv", ...) \method{varImp}{rpart}(object, surrogates = FALSE, competes = TRUE, ...) \method{varImp}{randomForest}(object, ...) \method{varImp}{gbm}(object, numTrees, ...) \method{varImp}{classbagg}(object, ...) \method{varImp}{regbagg}(object, ...) \method{varImp}{pamrtrained}(object, threshold, data, ...) \method{varImp}{lm}(object, ...) \method{varImp}{mvr}(object, estimate = NULL, ...) \method{varImp}{bagEarth}(object, ...) \method{varImp}{bagFDA}(object, ...) \method{varImp}{RandomForest}(object, ...) \method{varImp}{rfe}(object, drop = FALSE, ...) \method{varImp}{dsa}(object, cuts = NULL, ...) \method{varImp}{multinom}(object, ...) \method{varImp}{cubist}(object, weights = c(0.5, 0.5), ...) \method{varImp}{JRip}(object, ...) \method{varImp}{PART}(object, ...) \method{varImp}{C5.0}(object, ...) \method{varImp}{nnet}(object, ...) \method{varImp}{glmnet}(object, lambda = NULL, ...) \method{varImp}{plsda}(object, ...) } \arguments{ \item{object}{an object corresponding to a fitted model} \item{useModel}{use a model based technique for measuring variable importance? This is only used for some models (lm, pls, rf, rpart, gbm, pam and mars)} \item{nonpara}{should nonparametric methods be used to assess the relationship between the features and response (only used with \code{useModel = FALSE} and only passed to \code{filterVarImp}).} \item{scale}{should the importance values be scaled to 0 and 100?} \item{\dots}{parameters to pass to the specific \code{varImp} methods} \item{numTrees}{the number of iterations (trees) to use in a boosted tree model} \item{threshold}{the shrinkage threshold (\code{pamr} models only)} \item{data}{the training set predictors (\code{pamr} models only)} \item{value}{the statistic that will be used to calculate importance: either \code{gcv}, \code{nsubsets}, or \code{rss}} \item{surrogates}{should surrogate splits contribute to the importance calculation?} \item{competes}{should competing splits contribute to the importance calculation?} \item{estimate}{which estimate of performance should be used? See \code{\link[pls]{mvrVal}}} \item{drop}{a logical: should variables not included in the final set be calculated?} \item{cuts}{the number of rule sets to use in the model (for \code{partDSA} only)} \item{weights}{a numeric vector of length two that weighs the usage of variables in the rule conditions and the usage in the linear models (see details below).} \item{lambda}{a single value of the penalty parameter} } \value{ A data frame with class \code{c("varImp.train", "data.frame")} for \code{varImp.train} or a matrix for other models. } \details{ For models that do not have corresponding \code{varImp} methods, see \code{filerVarImp}. Otherwise: \bold{Linear Models}: the absolute value of the t--statistic for each model parameter is used. \bold{Random Forest}: \code{varImp.randomForest} and \code{varImp.RandomForest} are wrappers around the importance functions from the \pkg{randomForest} and \pkg{party} packages, respectively. \bold{Partial Least Squares}: the variable importance measure here is based on weighted sums of the absolute regression coefficients. The weights are a function of the reduction of the sums of squares across the number of PLS components and are computed separately for each outcome. Therefore, the contribution of the coefficients are weighted proportionally to the reduction in the sums of squares. \bold{Recursive Partitioning}: The reduction in the loss function (e.g. mean squared error) attributed to each variable at each split is tabulated and the sum is returned. Also, since there may be candidate variables that are important but are not used in a split, the top competing variables are also tabulated at each split. This can be turned off using the \code{maxcompete} argument in \code{rpart.control}. This method does not currently provide class--specific measures of importance when the response is a factor. \bold{Bagged Trees}: The same methodology as a single tree is applied to all bootstrapped trees and the total importance is returned \bold{Boosted Trees}: \code{varImp.gbm} is a wrapper around the function from that package (see the \pkg{gbm} package vignette) \bold{ Multivariate Adaptive Regression Splines}: MARS models include a backwards elimination feature selection routine that looks at reductions in the generalized cross-validation (GCV) estimate of error. The \code{varImp} function tracks the changes in model statistics, such as the GCV, for each predictor and accumulates the reduction in the statistic when each predictor's feature is added to the model. This total reduction is used as the variable importance measure. If a predictor was never used in any of the MARS basis functions in the final model (after pruning), it has an importance value of zero. Prior to June 2008, the package used an internal function for these calculations. Currently, the \code{varImp} is a wrapper to the \code{\link[earth]{evimp}} function in the \code{earth} package. There are three statistics that can be used to estimate variable importance in MARS models. Using \code{varImp(object, value = "gcv")} tracks the reduction in the generalized cross-validation statistic as terms are added. However, there are some cases when terms are retained in the model that result in an increase in GCV. Negative variable importance values for MARS are set to zero. Alternatively, using \code{varImp(object, value = "rss")} monitors the change in the residual sums of squares (RSS) as terms are added, which will never be negative. Also, the option \code{varImp(object, value =" nsubsets")}, which counts the number of subsets where the variable is used (in the final, pruned model). \bold{Nearest shrunken centroids}: The difference between the class centroids and the overall centroid is used to measure the variable influence (see \code{pamr.predict}). The larger the difference between the class centroid and the overall center of the data, the larger the separation between the classes. The training set predictions must be supplied when an object of class \code{pamrtrained} is given to \code{varImp}. \bold{Cubist}: The Cubist output contains variable usage statistics. It gives the percentage of times where each variable was used in a condition and/or a linear model. Note that this output will probably be inconsistent with the rules shown in the output from \code{\link[Cubist]{summary.cubist}}. At each split of the tree, Cubist saves a linear model (after feature selection) that is allowed to have terms for each variable used in the current split or any split above it. Quinlan (1992) discusses a smoothing algorithm where each model prediction is a linear combination of the parent and child model along the tree. As such, the final prediction is a function of all the linear models from the initial node to the terminal node. The percentages shown in the Cubist output reflects all the models involved in prediction (as opposed to the terminal models shown in the output). The variable importance used here is a linear combination of the usage in the rule conditions and the model. \bold{PART} and \bold{JRip}: For these rule-based models, the importance for a predictor is simply the number of rules that involve the predictor. \bold{C5.0}: C5.0 measures predictor importance by determining the percentage of training set samples that fall into all the terminal nodes after the split. For example, the predictor in the first split automatically has an importance measurement of 100 percent since all samples are affected by this split. Other predictors may be used frequently in splits, but if the terminal nodes cover only a handful of training set samples, the importance scores may be close to zero. The same strategy is applied to rule-based models and boosted versions of the model. The underlying function can also return the number of times each predictor was involved in a split by using the option \code{metric = "usage"}. \bold{Neural Networks}: The method used here is based on Gevrey et al (2003), which uses combinations of the absolute values of the weights. For classification models, the class-specific importances will be the same. \bold{Recursive Feature Elimination}: Variable importance is computed using the ranking method used for feature selection. For the final subset size, the importances for the models across all resamples are averaged to compute an overall value. \bold{Feature Selection via Univariate Filters}, the percentage of resamples that a predictor was selected is determined. In other words, an importance of 0.50 means that the predictor survived the filter in half of the resamples. } \author{Max Kuhn} \references{Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249-264. Quinlan, J. (1992). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, 343-348.} \keyword{ models } caret/man/bag.Rd0000644000175000017500000001235612453550065013633 0ustar rbalintrbalint\name{bag.default} \alias{bag.default} \alias{bag} \alias{bagControl} \alias{predict.bag} \alias{ldaBag} \alias{plsBag} \alias{nbBag} \alias{ctreeBag} \alias{svmBag} \alias{nnetBag} \title{ A General Framework For Bagging } \description{ \code{bag} provides a framework for bagging classification or regression models. The user can provide their own functions for model building, prediction and aggregation of predictions (see Details below). } \usage{ bag(x, ...) \method{bag}{default}(x, y, B = 10, vars = ncol(x), bagControl = NULL, ...) bagControl(fit = NULL, predict = NULL, aggregate = NULL, downSample = FALSE, oob = TRUE, allowParallel = TRUE) ldaBag plsBag nbBag ctreeBag svmBag nnetBag \method{predict}{bag}(object, newdata = NULL, ...) } \arguments{ \item{x}{ a matrix or data frame of predictors } \item{y}{ a vector of outcomes } \item{B}{ the number of bootstrap samples to train over. } \item{bagControl}{ a list of options. } \item{\dots}{ arguments to pass to the model function } \item{fit}{ a function that has arguments \code{x}, \code{y} and \code{...} and produces a model object that can later be used for prediction. Example functions are found in \code{ldaBag}, \code{plsBag}, \code{nbBag}, \code{svmBag} and \code{nnetBag}. } \item{predict}{ a function that generates predictions for each sub-model. The function should have arguments \code{object} and \code{x}. The output of the function can be any type of object (see the example below where posterior probabilities are generated. Example functions are found in \code{ldaBag}, \code{plsBag}, \code{nbBag}, \code{svmBag} and \code{nnetBag}.) } \item{aggregate}{ a function with arguments \code{x} and \code{type}. The function that takes the output of the \code{predict} function and reduces the bagged predictions to a single prediction per sample. the \code{type} argument can be used to switch between predicting classes or class probabilities for classification models. Example functions are found in \code{ldaBag}, \code{plsBag}, \code{nbBag}, \code{svmBag} and \code{nnetBag}. } \item{downSample}{a logical: for classification, should the data set be randomly sampled so that each class has the same number of samples as the smallest class?} \item{oob}{a logical: should out-of-bag statistics be computed and the predictions retained?} \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?} \item{vars}{ an integer. If this argument is not \code{NULL}, a random sample of size \code{vars} is taken of the predictors in each bagging iteration. If \code{NULL}, all predictors are used. } \item{object}{ an object of class \code{bag}. } \item{newdata}{ a matrix or data frame of samples for prediction. Note that this argument must have a non-null value } } \details{ The function is basically a framework where users can plug in any model in to assess the effect of bagging. Examples functions can be found in \code{ldaBag}, \code{plsBag}, \code{nbBag}, \code{svmBag} and \code{nnetBag}. Each has elements \code{fit}, \code{pred} and \code{aggregate}. One note: when \code{vars} is not \code{NULL}, the sub-setting occurs prior to the \code{fit} and \code{predict} functions are called. In this way, the user probably does not need to account for the change in predictors in their functions. When using \code{bag} with \code{\link{train}}, classification models should use \code{type = "prob"} inside of the \code{predict} function so that \code{predict.train(object, newdata, type = "prob")} will work. If a parallel backend is registered, the \pkg{foreach} package is used to train the models in parallel. } \value{ \code{bag} produces an object of class \code{bag} with elements \item{fits }{a list with two sub-objects: the \code{fit} object has the actual model fit for that bagged samples and the \code{vars} object is either \code{NULL} or a vector of integers corresponding to which predictors were sampled for that model} \item{control }{a mirror of the arguments passed into \code{bagControl}} \item{call }{the call} \item{B }{the number of bagging iterations} \item{dims }{the dimensions of the training set} } \author{ Max Kuhn } \examples{ ## A simple example of bagging conditional inference regression trees: data(BloodBrain) ## treebag <- bag(bbbDescr, logBBB, B = 10, ## bagControl = bagControl(fit = ctreeBag$fit, ## predict = ctreeBag$pred, ## aggregate = ctreeBag$aggregate)) ## An example of pooling posterior probabilities to generate class predictions data(mdrr) ## remove some zero variance predictors and linear dependencies mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .95)] ## basicLDA <- train(mdrrDescr, mdrrClass, "lda") ## bagLDA2 <- train(mdrrDescr, mdrrClass, ## "bag", ## B = 10, ## bagControl = bagControl(fit = ldaBag$fit, ## predict = ldaBag$pred, ## aggregate = ldaBag$aggregate), ## tuneGrid = data.frame(vars = c((1:10)*10 , ncol(mdrrDescr)))) } \keyword{models } caret/man/selection.Rd0000644000175000017500000001142612516214271015060 0ustar rbalintrbalint\name{oneSE} \alias{oneSE} \alias{best} \alias{tolerance} \title{Selecting tuning Parameters} \description{ Various functions for setting tuning parameters } \usage{ best(x, metric, maximize) oneSE(x, metric, num, maximize) tolerance(x, metric, tol = 1.5, maximize) } \arguments{ \item{x}{a data frame of tuning parameters and model results, sorted from least complex models to the mst complex} \item{metric}{a string that specifies what summary metric will be used to select the optimal model. By default, possible values are "RMSE" and "Rsquared" for regression and "Accuracy" and "Kappa" for classification. If custom performance metrics are used (via the \code{summaryFunction} argument in \code{\link{trainControl}}, the value of \code{metric} should match one of the arguments. If it does not, a warning is issued and the first metric given by the \code{summaryFunction} is used.} \item{maximize}{a logical: should the metric be maximized or minimized?} \item{num}{the number of resamples (for \code{oneSE} only)} \item{tol}{the acceptable percent tolerance (for \code{tolerance} only)} } \details{ These functions can be used by \code{\link{train}} to select the "optimal" model from a series of models. Each requires the user to select a metric that will be used to judge performance. For regression models, values of \code{"RMSE"} and \code{"Rsquared"} are applicable. Classification models use either \code{"Accuracy"} or \code{"Kappa"} (for unbalanced class distributions. More details on these functions can be found at \url{http://topepo.github.io/caret/training.html#custom}. By default, \code{\link{train}} uses \code{best}. \code{best} simply chooses the tuning parameter associated with the largest (or lowest for \code{"RMSE"}) performance. \code{oneSE} is a rule in the spirit of the "one standard error" rule of Breiman et al. (1984), who suggest that the tuning parameter associated with the best performance may over fit. They suggest that the simplest model within one standard error of the empirically optimal model is the better choice. This assumes that the models can be easily ordered from simplest to most complex (see the Details section below). \code{tolerance} takes the simplest model that is within a percent tolerance of the empirically optimal model. For example, if the largest Kappa value is 0.5 and a simpler model within 3 percent is acceptable, we score the other models using \code{(x - 0.5)/0.5 * 100}. The simplest model whose score is not less than 3 is chosen (in this case, a model with a Kappa value of 0.35 is acceptable). User--defined functions can also be used. The argument \code{selectionFunction} in \code{\link{trainControl}} can be used to pass the function directly or to pass the function by name. } \value{ a row index } \references{Breiman, Friedman, Olshen, and Stone. (1984) \emph{Classification and Regression Trees}. Wadsworth.} \author{Max Kuhn} \note{ In many cases, it is not very clear how to order the models on simplicity. For simple trees and other models (such as PLS), this is straightforward. However, for others it is not. For example, many of the boosting models used by \pkg{caret} have parameters for the number of boosting iterations and the tree complexity (others may also have a learning rate parameter). In this implementation, we order models on number of iterations, then tree depth. Clearly, this is arguable (please email the author for suggestions though). For MARS models, they are orders on the degree of the features, then the number of retained terms. RBF SVM models are ordered first by the cost parameter, then by the kernel parameter while polynomial models are ordered first on polynomial degree, then cost and scale. Neural networks are ordered by the number of hidden units and then the amount of weight decay. k--nearest neighbor models are ordered from most neighbors to least (i.e. smoothest to model jagged decision boundaries). Elastic net models are ordered first on the L1 penalty, then by the L2 penalty. } \seealso{\code{\link{train}}, \code{\link{trainControl}}} \examples{ \dontrun{ # simulate a PLS regression model test <- data.frame(ncomp = 1:5, RMSE = c(3, 1.1, 1.02, 1, 2), RMSESD = .4) best(test, "RMSE", maximize = FALSE) oneSE(test, "RMSE", maximize = FALSE, num = 10) tolerance(test, "RMSE", tol = 3, maximize = FALSE) ### usage example data(BloodBrain) marsGrid <- data.frame(degree = 1, nprune = (1:10) * 3) set.seed(1) marsFit <- train(bbbDescr, logBBB, method = "earth", tuneGrid = marsGrid, trControl = trainControl(method = "cv", number = 10, selectionFunction = "tolerance")) # around 18 terms should yield the smallest CV RMSE } } \keyword{manip} caret/man/predict.knnreg.Rd0000644000175000017500000000143012453550065016006 0ustar rbalintrbalint\name{predict.knnreg} \alias{predict.knnreg} \title{ Predictions from k-Nearest Neighbors Regression Model } \description{ Predict the outcome of a new observation based on k-NN. } \usage{ \method{predict}{knnreg}(object, newdata, ...) } \arguments{ \item{object}{object of class \code{knnreg}.} \item{newdata}{a data frame or matrix of new observations. } \item{...}{additional arguments.} } \details{ This function is a method for the generic function \code{\link{predict}} for class \code{knnreg}. For the details see \code{\link{knnreg}}. This is essentially a copy of \code{\link[ipred]{predict.ipredknn}}. } \value{ a numeric vector } \author{Max Kuhn, Chris Keefer, adapted from \code{\link[class]{knn}} and \code{\link[ipred]{predict.ipredknn}}} \keyword{multivariate} caret/man/extractPrediction.Rd0000644000175000017500000001112412453550065016565 0ustar rbalintrbalint\name{predict.train} \alias{predict.list} \alias{predict.train} \alias{extractPrediction} \alias{extractProb} \title{Extract predictions and class probabilities from train objects} \description{ These functions can be used for a single \code{train} object or to loop through a number of \code{train} objects to calculate the training and test data predictions and class probabilities. } \usage{ \method{predict}{list}(object, ...) \method{predict}{train}(object, newdata = NULL, type = "raw", na.action = na.omit, ...) extractPrediction(models, testX = NULL, testY = NULL, unkX = NULL, unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE) extractProb(models, testX = NULL, testY = NULL, unkX = NULL, unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE) } \arguments{ \item{object}{For \code{predict.train}, an object of class \code{\link{train}}. For \code{predict.list}, a list of objects of class \code{\link{train}}. } \item{newdata}{an optional set of data to predict on. If \code{NULL}, then the original training data are used} \item{type}{either "raw" or "prob", for the number/class predictions or class probabilities, respectively. Class probabilities are not available for all classification models} \item{models}{a list of objects of the class \code{train}. The objects must have been generated with \code{fitBest = FALSE} and \code{returnData = TRUE}.} \item{na.action}{the method for handling missing data } \item{testX}{an optional set of data to predict} \item{testY}{an optional outcome corresponding to the data given in \code{testX}} \item{unkX}{another optional set of data to predict without known outcomes} \item{unkOnly}{a logical to bypass training and test set predictions. This is useful if speed is needed for unknown samples.} \item{verbose}{a logical for printing messages} \item{\ldots}{additional arguments to be passed to other methods} } \details{ These functions are wrappers for the specific prediction functions in each modeling package. In each case, the optimal tuning values given in the \code{tuneValue} slot of the \code{finalModel} object are used to predict. To get simple predictions for a new data set, the \code{predict} function can be used. Limits can be imposed on the range of predictions. See \code{\link{trainControl}} for more information. To get predictions for a series of models at once, a list of \code{\link{train}} objects can be passes to the \code{predict} function and a list of model predictions will be returned. The two extraction functions can be used to get the predictions and observed outcomes at once for the training, test and/or unknown samples at once in a single data frame (instead of a list of just the predictions). These objects can then be passes to \code{\link{plotObsVsPred}} or \code{\link{plotClassProbs}}. } \value{ For \code{predict.train}, a vector of predictions if \code{type = "raw"} or a data frame of class probabilities for \code{type = "probs"}. In the latter case, there are columns for each class. For \code{predict.list}, a list results. Each element is produced by \code{predict.train}. For \code{extractPrediction}, a data frame with columns: \item{obs }{the observed training and test data} \item{pred }{predicted values} \item{model}{the type of model used to predict} \item{object}{the names of the objects within \code{models}. If \code{models} is an un-named list, the values of \code{object} will be "Object1", "Object2" and so on} \item{dataType }{"Training", "Test" or "Unknown" depending on what was specified} For \code{extractProb}, a data frame. There is a column for each class containing the probabilities. The remaining columns are the same as above (although the \code{pred} column is the predicted class) } \author{Max Kuhn} \references{Kuhn (2008), ``Building Predictive Models in R Using the caret'' (\url{http://www.jstatsoft.org/v28/i05/})} \seealso{\code{\link{plotObsVsPred}}, \code{\link{plotClassProbs}}, \code{\link{trainControl}}} \examples{ \dontrun{ knnFit <- train(Species ~ ., data = iris, method = "knn", trControl = trainControl(method = "cv")) rdaFit <- train(Species ~ ., data = iris, method = "rda", trControl = trainControl(method = "cv")) predict(knnFit) predict(knnFit, type = "prob") bothModels <- list(knn = knnFit, tree = rdaFit) predict(bothModels) extractPrediction(bothModels, testX = iris[1:10, -5]) extractProb(bothModels, testX = iris[1:10, -5]) } } \keyword{manip} caret/man/predict.bagEarth.Rd0000644000175000017500000000432212453550065016242 0ustar rbalintrbalint\name{predict.bagEarth} \alias{predict.bagEarth} \alias{predict.bagFDA} \title{Predicted values based on bagged Earth and FDA models} \description{ Predicted values based on bagged Earth and FDA models } \usage{ \method{predict}{bagEarth}(object, newdata = NULL, type = "response", ...) \method{predict}{bagFDA}(object, newdata = NULL, type = "class", ...) } \arguments{ \item{object}{Object of class inheriting from \code{bagEarth}} \item{newdata}{ An optional data frame or matrix in which to look for variables with which to predict. If omitted, the fitted values are used (see note below).} \item{type}{The type of prediction. For bagged \code{\link[earth]{earth}} regression model, \code{type = "response"} will produce a numeric vector of the usual model predictions. \code{\link[earth]{earth}} also allows the user to fit generalized linear models. In this case, \code{type = "response"} produces the inverse link results as a vector. In the case of a binomial generalized linear model, \code{type = "response"} produces a vector of probabilities, \code{type = "class"} generates a factor vector and \code{type = "prob"} produces a two-column matrix with probabilities for both classes (averaged across the individual models). Similarly, for bagged \code{\link[mda]{fda}} models, \code{type = "class"} generates a factor vector and \code{type = "probs"} outputs a matrix of class probabilities.} \item{\dots}{not used} } \value{ a vector of predictions } \author{Max Kuhn} \note{ If the predictions for the original training set are needed, there are two ways to calculate them. First, the original data set can be predicted by each bagged earth model. Secondly, the predictions from each bootstrap sample could be used (but are more likely to overfit). If the original call to \code{bagEarth} or \code{bagFDA} had \code{keepX = TRUE}, the first method is used, otherwise the values are calculated via the second method. } \seealso{\code{\link{bagEarth}}} \examples{ \dontrun{ data(trees) ## out of bag predictions vs just re-predicting the training set fit1 <- bagEarth(Volume ~ ., data = trees, keepX = TRUE) fit2 <- bagEarth(Volume ~ ., data = trees, keepX = FALSE) hist(predict(fit1) - predict(fit2)) } } \keyword{regression} caret/man/bagEarth.Rd0000644000175000017500000000503312453550065014611 0ustar rbalintrbalint\name{bagEarth} \alias{bagEarth} \alias{print.bagEarth} \alias{bagEarth.default} \alias{bagEarth.formula} \title{Bagged Earth} \description{A bagging wrapper for multivariate adaptive regression splines (MARS) via the \code{earth} function} \usage{ \method{bagEarth}{formula}(formula, data = NULL, B = 50, summary = mean, keepX = TRUE, ..., subset, weights, na.action = na.omit) \method{bagEarth}{default}(x, y, weights = NULL, B = 50, summary = mean, keepX = TRUE, ...) } \arguments{ \item{formula}{A formula of the form \code{y ~ x1 + x2 + ...} } \item{x}{matrix or data frame of 'x' values for examples.} \item{y}{matrix or data frame of numeric values outcomes. } \item{weights}{(case) weights for each example - if missing defaults to 1.} \item{data}{Data frame from which variables specified in 'formula' are preferentially to be taken. } \item{subset}{An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) } \item{na.action}{A function to specify the action to be taken if 'NA's are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) } \item{B}{the number of bootstrap samples } \item{summary}{a function with a single argument specifying how the bagged predictions should be summarized } \item{keepX}{a logical: should the original training data be kept?} \item{\dots}{arguments passed to the \code{earth} function} } \details{The function computes a Earth model for each bootstap sample.} \value{ A list with elements \item{fit }{a list of \code{B} Earth fits} \item{B }{the number of bootstrap samples} \item{call }{the function call} \item{x }{either \code{NULL} or the value of \code{x}, depending on the value of \code{keepX}} \item{oob}{a matrix of performance estimates for each bootstrap sample} } \references{J. Friedman, ``Multivariate Adaptive Regression Splines'' (with discussion) (1991). Annals of Statistics, 19/1, 1-141.} \author{Max Kuhn (\code{bagEarth.formula} is based on Ripley's \code{nnet.formula})} \seealso{\code{\link[earth]{earth}}, \code{\link{predict.bagEarth}}} \examples{ \dontrun{ library(mda) library(earth) data(trees) fit1 <- earth(trees[,-3], trees[,3]) fit2 <- bagEarth(trees[,-3], trees[,3], B = 10) } } \keyword{regression} caret/man/findCorrelation.Rd0000644000175000017500000000407412453550065016222 0ustar rbalintrbalint\name{findCorrelation} \alias{findCorrelation} \title{Determine highly correlated variables} \description{ This function searches through a correlation matrix and returns a vector of integers corresponding to columns to remove to reduce pair-wise correlations. } \usage{ findCorrelation(x, cutoff = .90, verbose = FALSE) } \arguments{ \item{x}{A correlation matrix} \item{cutoff}{A numeric value for the pair-wise absolute correlation cutoff} \item{verbose}{A boolean for printing the details} } \details{ The absolute values of pair-wise correlations are considered. If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation. There are several function in the \pkg{subselect} package (\code{\link[subselect:eleaps]{leaps}}, \code{\link[subselect:genetic]{genetic}}, \code{\link[subselect:anneal]{anneal}}) that can also be used to accomplish the same goal. } \value{ A vector of indices denoting the columns to remove. If no correlations meet the criteria, \code{numeric(0)} is returned. } \author{Original R code by Dong Li, modified by Max Kuhn} \seealso{\code{\link[subselect:eleaps]{leaps}}, \code{\link[subselect:genetic]{genetic}}, \code{\link[subselect:anneal]{anneal}}, \code{\link{findLinearCombos}}} \examples{ corrMatrix <- diag(rep(1, 5)) corrMatrix[2, 3] <- corrMatrix[3, 2] <- .7 corrMatrix[5, 3] <- corrMatrix[3, 5] <- -.7 corrMatrix[4, 1] <- corrMatrix[1, 4] <- -.67 corrDF <- expand.grid(row = 1:5, col = 1:5) corrDF$correlation <- as.vector(corrMatrix) levelplot(correlation ~ row+ col, corrDF) findCorrelation(corrMatrix, cutoff = .65, verbose = TRUE) findCorrelation(corrMatrix, cutoff = .99, verbose = TRUE) \dontshow{ removeCols <- findCorrelation(corrMatrix, cutoff = .65, verbose = FALSE) if(!isTRUE(all.equal(corrMatrix[-removeCols, -removeCols], diag(rep(1, 3))))) stop("test 1 failed") if(!isTRUE(all.equal( findCorrelation(corrMatrix, .99, verbose = FALSE), numeric(0)))) stop("test 2 failed") } } \keyword{manip} caret/man/dotplot.varImp.train.Rd0000644000175000017500000000140212453550065017126 0ustar rbalintrbalint\name{dotPlot} \alias{dotPlot} \title{Create a dotplot of variable importance values} \description{ A lattice \code{\link[lattice:xyplot]{dotplot}} is created from an object of class \code{varImp.train}. } \usage{ dotPlot(x, top = min(20, dim(x$importance)[1]), ...) } \arguments{ \item{x}{an object of class \code{varImp.train}} \item{top}{the number of predictors to plot} \item{\dots}{options passed to \code{\link[lattice:xyplot]{dotplot}}} } \value{ an object of class \code{trellis}. } \author{Max Kuhn} \seealso{\code{\link{varImp}}, \code{\link[lattice:xyplot]{dotplot}}} \examples{ data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] knnFit <- train(TrainData, TrainClasses, "knn") knnImp <- varImp(knnFit) dotPlot(knnImp) } \keyword{hplot} caret/man/panel.lift.Rd0000644000175000017500000000420112453550065015124 0ustar rbalintrbalint\name{panel.lift2} \alias{panel.lift} \alias{panel.lift2} \title{ Lattice Panel Functions for Lift Plots } \description{ Two panel functions that be used in conjunction with \code{\link{lift}}. } \usage{ panel.lift(x, y, ...) panel.lift2(x, y, pct = 0, values = NULL, ...) } \arguments{ \item{x}{ the percentage of searched to be plotted in the scatterplot } \item{y}{ the percentage of events found to be plotted in the scatterplot } \item{pct}{ the baseline percentage of true events in the data } \item{values}{ A vector of numbers between 0 and 100 specifying reference values for the percentage of samples found (i.e. the y-axis). Corresponding points on the x-axis are found via interpolation and line segments are shown to indicate how many samples must be tested before these percentages are found. The lines use either the \code{plot.line} or \code{superpose.line} component of the current lattice theme to draw the lines (depending on whether groups were used} \item{\dots}{ options to pass to \code{\link[lattice:panel.xyplot]{panel.xyplot}} } } \details{ \code{panel.lift} plots the data with a simple (black) 45 degree reference line. \code{panel.lift2} is the default for \code{\link{lift}} and plots the data points with a shaded region encompassing the space between to the random model and perfect model trajectories. The color of the region is determined by the lattice \code{reference.line} information (see example below). } \author{ Max Kuhn } \seealso{ \code{\link{lift}}, \code{\link[lattice:panel.xyplot]{panel.xyplot}}, \code{\link[lattice:xyplot]{xyplot}}, \link[lattice:trellis.par.get]{trellis.par.set}} \examples{ set.seed(1) simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)), perfect = sort(runif(200), decreasing = TRUE), random = runif(200)) regionInfo <- trellis.par.get("reference.line") regionInfo$col <- "lightblue" trellis.par.set("reference.line", regionInfo) lift2 <- lift(obs ~ random + perfect, data = simulated) lift2 xyplot(lift2, auto.key = list(columns = 2)) ## use a different panel function xyplot(lift2, panel = panel.lift) } \keyword{hplot } caret/man/modelLookup.Rd0000644000175000017500000000471212453550065015371 0ustar rbalintrbalint\name{modelLookup} \alias{modelLookup} \alias{getModelInfo} \alias{checkInstall} \title{ Tools for Models Available in \code{train} } \description{ These function show information about models and packages that are accessible via \code{\link{train}} } \usage{ modelLookup(model = NULL) getModelInfo(model = NULL, regex = TRUE, ...) checkInstall(pkg) } \arguments{ \item{model}{ a character string associated with the \code{method} argument of \code{\link{train}}. If no value is passed, all models are returned. For \code{getModelInfo}, regular expressions can be used. } \item{regex}{ a logical: should a regular expressions be used? If \code{FALSE}, a simple match is conducted against the whole name of the model. } \item{pkg}{ a character string of package names. } \item{...}{ options to pass to \code{\link[base]{grepl}} } } \details{ \code{modelLookup} is good for getting information related to the tuning parameters for a model. \code{getModelInfo} will return all the functions and metadata associated with a model. Both of these functions will only search within the models bundled in this package. \code{checkInstall} will check to see if packages are installed. If they are not and the session is interactive, an option is given to install the packages using \code{\link[utils]{install.packages}} using that functions default arguments (the missing packages are listed if you would like to install them with other options). If the session is not interactive, an error is thrown. } \value{ \code{modelLookup} produces a data frame with columns \item{model }{a character string for the model code} \item{parameter }{the tuning parameter name} \item{label }{a tuning parameter label (used in plots)} \item{forReg }{a logical; can the model be used for regression?} \item{forClass }{a logical; can the model be used for classification?} \item{probModel }{a logical; does the model produce class probabilities?} \code{getModelInfo} returns a list containing one or more lists of the standard model information. \code{checkInstall} returns not value. } \note{The column \code{seq} is no longer included in the output of \code{modelLookup}.} \author{ Max Kuhn } \seealso{ \code{\link{train}}, \code{\link[utils]{install.packages}}, \code{\link[base]{grepl}} } \examples{ modelLookup() modelLookup("gbm") getModelInfo("pls") getModelInfo("^pls") getModelInfo("pls", regex = FALSE) \dontrun{ checkInstall(getModelInfo("pls")$library) } } \keyword{ utilities } caret/man/confusionMatrix.Rd0000644000175000017500000001310512516214271016257 0ustar rbalintrbalint\name{confusionMatrix} \alias{confusionMatrix.table} \alias{confusionMatrix.default} \alias{confusionMatrix} \title{Create a confusion matrix} \description{ Calculates a cross-tabulation of observed and predicted classes with associated statistics. } \usage{ confusionMatrix(data, ...) \method{confusionMatrix}{default}(data, reference, positive = NULL, dnn = c("Prediction", "Reference"), prevalence = NULL, ...) \method{confusionMatrix}{table}(data, positive = NULL, prevalence = NULL, ...) } \arguments{ \item{data}{a factor of predicted classes (for the default method) or an object of class \code{\link[base]{table}}.} \item{reference}{a factor of classes to be used as the true results} \item{positive}{an optional character string for the factor level that corresponds to a "positive" result (if that makes sense for your data). If there are only two factor levels, the first level will be used as the "positive" result.} \item{dnn}{a character vector of dimnames for the table} \item{prevalence}{a numeric value or matrix for the rate of the "positive" class of the data. When \code{data} has two levels, \code{prevalence} should be a single numeric value. Otherwise, it should be a vector of numeric values with elements for each class. The vector should have names corresponding to the classes.} \item{\dots}{options to be passed to \code{table}. NOTE: do not include \code{dnn} here} } \details{ The functions requires that the factors have exactly the same levels. For two class problems, the sensitivity, specificity, positive predictive value and negative predictive value is calculated using the \code{positive} argument. Also, the prevalence of the "event" is computed from the data (unless passed in as an argument), the detection rate (the rate of true events also predicted to be events) and the detection prevalence (the prevalence of predicted events). Suppose a 2x2 table with notation \tabular{rcc}{ \tab Reference \tab \cr Predicted \tab Event \tab No Event \cr Event \tab A \tab B \cr No Event \tab C \tab D \cr } The formulas used here are: \deqn{Sensitivity = A/(A+C)} \deqn{Specificity = D/(B+D)} \deqn{Prevalence = (A+C)/(A+B+C+D)} \deqn{PPV = (sensitivity * Prevalence)/((sensitivity*Prevalence) + ((1-specificity)*(1-Prevalence)))} \deqn{NPV = (specificity * (1-Prevalence))/(((1-sensitivity)*Prevalence) + ((specificity)*(1-Prevalence)))} \deqn{Detection Rate = A/(A+B+C+D)} \deqn{Detection Prevalence = (A+B)/(A+B+C+D)} \deqn{Balanced Accuracy = (Sensitivity+Specificity)/2} See the references for discussions of the first five formulas. For more than two classes, these results are calculated comparing each factor level to the remaining levels (i.e. a "one versus all" approach). The overall accuracy and unweighted Kappa statistic are calculated. A p-value from McNemar's test is also computed using \code{\link[stats]{mcnemar.test}} (which can produce \code{NA} values with sparse tables). The overall accuracy rate is computed along with a 95 percent confidence interval for this rate (using \code{\link[stats]{binom.test}}) and a one-sided test to see if the accuracy is better than the "no information rate," which is taken to be the largest class percentage in the data. } \value{ a list with elements \item{table}{the results of \code{table} on \code{data} and \code{reference}} \item{positive}{the positive result level} \item{overall}{a numeric vector with overall accuracy and Kappa statistic values} \item{byClass}{the sensitivity, specificity, positive predictive value, negative predictive value, prevalence, detection rate, detection prevalence and balanced accuracy for each class. For two class systems, this is calculated once using the \code{positive} argument} } \note{ If the reference and data factors have the same levels, but in the incorrect order, the function will reorder them to the order of the data and issue a warning. } \author{Max Kuhn} \references{Kuhn, M. (2008), ``Building predictive models in R using the caret package, '' \emph{Journal of Statistical Software}, (\url{http://www.jstatsoft.org/v28/i05/}). Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 1: sensitivity and specificity,'' \emph{British Medical Journal}, vol 308, 1552. Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 2: predictive values,'' \emph{British Medical Journal}, vol 309, 102. Velez, D.R., et. al. (2008) ``A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction.,'' \emph{Genetic Epidemiology}, vol 4, 306. } \seealso{\code{\link{as.table.confusionMatrix}}, \code{\link{as.matrix.confusionMatrix}}, \code{\link{sensitivity}}, \code{\link{specificity}}, \code{\link{posPredValue}}, \code{\link{negPredValue}}, \code{\link{print.confusionMatrix}}, \code{\link[stats]{binom.test}}} \examples{ ################### ## 2 class example lvs <- c("normal", "abnormal") truth <- factor(rep(lvs, times = c(86, 258)), levels = rev(lvs)) pred <- factor( c( rep(lvs, times = c(54, 32)), rep(lvs, times = c(27, 231))), levels = rev(lvs)) xtab <- table(pred, truth) confusionMatrix(xtab) confusionMatrix(pred, truth) confusionMatrix(xtab, prevalence = 0.25) ################### ## 3 class example confusionMatrix(iris$Species, sample(iris$Species)) newPrior <- c(.05, .8, .15) names(newPrior) <- levels(iris$Species) confusionMatrix(iris$Species, sample(iris$Species)) } \keyword{utilities} caret/man/createDataPartition.Rd0000644000175000017500000001063612516214271017024 0ustar rbalintrbalint\name{createDataPartition} \alias{createDataPartition} \alias{createResample} \alias{createFolds} \alias{createMultiFolds} \alias{createTimeSlices} \title{Data Splitting functions} \description{ A series of test/training partitions are created using \code{createDataPartition} while \code{createResample} creates one or more bootstrap samples. \code{createFolds} splits the data into \code{k} groups while \code{createTimeSlices} creates cross-validation sample information to be used with time series data. } \usage{ createDataPartition(y, times = 1, p = 0.5, list = TRUE, groups = min(5, length(y))) createResample(y, times = 10, list = TRUE) createFolds(y, k = 10, list = TRUE, returnTrain = FALSE) createMultiFolds(y, k = 10, times = 5) createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0) } \arguments{ \item{y}{a vector of outcomes. For \code{createTimeSlices}, these should be in chronological order.} \item{times}{the number of partitions to create} \item{p}{the percentage of data that goes to training} \item{list}{logical - should the results be in a list (\code{TRUE}) or a matrix with the number of rows equal to \code{floor(p * length(y))} and \code{times} columns.} \item{groups}{for numeric \code{y}, the number of breaks in the quantiles (see below)} \item{k}{an integer for the number of folds.} \item{returnTrain}{a logical. When true, the values returned are the sample positions corresponding to the data used during training. This argument only works in conjunction with \code{list = TRUE}} \item{initialWindow}{The initial number of consecutive values in each training set sample} \item{horizon}{The number of consecutive values in test set sample} \item{fixedWindow}{A logical: if \code{FALSE}, the training set always start at the first sample.} \item{skip}{An integer specifying how many (if any) resamples to skip to thin the total amount.} } \details{ For bootstrap samples, simple random sampling is used. For other data splitting, the random sampling is done within the levels of \code{y} when \code{y} is a factor in an attempt to balance the class distributions within the splits. For numeric \code{y}, the sample is split into groups sections based on percentiles and sampling is done within these subgroups. For \code{createDataPartition}, the number of percentiles is set via the \code{groups} argument. For \code{createFolds} and \code{createMultiFolds}, the number of groups is set dynamically based on the sample size and \code{k}. For smaller samples sizes, these two functions may not do stratified splitting and, at most, will split the data into quartiles. Also, for \code{createDataPartition}, very small class sizes (<= 3) the classes may not show up in both the training and test data For multiple k-fold cross-validation, completely independent folds are created. The names of the list objects will denote the fold membership using the pattern "Foldi.Repj" meaning the ith section (of k) of the jth cross-validation set (of \code{times}). Note that this function calls \code{createFolds} with \code{list = TRUE} and \code{returnTrain = TRUE}. Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin< techniques that move the training and test sets in time. \code{createTimeSlices} can create the indices for this type of splitting. } \value{ A list or matrix of row position integers corresponding to the training data } \references{ \url{http://topepo.github.io/caret/splitting.html} Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. \url{https://www.otexts.org/fpp} } \author{Max Kuhn, \code{createTimeSlices} by Tony Cooper} \examples{ data(oil) createDataPartition(oilType, 2) x <- rgamma(50, 3, .5) inA <- createDataPartition(x, list = FALSE) plot(density(x[inA])) rug(x[inA]) points(density(x[-inA]), type = "l", col = 4) rug(x[-inA], col = 4) createResample(oilType, 2) createFolds(oilType, 10) createFolds(oilType, 5, FALSE) createFolds(rnorm(21)) createTimeSlices(1:9, 5, 1, fixedWindow = FALSE) createTimeSlices(1:9, 5, 1, fixedWindow = TRUE) createTimeSlices(1:9, 5, 3, fixedWindow = TRUE) createTimeSlices(1:9, 5, 3, fixedWindow = FALSE) createTimeSlices(1:15, 5, 3) createTimeSlices(1:15, 5, 3, skip = 2) createTimeSlices(1:15, 5, 3, skip = 3) } \keyword{utilities} caret/man/classDist.Rd0000644000175000017500000000643612453550065015035 0ustar rbalintrbalint\name{classDist} \Rdversion{1.1} \alias{classDist.default} \alias{classDist} \alias{predict.classDist} \title{ Compute and predict the distances to class centroids } \description{ This function computes the class centroids and covariance matrix for a training set for determining Mahalanobis distances of samples to each class centroid. } \usage{ classDist(x, ...) \method{classDist}{default}(x, y, groups = 5, pca = FALSE, keep = NULL, ...) \method{predict}{classDist}(object, newdata, trans = log, ...) } \arguments{ \item{x}{a matrix or data frame of predictor variables} \item{y}{a numeric or factor vector of class labels} \item{groups}{an integer for the number of bins for splitting a numeric outcome} \item{pca}{a logical: should principal components analysis be applied to the dataset prior to splitting the data by class?} \item{keep}{an integer for the number of PCA components that should by used to predict new samples (\code{NULL} uses all within a tolerance of \code{sqrt(.Machine$double.eps)})} \item{object}{an object of class \code{classDist}} \item{newdata}{a matrix or data frame. If \code{vars} was previously specified, these columns should be in \code{newdata}} \item{trans}{an optional function that can be applied to each class distance. \code{trans = NULL} will not apply a function} \item{\dots}{optional arguments to pass (not currently used)} } \details{ For factor outcomes, the data are split into groups for each class and the mean and covariance matrix are calculated. These are then used to compute Mahalanobis distances to the class centers (using \code{predict.classDist} The function will check for non-singular matrices. For numeric outcomes, the data are split into roughly equal sized bins based on \code{groups}. Percentiles are used to split the data. } \value{ for \code{classDist}, an object of class \code{classDist} with elements: \item{values }{a list with elements for each class. Each element contains a mean vector for the class centroid and the inverse of the class covariance matrix} \item{classes}{a character vector of class labels} \item{pca}{the results of \code{\link[stats]{prcomp}} when \code{pca = TRUE}} \item{call}{the function call} \item{p}{the number of variables} \item{n}{a vector of samples sizes per class} For \code{predict.classDist}, a matrix with columns for each class. The columns names are the names of the class with the prefix \code{dist.}. In the case of numeric \code{y}, the class labels are the percentiles. For example, of \code{groups = 9}, the variable names would be \code{dist.11.11}, \code{dist.22.22}, etc. } \author{ Max Kuhn } \references{Forina et al. CAIMAN brothers: A family of powerful classification and class modeling techniques. Chemometrics and Intelligent Laboratory Systems (2009) vol. 96 (2) pp. 239-245} \seealso{\code{\link[stats]{mahalanobis}}} \examples{ trainSet <- sample(1:150, 100) distData <- classDist(iris[trainSet, 1:4], iris$Species[trainSet]) newDist <- predict(distData, iris[-trainSet, 1:4]) splom(newDist, groups = iris$Species[-trainSet]) } \keyword{ manip } caret/man/calibration.Rd0000644000175000017500000001120312516214271015353 0ustar rbalintrbalint\name{calibration} \alias{calibration} \alias{calibration.formula} \alias{calibration.default} \alias{xyplot.calibration} \alias{panel.calibration} \title{ Probability Calibration Plot } \description{ For classification models, this function creates a 'calibration plot' that describes how consistent model probabilities are with observed event rates. } \usage{ calibration(x, ...) \method{calibration}{formula}(x, data = NULL, class = NULL, cuts = 11, subset = TRUE, lattice.options = NULL, ...) \method{xyplot}{calibration}(x, data, ...) panel.calibration(...) } \arguments{ \item{x}{ a \code{lattice} formula (see \code{\link[lattice:xyplot]{xyplot}} for syntax) where the left-hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model columns corresponding to a numeric ranking variable for a model (e.g. class probabilities). The classification variable should have two levels. } \item{data}{ For \code{calibration.formula}, a data frame (or more precisely, anything that is a valid \code{envir} argument in \code{eval}, e.g., a list or an environment) containing values for any variables in the formula, as well as \code{groups} and \code{subset} if applicable. If not found in \code{data}, or if \code{data} is unspecified, the variables are looked for in the environment of the formula. This argument is not used for \code{xyplot.calibration}. } \item{class}{ a character string for the class of interest } \item{cuts}{ If a single number this indicates the number of splits of the data are used to create the plot. By default, it uses as many cuts as there are rows in \code{data}. If a vector, these are the actual cuts that will be used. } \item{subset}{ An expression that evaluates to a logical or integer indexing vector. It is evaluated in \code{data}. Only the resulting rows of \code{data} are used for the plot. } \item{lattice.options}{ A list that could be supplied to \code{\link[lattice:lattice.options]{lattice.options}} } \item{\dots}{ options to pass through to \code{\link[lattice:xyplot]{xyplot}} or the panel function (not used in \code{calibration.formula}). } } \details{ \code{calibration.formula} is used to process the data and \code{xyplot.calibration} is used to create the plot. To construct the calibration plot, the following steps are used for each model: \enumerate{ \item The data are split into \code{cuts - 1} roughly equal groups by their class probabilities \item the number of samples with true results equal to \code{class} are determined \item the event rate is determined for each bin} \code{xyplot.calibration} produces a plot of the observed event rate by the mid-point of the bins. This implementation uses the \pkg{lattice} function \code{\link[lattice:xyplot]{xyplot}}, so plot elements can be changed via panel functions, \code{\link[lattice:trellis.par.get]{trellis.par.set}} or other means. \code{calibration} uses the panel function \code{\link{panel.calibration}} by default, but it can be changed by passing that argument into \code{xyplot.calibration}. The following elements are set by default in the plot but can be changed by passing new values into \code{xyplot.calibration}: \code{xlab = "Bin Midpoint"}, \code{ylab = "Observed Event Percentage"}, \code{type = "o"}, \code{ylim = extendrange(c(0, 100))},\code{xlim = extendrange(c(0, 100))} and \code{panel = panel.calibration} } \value{ \code{calibration.formula} returns a list with elements: \item{data}{the data used for plotting} \item{cuts}{the number of cuts} \item{class}{the event class} \item{probNames}{the names of the model probabilities} \code{xyplot.calibration} returns a \pkg{lattice} object } \author{ Max Kuhn, some \pkg{lattice} code and documentation by Deepayan Sarkar } \seealso{ \code{\link[lattice:xyplot]{xyplot}}, \code{\link[lattice:trellis.par.get]{trellis.par.set}}} \examples{ \dontrun{ data(mdrr) mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .5)] inTrain <- createDataPartition(mdrrClass) trainX <- mdrrDescr[inTrain[[1]], ] trainY <- mdrrClass[inTrain[[1]]] testX <- mdrrDescr[-inTrain[[1]], ] testY <- mdrrClass[-inTrain[[1]]] library(MASS) ldaFit <- lda(trainX, trainY) qdaFit <- qda(trainX, trainY) testProbs <- data.frame(obs = testY, lda = predict(ldaFit, testX)$posterior[,1], qda = predict(qdaFit, testX)$posterior[,1]) calibration(obs ~ lda + qda, data = testProbs) calPlotData <- calibration(obs ~ lda + qda, data = testProbs) calPlotData xyplot(calPlotData, auto.key = list(columns = 2)) } } \keyword{hplot} caret/man/knnreg.Rd0000644000175000017500000000475212453550065014367 0ustar rbalintrbalint\name{knnreg} \alias{knnreg} \alias{knnregTrain} \alias{knnreg.formula} \alias{knnreg.default} \alias{knnreg.matrix} \alias{knnreg.data.frame} \alias{knnreg} \title{ k-Nearest Neighbour Regression } \description{ $k$-nearest neighbour regression that can return the average value for the neighbours. } \usage{ \method{knnreg}{default}(x, ...) \method{knnreg}{formula}(formula, data, subset, na.action, k = 5, ...) \method{knnreg}{matrix}(x, y, k = 5, ...) \method{knnreg}{data.frame}(x, y, k = 5, ...) knnregTrain(train, test, y, k = 5, use.all=TRUE) } \arguments{ \item{formula}{a formula of the form \code{lhs ~ rhs} where \code{lhs} is the response variable and \code{rhs} a set of predictors.} \item{data}{optional data frame containing the variables in the model formula.} \item{subset}{optional vector specifying a subset of observations to be used.} \item{na.action}{function which indicates what should happen when the data contain \code{NA}s.} \item{k}{number of neighbours considered.} \item{x}{a matrix or data frame of training set predictors.} \item{y}{a numeric vector of outcomes.} \item{...}{additional parameters to pass to \code{knnregTrain}.} \item{train}{matrix or data frame of training set cases.} \item{test}{matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case.} \item{use.all}{controls handling of ties. If true, all distances equal to the \code{k}th largest are included. If false, a random selection of distances equal to the \code{k}th is chosen to use exactly \code{k} neighbours.} } \details{ \code{knnreg} is similar to \code{\link[ipred]{ipredknn}} and \code{knnregTrain} is a modification of \code{\link[class]{knn}}. The underlying C code from the \code{class} package has been modified to return average outcome. } \value{ An object of class \code{knnreg}. See \code{\link{predict.knnreg}}. } \author{\code{\link[class]{knn}} by W. N. Venables and B. D. Ripley and \code{\link[ipred]{ipredknn}} by Torsten.Hothorn , modifications by Max Kuhn and Chris Keefer } \examples{ data(BloodBrain) inTrain <- createDataPartition(logBBB, p = .8)[[1]] trainX <- bbbDescr[inTrain,] trainY <- logBBB[inTrain] testX <- bbbDescr[-inTrain,] testY <- logBBB[-inTrain] fit <- knnreg(trainX, trainY, k = 3) plot(testY, predict(fit, testX)) } \keyword{multivariate} caret/man/lattice.rfe.Rd0000644000175000017500000000457612453550065015307 0ustar rbalintrbalint\name{lattice.rfe} \Rdversion{1.1} \alias{xyplot.rfe} \alias{stripplot.rfe} \alias{densityplot.rfe} \alias{histogram.rfe} \title{Lattice functions for plotting resampling results of recursive feature selection} \description{ A set of lattice functions are provided to plot the resampled performance estimates (e.g. classification accuracy, RMSE) over different subset sizes. } \usage{ \method{histogram}{rfe}(x, data = NULL, metric = x$metric, ...) \method{densityplot}{rfe}(x, data = NULL, metric = x$metric, ...) \method{xyplot}{rfe}(x, data = NULL, metric = x$metric, ...) \method{stripplot}{rfe}(x, data = NULL, metric = x$metric, ...) } \arguments{ \item{x}{ An object produced by \code{\link{rfe}} } \item{data}{This argument is not used } \item{metric}{A character string specifying the single performance metric that will be plotted } \item{\dots}{arguments to pass to either \code{\link[lattice:histogram]{histogram}}, \code{\link[lattice:histogram]{densityplot}}, \code{\link[lattice:xyplot]{xyplot}} or \code{\link[lattice:xyplot]{stripplot}}} } \details{ By default, only the resampling results for the optimal model are saved in the \code{rfe} object. The function \code{\link{rfeControl}} can be used to save all the results using the \code{returnResamp} argument. If leave-one-out or out-of-bag resampling was specified, plots cannot be produced (see the \code{method} argument of \code{\link{rfeControl}}) } \value{A lattice plot object} \author{Max Kuhn} \seealso{\code{\link{rfe}}, \code{\link{rfeControl}}, \code{\link[lattice:histogram]{histogram}}, \code{\link[lattice:histogram]{densityplot}}, \code{\link[lattice:xyplot]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}}} \examples{ \dontrun{ library(mlbench) n <- 100 p <- 40 sigma <- 1 set.seed(1) sim <- mlbench.friedman1(n, sd = sigma) x <- cbind(sim$x, matrix(rnorm(n * p), nrow = n)) y <- sim$y colnames(x) <- paste("var", 1:ncol(x), sep = "") normalization <- preProcess(x) x <- predict(normalization, x) x <- as.data.frame(x) subsets <- c(10, 15, 20, 25) ctrl <- rfeControl( functions = lmFuncs, method = "cv", verbose = FALSE, returnResamp = "all") lmProfile <- rfe(x, y, sizes = subsets, rfeControl = ctrl) xyplot(lmProfile) stripplot(lmProfile) histogram(lmProfile) densityplot(lmProfile) } } \keyword{hplot} caret/man/plot.gafs.Rd0000644000175000017500000000351712453550065014776 0ustar rbalintrbalint\name{plot.gafs} \alias{plot.safs} \alias{plot.gafs} \title{ Plot Method for the gafs and safs Classes } \description{ Plot the performance values versus search iteration } \usage{ \method{plot}{gafs}(x, metric = x$control$metric["external"], estimate = c("internal", "external"), output = "ggplot", ...) \method{plot}{safs}(x, metric = x$control$metric["external"], estimate = c("internal", "external"), output = "ggplot", ...) } \arguments{ \item{x}{an object of class \code{\link{gafs}} or \code{\link{safs}}} \item{metric}{the measure of performance to plot (e.g. RMSE, accuracy, etc)} \item{estimate}{the type of estimate: either "internal" or "external"} \item{output}{either "data", "ggplot" or "lattice"} \item{\dots}{options passed to \code{\link[lattice]{xyplot}}} } \details{ The mean (averaged over the resamples) is plotted against the search iteration using a scatter plot. When \code{output = "data"}, the unaveraged data are returned with columns for all the performance metrics and the resample indicator. } \value{ Either a data frame, ggplot object or lattice object } \author{ Max Kuhn } \seealso{\code{\link{gafs}}, \code{\link{safs}}, \code{\link[ggplot2]{ggplot}}, \code{\link[lattice]{xyplot}} } \examples{ \dontrun{ set.seed(1) train_data <- twoClassSim(100, noiseVars = 10) test_data <- twoClassSim(10, noiseVars = 10) ## A short example ctrl <- safsControl(functions = rfSA, method = "cv", number = 3) rf_search <- safs(x = train_data[, -ncol(train_data)], y = train_data$Class, iters = 50, safsControl = ctrl) plot(rf_search) plot(rf_search, output = "lattice", auto.key = list(columns = 2)) plot_data <- plot(rf_search, output = "data") summary(plot_data) } } \keyword{hplot} caret/man/confusionMatrix.train.Rd0000644000175000017500000000537412453550065017410 0ustar rbalintrbalint\name{confusionMatrix.train} \alias{confusionMatrix.train} \alias{confusionMatrix.rfe} \alias{confusionMatrix.sbf} \title{Estimate a Resampled Confusion Matrix} \description{ Using a \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} object, determine a confusion matrix based on the resampling procedure} \usage{ \method{confusionMatrix}{train}(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) \method{confusionMatrix}{rfe}(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) \method{confusionMatrix}{sbf}(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) } \arguments{ \item{data}{an object of class \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} that did not use out-of-bag resampling or leave-one-out cross-validation.} \item{norm}{a character string indicating how the table entries should be normalized. Valid values are "none", "overall" or "average". } \item{dnn}{a character vector of dimnames for the table} \item{\dots}{not used here} } \details{ When \code{\link{train}} is used for tuning a model, it tracks the confusion matrix cell entries for the hold-out samples. These can be aggregated and used for diagnostic purposes. For \code{\link{train}}, the matrix is estimated for the final model tuning parameters determined by \code{\link{train}}. For \code{\link{rfe}}, the matrix is associated with the optimal number of variables. There are several ways to show the table entries. Using \code{norm = "none"} will show the frequencies of samples on each of the cells (across all resamples). \code{norm = "overall"} first divides the cell entries by the total number of data points in the table, then averages these percentages. \code{norm = "average"} takes the raw, aggregate cell counts across resamples and divides by the number of resamples (i.e. to yield an average count for each cell). } \value{ a list of class \code{confusionMatrix.train}, \code{confusionMatrix.rfe} or \code{confusionMatrix.sbf} with elements \item{table}{the normalized matrix} \item{norm}{an echo fo the call} \item{text}{a character string with details about the resampling procedure (e.g. "Bootstrapped (25 reps) Confusion Matrix"} } \author{Max Kuhn} \seealso{\code{\link{confusionMatrix}}, \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}}, \code{\link{trainControl}}} \examples{ data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] knnFit <- train(TrainData, TrainClasses, method = "knn", preProcess = c("center", "scale"), tuneLength = 10, trControl = trainControl(method = "cv")) confusionMatrix(knnFit) confusionMatrix(knnFit, "average") confusionMatrix(knnFit, "none") } \keyword{utilities} caret/man/update.train.Rd0000644000175000017500000000333212453550065015472 0ustar rbalintrbalint\name{update.train} \alias{update.train} \title{ Update or Re-fit a Model } \description{ \code{update} allows a user to over-ride the tuning parameter selection process by specifying a set of tuning parameters or to update the model object to the latest version of this package. } \usage{ \method{update}{train}(object, param = NULL, ...) } \arguments{ \item{object}{ an object of class \code{\link{train}} } \item{param}{ a data frame or named list of all tuning parameters } \item{\dots}{ not currently used } } \details{ If the model object was created with version 5.17-7 or earlier, the underlying package structure was different. To make old \code{\link{train}} objects consistent with the new structure, use \code{param = NULL} to get the same object back with updates. To update the model parameters, the training data must be stored in the model object (see the option \code{returnData} in \code{\link{trainControl}}. Also, all tuning parameters must be specified in the \code{param} slot. All other options are held constant, including the original pre-processing (if any), options passed in using code{...} and so on. When printing, the verbiage "The tuning parameter was set manually." is used to describe how the tuning parameters were created. } \value{ a new \code{\link{train}} object } \author{ Max Kuhn } \seealso{ \code{\link{train}}, \code{\link{trainControl}} } \examples{ \dontrun{ data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] knnFit1 <- train(TrainData, TrainClasses, method = "knn", preProcess = c("center", "scale"), tuneLength = 10, trControl = trainControl(method = "cv")) update(knnFit1, list(.k = 3)) } } \keyword{ models } caret/man/bagFDA.Rd0000644000175000017500000000503412453550065014141 0ustar rbalintrbalint\name{bagFDA} \alias{bagFDA} \alias{print.bagFDA} \alias{bagFDA.default} \alias{bagFDA.formula} \title{Bagged FDA} \description{A bagging wrapper for flexible discriminant analysis (FDA) using multivariate adaptive regression splines (MARS) basis functions} \usage{ bagFDA(x, ...) \method{bagFDA}{formula}(formula, data = NULL, B = 50, keepX = TRUE, ..., subset, weights, na.action = na.omit) \method{bagFDA}{default}(x, y, weights = NULL, B = 50, keepX = TRUE, ...) } \arguments{ \item{formula}{A formula of the form \code{y ~ x1 + x2 + ...} } \item{x}{matrix or data frame of 'x' values for examples.} \item{y}{matrix or data frame of numeric values outcomes. } \item{weights}{(case) weights for each example - if missing defaults to 1.} \item{data}{Data frame from which variables specified in 'formula' are preferentially to be taken. } \item{subset}{An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) } \item{na.action}{A function to specify the action to be taken if 'NA's are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) } \item{B}{the number of bootstrap samples } \item{keepX}{a logical: should the original training data be kept?} \item{\dots}{arguments passed to the \code{mars} function} } \details{The function computes a FDA model for each bootstap sample.} \value{ A list with elements \item{fit }{a list of \code{B} FDA fits} \item{B }{the number of bootstrap samples} \item{call }{the function call} \item{x }{either \code{NULL} or the value of \code{x}, depending on the value of \code{keepX}} \item{oob}{a matrix of performance estimates for each bootstrap sample} } \references{J. Friedman, ``Multivariate Adaptive Regression Splines'' (with discussion) (1991). Annals of Statistics, 19/1, 1-141.} \author{Max Kuhn (\code{bagFDA.formula} is based on Ripley's \code{nnet.formula})} \seealso{\code{\link[mda]{fda}}, \code{\link{predict.bagFDA}}} \examples{ library(mlbench) library(earth) data(Glass) set.seed(36) inTrain <- sample(1:dim(Glass)[1], 150) trainData <- Glass[ inTrain, ] testData <- Glass[-inTrain, ] baggedFit <- bagFDA(Type ~ ., trainData) confusionMatrix(predict(baggedFit, testData[, -10]), testData[, 10]) } \keyword{regression} caret/man/rfe.Rd0000644000175000017500000001521312453550065013651 0ustar rbalintrbalint\name{rfe} \alias{rfe} \alias{rfe.default} \alias{rfeIter} \alias{predict.rfe} \alias{update.rfe} \title{Backwards Feature Selection} \description{ A simple backwards selection, a.k.a. recursive feature selection (RFE), algorithm } \usage{ rfe(x, ...) \method{rfe}{default}(x, y, sizes = 2^(2:4), metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), rfeControl = rfeControl(), ...) rfeIter(x, y, testX, testY, sizes, rfeControl = rfeControl(), label = "", seeds = NA, ...) \method{update}{rfe}(object, x, y, size, ...) \method{predict}{rfe}(object, newdata, ...) } \arguments{ \item{x}{a matrix or data frame of predictors for model training. This object must have unique column names.} \item{y}{a vector of training set outcomes (either numeric or factor) } \item{testX}{a matrix or data frame of test set predictors. This must have the same column names as \code{x}} \item{testY}{a vector of test set outcomes } \item{sizes}{a numeric vector of integers corresponding to the number of features that should be retained} \item{metric}{a string that specifies what summary metric will be used to select the optimal model. By default, possible values are "RMSE" and "Rsquared" for regression and "Accuracy" and "Kappa" for classification. If custom performance metrics are used (via the \code{functions} argument in \code{\link{rfeControl}}, the value of \code{metric} should match one of the arguments.} \item{maximize}{a logical: should the metric be maximized or minimized?} \item{rfeControl}{a list of options, including functions for fitting and prediction. The web page \url{http://topepo.github.io/caret/featureselection.html#rfe} has more details and examples related to this function.} \item{object}{an object of class \code{rfe}} \item{size}{a single integers corresponding to the number of features that should be retained in the updated model} \item{newdata}{a matrix or data frame of new samples for prediction} \item{label}{an optional character string to be printed when in verbose mode.} \item{seeds}{an optional vector of integers for the size. The vector should have length of \code{length(sizes)+1}} \item{\dots}{options to pass to the model fitting function (ignored in \code{predict.rfe})} } \details{ More details on this function can be found at \url{http://topepo.github.io/caret/featureselection.html}. This function implements backwards selection of predictors based on predictor importance ranking. The predictors are ranked and the less important ones are sequentially eliminated prior to modeling. The goal is to find a subset of predictors that can be used to produce an accurate model. The web page \url{http://topepo.github.io/caret/featureselection.html#rfe} has more details and examples related to this function. \code{rfe} can be used with "explicit parallelism", where different resamples (e.g. cross-validation group) can be split up and run on multiple machines or processors. By default, \code{rfe} will use a single processor on the host machine. As of version 4.99 of this package, the framework used for parallel processing uses the \pkg{foreach} package. To run the resamples in parallel, the code for \code{rfe} does not change; prior to the call to \code{rfe}, a parallel backend is registered with \pkg{foreach} (see the examples below). \code{rfeIter} is the basic algorithm while \code{rfe} wraps these operations inside of resampling. To avoid selection bias, it is better to use the function \code{rfe} than \code{rfeIter}. When updating a model, if the entire set of resamples were not saved using \code{rfeControl(returnResamp = "final")}, the existing resamples are removed with a warning. } \value{ A list with elements \item{finalVariables}{a list of size \code{length(sizes) + 1} containing the column names of the ``surviving'' predictors at each stage of selection. The first element corresponds to all the predictors (i.e. \code{size = ncol(x)})} \item{pred }{a data frame with columns for the test set outcome, the predicted outcome and the subset size.} } \author{Max Kuhn} \seealso{\code{\link{rfeControl}}} \examples{ \dontrun{ data(BloodBrain) x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)]) x <- x[, -findCorrelation(cor(x), .8)] x <- as.data.frame(x) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) set.seed(1) lmProfile2 <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, rerank = TRUE, number = 200)) xyplot(lmProfile$results$RMSE + lmProfile2$results$RMSE ~ lmProfile$results$Variables, type = c("g", "p", "l"), auto.key = TRUE) rfProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = rfFuncs)) bagProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = treebagFuncs)) set.seed(1) svmProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = caretFuncs, number = 200), ## pass options to train() method = "svmRadial") ## classification data(mdrr) mdrrDescr <- mdrrDescr[,-nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .8)] set.seed(1) inTrain <- createDataPartition(mdrrClass, p = .75, list = FALSE)[,1] train <- mdrrDescr[ inTrain, ] test <- mdrrDescr[-inTrain, ] trainClass <- mdrrClass[ inTrain] testClass <- mdrrClass[-inTrain] set.seed(2) ldaProfile <- rfe(train, trainClass, sizes = c(1:10, 15, 30), rfeControl = rfeControl(functions = ldaFuncs, method = "cv")) plot(ldaProfile, type = c("o", "g")) postResample(predict(ldaProfile, test), testClass) } ####################################### ## Parallel Processing Example via multicore \dontrun{ library(doMC) ## Note: if the underlying model also uses foreach, the ## number of cores specified above will double (along with ## the memory requirements) registerDoMC(cores = 2) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) } } \keyword{models} caret/man/safsControl.Rd0000644000175000017500000001746112453550065015401 0ustar rbalintrbalint\name{safsControl} \alias{safsControl} \alias{gafsControl} \title{Control parameters for GA and SA feature selection} \description{ Control the computational nuances of the \code{\link{gafs}} and \code{\link{safs}} functions } \usage{ gafsControl(functions = NULL, method = "repeatedcv", metric = NULL, maximize = NULL, number = ifelse(grepl("cv", method), 10, 25), repeats = ifelse(grepl("cv", method), 1, 5), verbose = FALSE, returnResamp = "final", p = 0.75, index = NULL, indexOut = NULL, seeds = NULL, holdout = 0, genParallel = FALSE, allowParallel = TRUE) safsControl(functions = NULL, method = "repeatedcv", metric = NULL, maximize = NULL, number = ifelse(grepl("cv", method), 10, 25), repeats = ifelse(grepl("cv", method), 1, 5), verbose = FALSE, returnResamp = "final", p = 0.75, index = NULL, indexOut = NULL, seeds = NULL, holdout = 0, improve = Inf, allowParallel = TRUE) } \arguments{ \item{functions}{a list of functions for model fitting, prediction etc (see Details below)} \item{method}{The resampling method: \code{boot}, \code{boot632}, \code{cv}, \code{repeatedcv}, \code{LOOCV}, \code{LGOCV} (for repeated training/test splits)} \item{metric}{a two-element string that specifies what summary metric will be used to select the optimal number of iterations from the external fitness value and which metric should guide subset selection. If specified, this vector should have names \code{"internal"} and \code{"external"}. See \code{\link{gafs}} and/or \code{\link{safs}} for explanations of the difference.} \item{maximize}{a two-element logical: should the metrics be maximized or minimized? Like the \code{metric} argument, this this vector should have names \code{"internal"} and \code{"external"}.} \item{number}{Either the number of folds or number of resampling iterations} \item{repeats}{For repeated k-fold cross-validation only: the number of complete sets of folds to compute} \item{verbose}{a logical for printing results} \item{returnResamp}{A character string indicating how much of the resampled summary metrics should be saved. Values can be ``all'' or ``none''} \item{p}{For leave-group out cross-validation: the training percentage} \item{index}{a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.} \item{indexOut}{a list (the same length as \code{index}) that dictates which sample are held-out for each resample. If \code{NULL}, then the unique set of samples not contained in \code{index} is used.} \item{seeds}{a vector or integers that can be used to set the seed during each search. The number of seeds must be equal to the number of resamples plus one. } \item{holdout}{the proportion of data in [0, 1) to be held-back from \code{x} and \code{y} to calculate the internal fitness values} \item{improve}{the number of iterations without improvement before \code{\link{safs}} reverts back to the previous optimal subset} \item{genParallel}{if a parallel backend is loaded and available, should \code{\link{gafs}} use it tp parallelize the fitness calculations within a generation within a resample?} \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?} } \details{ Many of these options are the same as those described for \code{\link[caret]{trainControl}}. More extensive documentation and examples can be found on the \pkg{caret} website at \url{http://topepo.github.io/caret/GA.html#syntax} and \url{http://topepo.github.io/caret/SA.html#syntax}. The \code{functions} component contains the information about how the model should be fit and summarized. It also contains the elements needed for the GA and SA modules (e.g. cross-over, etc). The elements of \code{functions} that are the same for GAs and SAs are: \itemize{ \item \code{fit}, with arguments \code{x}, \code{y}, \code{lev}, \code{last}, and \code{...}, is used to fit the classification or regression model \item \code{pred}, with arguments \code{object} and \code{x}, predicts new samples \item \code{fitness_intern}, with arguments \code{object}, \code{x}, \code{y}, \code{maximize}, and \code{p}, summarizes performance for the internal estimates of fitness \item \code{fitness_extern}, with arguments \code{data}, \code{lev}, and \code{model}, summarizes performance using the externally held-out samples \item \code{selectIter}, with arguments \code{x}, \code{metric}, and \code{maximize}, determines the best search iteration for feature selection. } The elements of \code{functions} specific to genetic algorithms are: \itemize{ \item \code{initial}, with arguments \code{vars}, \code{popSize} and \code{...}, creates an initial population. \item \code{selection}, with arguments \code{population}, \code{fitness}, \code{r}, \code{q}, and \code{...}, conducts selection of individuals. \item \code{crossover}, with arguments \code{population}, \code{fitness}, \code{parents} and \code{...}, control genetic reproduction. \item \code{mutation}, with arguments \code{population}, \code{parent} and \code{...}, adds mutations. } The elements of \code{functions} specific to simulated annealing are: \itemize{ \item \code{initial}, with arguments \code{vars}, \code{prob}, and \code{...}, creates the initial subset. \item \code{perturb}, with arguments \code{x}, \code{vars}, and \code{number}, makes incremental changes to the subsets. \item \code{prob}, with arguments \code{old}, \code{new}, and \code{iteration}, computes the acceptance probabilities } The pages \url{http://topepo.github.io/caret/GA.html#custom} and \url{http://topepo.github.io/caret/SA.html#custom} have more details about each of these functions. \code{holdout} can be used to hold out samples for computing the internal fitness value. Note that this is independent of the external resampling step. Suppose 10-fold CV is being used. Within a resampling iteration, \code{holdout} can be used to sample an additional proportion of the 90\% resampled data to use for estimating fitness. This may not be a good idea unless you have a very large training set and want to avoid an internal resampling procedure to estimate fitness. The search algorithms can be parallelized in several places: \enumerate{ \item each externally resampled GA or SA can be run independently (controlled by the \code{allowParallel} options) \item within a GA, the fitness calculations at a particular generation can be run in parallel over the current set of individuals (see the \code{genParallel}) \item if inner resampling is used, these can be run in parallel (controls depend on the function used. See, for example, \code{\link[caret]{trainControl}}) \item any parallelization of the individual model fits. This is also specific to the modeling function. } It is probably best to pick one of these areas for parallelization and the first is likely to produces the largest decrease in run-time since it is the least likely to incur multiple re-starting of the worker processes. Keep in mind that if multiple levels of parallelization occur, this can effect the number of workers and the amount of memory required exponentially. } \value{An echo of the parameters specified} \author{Max Kuhn} \references{\url{http://topepo.github.io/caret/GA.html}, \url{http://topepo.github.io/caret/SA.html}} \seealso{\code{\link{safs}}, \code{\link{safs}}, , \code{\link{caretGA}}, \code{\link{rfGA}}, \code{\link{treebagGA}}, \code{\link{caretSA}}, \code{\link{rfSA}}, \code{\link{treebagSA}}} \keyword{utilities} caret/man/resamples.Rd0000644000175000017500000001030612453550065015066 0ustar rbalintrbalint\name{resamples} \alias{resamples.default} \alias{resamples} \alias{summary.resamples} \alias{sort.resamples} \alias{modelCor} \title{ Collation and Visualization of Resampling Results } \description{ These functions provide methods for collection, analyzing and visualizing a set of resampling results from a common data set. } \usage{ resamples(x, ...) \method{resamples}{default}(x, modelNames = names(x), ...) \method{summary}{resamples}(object, metric = object$metrics, ...) \method{sort}{resamples}(x, decreasing = FALSE, metric = x$metric[1], FUN = mean, ...) modelCor(x, metric = x$metric[1], ...) } \arguments{ \item{x}{a list of two or more objects of class \code{\link{train}}, \code{\link{sbf}} or \code{\link{rfe}} with a common set of resampling indices in the \code{control} object. For \code{sort.resamples}, it is an object generated by \code{resamples}.} \item{modelNames}{an optional set of names to give to the resampling results} \item{object}{an object generated by \code{resamples}} \item{metric}{a character string for the performance measure used to sort or computing the between-model correlations} \item{decreasing}{logical. Should the sort be increasing or decreasing? } \item{FUN}{a function whose first argument is a vector and returns a scalar, to be applied to each model's performance measure.} \item{\dots}{only used for \code{sort} and \code{modelCor} and captures arguments to pass to \code{sort} or \code{FUN}.} } \details{ The ideas and methods here are based on Hothorn et al. (2005) and Eugster et al. (2008). The results from \code{\link{train}} can have more than one performance metric per resample. Each metric in the input object is saved. \code{resamples} checks that the resampling results match; that is, the indices in the object \code{trainObject$control$index} are the same. Also, the argument \code{\link{trainControl}} \code{returnResamp} should have a value of \code{"final"} for each model. The summary function computes summary statistics across each model/metric combination. } \value{ For \code{resamples}: an object with class \code{"resamples"} with elements \item{call }{the call} \item{values }{a data frame of results where rows correspond to resampled data sets and columns indicate the model and metric} \item{models }{a character string of model labels} \item{metrics }{a character string of performance metrics} \item{methods }{a character string of the \code{\link{train}} \code{method} argument values for each model } For \code{sort.resamples} a character string in the sorted order is generated. \code{modelCor} returns a correlation matrix. } \author{ Max Kuhn } \seealso{ \code{\link{train}}, \code{\link{trainControl}}, \code{\link{diff.resamples}}, \code{\link{xyplot.resamples}}, \code{\link{densityplot.resamples}}, \code{\link{bwplot.resamples}}, \code{\link{splom.resamples}}} \references{Hothorn et al. The design and analysis of benchmark experiments. Journal of Computational and Graphical Statistics (2005) vol. 14 (3) pp. 675-699 Eugster et al. Exploratory and inferential analysis of benchmark experiments. Ludwigs-Maximilians-Universitat Munchen, Department of Statistics, Tech. Rep (2008) vol. 30} \examples{ data(BloodBrain) set.seed(1) ## tmp <- createDataPartition(logBBB, ## p = .8, ## times = 100) ## rpartFit <- train(bbbDescr, logBBB, ## "rpart", ## tuneLength = 16, ## trControl = trainControl( ## method = "LGOCV", index = tmp)) ## ctreeFit <- train(bbbDescr, logBBB, ## "ctree", ## trControl = trainControl( ## method = "LGOCV", index = tmp)) ## earthFit <- train(bbbDescr, logBBB, ## "earth", ## tuneLength = 20, ## trControl = trainControl( ## method = "LGOCV", index = tmp)) ## or load pre-calculated results using: ## load(url("http://caret.r-forge.r-project.org/exampleModels.RData")) ## resamps <- resamples(list(CART = rpartFit, ## CondInfTree = ctreeFit, ## MARS = earthFit)) ## resamps ## summary(resamps) } \keyword{ models } caret/man/print.train.Rd0000644000175000017500000000243712453550065015351 0ustar rbalintrbalint\name{print.train} \alias{print.train} \title{Print Method for the train Class} \description{ Print the results of a \code{\link{train}} object. } \usage{ \method{print}{train}(x, printCall = FALSE, details = FALSE, selectCol = FALSE, ...) } \arguments{ \item{x}{an object of class \code{\link{train}}.} \item{printCall}{a logical to print the call at the top of the output} \item{details}{a logical to show print or summary methods for the final model. In some cases (such as \code{gbm}, \code{knn}, \code{lvq}, naive Bayes and bagged tree models), no information will be printed even if \code{details = TRUE}} \item{selectCol}{a logical whether to add a column with a star next to the selected parameters} \item{\ldots}{options passed to \code{\link[base]{format}}} } \details{ The table of complexity parameters used, their resampled performance and a flag for which rows are optimal. } \value{ A matrix with the complexity parameters and performance (invisibly). } \author{Max Kuhn} \seealso{\code{\link{train}}} \examples{ \dontrun{ data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] library(klaR) rdaFit <- train(TrainData, TrainClasses, method = "rda", control = trainControl(method = "cv")) print(rdaFit) } } \keyword{print} caret/man/update.safs.Rd0000644000175000017500000000331512453550065015312 0ustar rbalintrbalint\name{update.safs} \alias{update.safs} \alias{update.gafs} \title{ Update or Re-fit a SA or GA Model } \description{ \code{update} allows a user to over-ride the search iteration selection process. } \usage{ \method{update}{gafs}(object, iter, x, y, ...) \method{update}{safs}(object, iter, x, y, ...) } \arguments{ \item{object}{An object produced by \code{\link{gafs}} or \code{\link{safs}}} \item{iter}{a single numeric integer} \item{x, y}{the original training data used in the call to \code{\link{gafs}} or \code{\link{safs}}} \item{\dots}{not currently used} } \details{ Based on the results of plotting a \code{\link{gafs}} or \code{\link{safs}} object, these functions can be used to supersede the number of iterations determined analytically from the resamples. Any values of \code{...} originally passed to \code{\link{gafs}} or \code{\link{safs}} are automatically passed on to the updated model (i.e. they do not need to be supplied again to \code{update}. } \value{ an object of class \code{\link{gafs}} or \code{\link{safs}} } \author{ Max Kuhn } \seealso{ \code{\link{gafs}}, \code{\link{safs}} } \examples{ \dontrun{ set.seed(1) train_data <- twoClassSim(100, noiseVars = 10) test_data <- twoClassSim(10, noiseVars = 10) ## A short example ctrl <- safsControl(functions = rfSA, method = "cv", number = 3) rf_search <- safs(x = train_data[, -ncol(train_data)], y = train_data$Class, iters = 3, safsControl = ctrl) rf_search2 <- update(rf_search, iter = 1, x = train_data[, -ncol(train_data)], y = train_data$Class) rf_search2 } } \keyword{ models } caret/man/predict.gafs.Rd0000644000175000017500000000230112453550065015440 0ustar rbalintrbalint\name{predict.gafs} \alias{predict.gafs} \alias{predict.safs} \title{Predict new samples} \description{ Predict new samples using \code{\link{safs}} and \code{\link{gafs}} objects. } \usage{ \method{predict}{gafs}(object, newdata, ...) \method{predict}{safs}(object, newdata, ...) } \arguments{ \item{object}{an object of class \code{\link{safs}} or \code{\link{gafs}}} \item{newdata}{a data frame or matrix of predictors. } \item{\dots}{not currently used} } \details{ Only the predictors listed in \code{object$optVariables} are required. } \value{ The type of result depends on what was specified in \code{object$control$functions$predict}. } \author{Max Kuhn} \seealso{\code{\link{safs}}, \code{\link{gafs}}} \examples{ \dontrun{ set.seed(1) train_data <- twoClassSim(100, noiseVars = 10) test_data <- twoClassSim(10, noiseVars = 10) ## A short example ctrl <- safsControl(functions = rfSA, method = "cv", number = 3) rf_search <- safs(x = train_data[, -ncol(train_data)], y = train_data$Class, iters = 3, safsControl = ctrl) rf_search predict(rf_search, train_data) } } \keyword{multivariate} caret/man/filterVarImp.Rd0000644000175000017500000000453612453550065015507 0ustar rbalintrbalint\name{filterVarImp} \alias{filterVarImp} \title{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{ \item{x}{A matrix or data frame of predictor data} \item{y}{A vector (numeric or factor) of outcomes)} \item{nonpara}{should nonparametric methods be used to assess the relationship between the features and response} \item{...}{options to pass to either \code{\link[stats]{lm}} or \code{\link[stats]{loess}}} } \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. } \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, \code{nonpara}, is used to pick the model fitting technique. When \code{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. } \author{Max Kuhn} \examples{ data(mdrr) filterVarImp(mdrrDescr[, 1:5], mdrrClass) data(BloodBrain) filterVarImp(bbbDescr[, 1:5], logBBB, nonpara = FALSE) apply(bbbDescr[, 1:5], 2, function(x, y) summary(lm(y~x))$coefficients[2,3], y = logBBB) filterVarImp(bbbDescr[, 1:5], logBBB, nonpara = TRUE) } \keyword{ models } caret/man/predict.knn3.Rd0000644000175000017500000000170112453550065015374 0ustar rbalintrbalint\name{predict.knn3} \alias{predict.knn3} \title{ Predictions from k-Nearest Neighbors } \description{ Predict the class of a new observation based on k-NN. } \usage{ \method{predict}{knn3}(object, newdata, type=c("prob", "class"), ...) } \arguments{ \item{object}{object of class \code{knn3}.} \item{newdata}{a data frame of new observations. } \item{type}{return either the predicted class or the proportion of the votes for the winning class.} \item{...}{additional arguments.} } \details{ This function is a method for the generic function \code{\link{predict}} for class \code{knn3}. For the details see \code{\link{knn3}}. This is essentially a copy of \code{\link[ipred]{predict.ipredknn}}. } \value{ Either the predicted class or the proportion of the votes for each class. } \author{\code{\link[ipred]{predict.ipredknn}} by Torsten.Hothorn } \keyword{multivariate} caret/man/gafs.default.Rd0000644000175000017500000001451512453550065015444 0ustar rbalintrbalint\name{gafs.default} \alias{gafs.default} \alias{gafs} \title{Genetic algorithm feature selection} \description{ Supervised feature selection using genetic algorithms } \usage{ gafs(x, ...) \method{gafs}{default}(x, y, iters = 10, popSize = 50, pcrossover = 0.8, pmutation = 0.1, elite = 0, suggestions = NULL, differences = TRUE, gafsControl = gafsControl(), ...) } \arguments{ \item{x}{an object where samples are in rows and features are in columns. This could be a simple matrix, data frame or other type (e.g. sparse matrix). See Details below} \item{y}{a numeric or factor vector containing the outcome for each sample} \item{iters}{number of search iterations} \item{popSize}{number of subsets evaluated at each iteration} \item{pcrossover}{the crossover probability} \item{pmutation}{the mutation probability} \item{elite}{the number of best subsets to survive at each generation} \item{suggestions}{a binary matrix of subsets strings to be included in the initial population. If provided the number of columns must match the number of columns in \code{x}} \item{differences}{a logical: should the difference in fitness values with and without each predictor be calculated?} \item{gafsControl}{a list of values that define how this function acts. See \code{\link{gafsControl}} and URL.} \item{\ldots}{arguments passed to the classification or regression routine specified in the function \code{gafsControl$functions$fit}} } \details{ \code{\link{gafs}} conducts a supervised binary search of the predictor space using a genetic algorithm. See XXX and Scrucca (2012) for more details on genetic algorithms. This function conducts the search of the feature space repeatedly within resampling iterations. First, the training data are split be whatever resampling method was specified in the control function. For example, if 10-fold cross-validation is selected, the entire genetic algorithm is conducted 10 separate times. For the first fold, nine tenths of the data are used in the search while the remaining tenth is used to estimate the external performance since these data points were not used in the search. During the genetic algorithm, a measure of fitness is needed to guide the search. This is the internal measure of performance. During the search, the data that are available are the instances selected by the top-level resampling (e.g. the nine tenths mentioned above). A common approach is to conduct another resampling procedure. Another option is to use a holdout set of samples to determine the internal estimate of performance (see the holdout argument of the control function). While this is faster, it is more likely to cause overfitting of the features and should only be used when a large amount of training data are available. Yet another idea is to use a penalized metric (such as the AIC statistic) but this may not exist for some metrics (e.g. the area under the ROC curve). The internal estimates of performance will eventually overfit the subsets to the data. However, since the external estimate is not used by the search, it is able to make better assessments of overfitting. After resampling, this function determines the optimal number of generations for the GA. Finally, the entire data set is used in the last execution of the genetic algorithm search and the final model is built on the predictor subset that is associated with the optimal number of generations determined by resampling (although the update function can be used to manually set the number of generations). This is an example of the output produced when \code{gafsControl(verbose = TRUE)} is used: \preformatted{Fold2 1 0.715 (13) Fold2 2 0.715->0.737 (13->17, 30.4\%) * Fold2 3 0.737->0.732 (17->14, 24.0\%) Fold2 4 0.737->0.769 (17->23, 25.0\%) *} For the second resample (e.g. fold 2), the best subset across all individuals tested in the first generation contained 13 predictors and was associated with a fitness value of 0.715. The second generation produced a better subset containing 17 samples with an associated fitness values of 0.737 (and improvement is symbolized by the \code{*}. The percentage listed is the Jaccard similarity between the previous best individual (with 13 predictors) and the new best. The third generation did not produce a better fitness value but the fourth generation did. The search algorithm can be parallelized in several places: \enumerate{ \item each externally resampled GA can be run independently (controlled by the \code{allowParallel} option of \code{\link{gafsControl}}) \item within a GA, the fitness calculations at a particular generation can be run in parallel over the current set of individuals (see the \code{genParallel} option in \code{\link{gafsControl}}) \item if inner resampling is used, these can be run in parallel (controls depend on the function used. See, for example, \code{\link[caret]{trainControl}}) \item any parallelization of the individual model fits. This is also specific to the modeling function. } It is probably best to pick one of these areas for parallelization and the first is likely to produces the largest decrease in run-time since it is the least likely to incur multiple re-starting of the worker processes. Keep in mind that if multiple levels of parallelization occur, this can effect the number of workers and the amount of memory required exponentially. } \value{ an object of class \code{gafs} } \references{ Kuhn M and Johnson K (2013), Applied Predictive Modeling, Springer, Chapter 19 \url{http://appliedpredictivemodeling.com} Scrucca L (2012). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. \url{www.jstatsoft.org/v53/i04} \url{http://en.wikipedia.org/wiki/Jaccard_index} } \author{Max Kuhn, Luca Scrucca (for GA internals)} \seealso{ \code{\link{gafsControl}}, \code{\link{predict.gafs}}, \code{\link{caretGA}}, \code{\link{rfGA}} \code{\link{treebagGA}} } \examples{ \dontrun{ set.seed(1) train_data <- twoClassSim(100, noiseVars = 10) test_data <- twoClassSim(10, noiseVars = 10) ## A short example ctrl <- gafsControl(functions = rfGA, method = "cv", number = 3) rf_search <- gafs(x = train_data[, -ncol(train_data)], y = train_data$Class, iters = 3, gafsControl = ctrl) rf_search } } \keyword{models} caret/man/GermanCredit.Rd0000644000175000017500000000163312453550065015442 0ustar rbalintrbalint\name{GermanCredit} \docType{data} \alias{GermanCredit} \title{German Credit Data} \description{ Data from Dr. Hans Hofmann of the University of Hamburg. These data have two classes for the credit worthiness: good or bad. There are predictors related to attributes, such as: checking account status, duration, credit history, purpose of the loan, amount of the loan, savings accounts or bonds, employment duration, Installment rate in percentage of disposable income, personal information, other debtors/guarantors, residence duration, property, age, other installment plans, housing, number of existing credits, job information, Number of people being liable to provide maintenance for, telephone, and foreign worker status. Many of these predictors are discrete and have been expanded into several 0/1 indicator variables } \usage{data(GermanCredit)} \source{UCI Machine Learning Repository} \keyword{datasets} caret/man/rf_seq.Rd0000644000175000017500000000155512516214271014354 0ustar rbalintrbalint\name{var_seq} \alias{var_seq} \title{ Sequences of Variables for Tuning } \description{ This function generates a sequence of \code{mtry} values for random forests. } \usage{ var_seq(p, classification = FALSE, len = 3) } \arguments{ \item{p}{ The number of predictors } \item{classification}{ Is the outcome a factor (\code{classification = TRUE} or numeric?) } \item{len}{ The number of \code{mtry} values to generate. } } \details{ If the number of predictors is less than 500, a simple sequence of values of length \code{len} is generated between 2 and \code{p}. For larger numbers of predictors, the sequence is created using \code{log2} steps. If \code{len = 1}, the defaults from the \code{randomForest} package are used. } \value{ a numeric vector } \author{ Max Kuhn } \examples{ var_seq(p = 100, len = 10) var_seq(p = 600, len = 10) } \keyword{models} caret/man/cox2.Rd0000644000175000017500000000224212453550065013746 0ustar rbalintrbalint\name{cox2} \docType{data} \alias{cox2} \alias{cox2Class} \alias{cox2Descr} \alias{cox2IC50} \title{COX-2 Activity Data} \description{ From Sutherland, O'Brien, and Weaver (2003): "A set of 467 cyclooxygenase-2 (COX-2) inhibitors has been assembled from the published work of a single research group, with in vitro activities against human recombinant enzyme expressed as IC50 values ranging from 1 nM to >100 uM (53 compounds have indeterminate IC50 values)." The data are in the Supplemental Data file for the article. A set of 255 descriptors (MOE2D and QikProp) were generated. To classify the data, we used a cutoff of $2^2.5$ to determine activity } \usage{data(cox2)} \value{ \item{cox2Descr}{the descriptors} \item{cox2IC50}{the IC50 data used to determine activity} \item{cox2Class}{the categorical outcome ("Active" or "Inactive") based on the $2^2.5$ cutoff} } \source{Sutherland, J. J., O'Brien, L. A. and Weaver, D. F. (2003). Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure-Activity Relationships, \emph{Journal of Chemical Information and Computer Sciences}, Vol. 43, pg. 1906--1915. } \keyword{datasets} caret/man/index2vec.Rd0000644000175000017500000000104612453550065014763 0ustar rbalintrbalint\name{index2vec} \alias{index2vec} \title{ Convert indicies to a binary vector } \description{ The function performs the opposite of \code{which} converting a set of integers to a binary vector } \usage{ index2vec(x, vars, sign = FALSE) } \arguments{ \item{x}{a vector of integers} \item{vars}{the number of possible locations} \item{sign}{a lgical; when true the data are encoded as -1/+1, and 0/1 otherwise} } \value{a numeric vector} \author{Max Kuhn} \examples{ index2vec(x = 1:2, vars = 5) index2vec(x = 1:2, vars = 5, sign = TRUE) } caret/man/sbf.Rd0000644000175000017500000001220612453550065013646 0ustar rbalintrbalint\name{sbf} \alias{sbf} \alias{sbf.default} \alias{sbf.formula} \alias{predict.sbf} \title{ Selection By Filtering (SBF) } \description{ Model fitting after applying univariate filters} \usage{ sbf(x, ...) \method{sbf}{default}(x, y, sbfControl = sbfControl(), ...) \method{sbf}{formula}(form, data, ..., subset, na.action, contrasts = NULL) \method{predict}{sbf}(object, newdata = NULL, ...) } \arguments{ \item{x}{a data frame containing training data where samples are in rows and features are in columns.} \item{y}{a numeric or factor vector containing the outcome for each sample.} \item{form}{A formula of the form \code{y ~ x1 + x2 + ...}} \item{data}{Data frame from which variables specified in \code{formula} are preferentially to be taken.} \item{subset}{An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)} \item{na.action}{A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)} \item{contrasts}{a list of contrasts to be used for some or all the factors appearing as variables in the model formula.} \item{sbfControl}{a list of values that define how this function acts. See \code{\link{sbfControl}}. (NOTE: If given, this argument must be named.)} \item{object}{an object of class \code{sbf}} \item{newdata}{a matrix or data frame of predictors. The object must have non-null column names} \item{\ldots}{for \code{sbf}: arguments passed to the classification or regression routine (such as \code{\link[randomForest]{randomForest}}). For \code{predict.sbf}: augments cannot be passed to the prediction function using \code{predict.sbf} as it uses the function originally specified for prediction.} } \details{ More details on this function can be found at \url{http://topepo.github.io/caret/featureselection.html#filter}. This function can be used to get resampling estimates for models when simple, filter-based feature selection is applied to the training data. For each iteration of resampling, the predictor variables are univariately filtered prior to modeling. Performance of this approach is estimated using resampling. The same filter and model are then applied to the entire training set and the final model (and final features) are saved. \code{sbf} can be used with "explicit parallelism", where different resamples (e.g. cross-validation group) can be split up and run on multiple machines or processors. By default, \code{sbf} will use a single processor on the host machine. As of version 4.99 of this package, the framework used for parallel processing uses the \pkg{foreach} package. To run the resamples in parallel, the code for \code{sbf} does not change; prior to the call to \code{sbf}, a parallel backend is registered with \pkg{foreach} (see the examples below). The modeling and filtering techniques are specified in \code{\link{sbfControl}}. Example functions are given in \code{\link{lmSBF}}. } \value{ for \code{sbf}, an object of class \code{sbf} with elements: \item{pred}{if \code{sbfControl$saveDetails} is \code{TRUE}, this is a list of predictions for the hold-out samples at each resampling iteration. Otherwise it is \code{NULL}} \item{variables}{a list of variable names that survived the filter at each resampling iteration} \item{results}{a data frame of results aggregated over the resamples} \item{fit}{the final model fit with only the filtered variables} \item{optVariables}{the names of the variables that survived the filter using the training set} \item{ call}{the function call} \item{control}{the control object} \item{resample}{if \code{sbfControl$returnResamp} is "all", a data frame of the resampled performance measures. Otherwise, \code{NULL}} \item{metrics}{a character vector of names of the performance measures} \item{dots}{a list of optional arguments that were passed in} For \code{predict.sbf}, a vector of predictions. } \author{ Max Kuhn } \seealso{ \code{\link{sbfControl}} } \examples{ \dontrun{ data(BloodBrain) ## Use a GAM is the filter, then fit a random forest model RFwithGAM <- sbf(bbbDescr, logBBB, sbfControl = sbfControl(functions = rfSBF, verbose = FALSE, method = "cv")) RFwithGAM predict(RFwithGAM, bbbDescr[1:10,]) ## classification example with parallel processing ## library(doMC) ## Note: if the underlying model also uses foreach, the ## number of cores specified above will double (along with ## the memory requirements) ## registerDoMC(cores = 2) data(mdrr) mdrrDescr <- mdrrDescr[,-nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .8)] set.seed(1) filteredNB <- sbf(mdrrDescr, mdrrClass, sbfControl = sbfControl(functions = nbSBF, verbose = FALSE, method = "repeatedcv", repeats = 5)) confusionMatrix(filteredNB) } } \keyword{models} caret/man/pcaNNet.Rd0000644000175000017500000000675412453550065014437 0ustar rbalintrbalint\name{pcaNNet.default} \alias{pcaNNet.default} \alias{predict.pcaNNet} \alias{pcaNNet.formula} \alias{pcaNNet} \title{Neural Networks with a Principal Component Step } \description{ Run PCA on a dataset, then use it in a neural network model } \usage{ \method{pcaNNet}{default}(x, y, thresh = 0.99, ...) \method{pcaNNet}{formula}(formula, data, weights, ..., thresh = .99, subset, na.action, contrasts = NULL) \method{predict}{pcaNNet}(object, newdata, type = c("raw", "class"), ...) } \arguments{ \item{formula}{ A formula of the form \code{class ~ x1 + x2 + \dots} } \item{x}{ matrix or data frame of \code{x} values for examples. } \item{y}{ matrix or data frame of target values for examples. } \item{weights}{ (case) weights for each example -- if missing defaults to 1. } \item{thresh}{a threshold for the cumulative proportion of variance to capture from the PCA analysis. For example, to retain enough PCA components to capture 95 percent of variation, set \code{thresh = .95}} \item{data}{ Data frame from which variables specified in \code{formula} are preferentially to be taken. } \item{subset}{ An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) } \item{na.action}{ A function to specify the action to be taken if \code{NA}s are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) } \item{contrasts}{ a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. } \item{object}{ an object of class \code{pcaNNet} as returned by \code{pcaNNet}. } \item{newdata}{ matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case. } \item{type}{ Type of output } \item{\dots}{ arguments passed to \code{\link[nnet]{nnet}} } } \details{ The function first will run principal component analysis on the data. The cumulative percentage of variance is computed for each principal component. The function uses the \code{thresh} argument to determine how many components must be retained to capture this amount of variance in the predictors. The principal components are then used in a neural network model. When predicting samples, the new data are similarly transformed using the information from the PCA analysis on the training data and then predicted. Because the variance of each predictor is used in the PCA analysis, the code does a quick check to make sure that each predictor has at least two distinct values. If a predictor has one unique value, it is removed prior to the analysis. } \value{ For \code{pcaNNet}, an object of \code{"pcaNNet"} or \code{"pcaNNet.formula"}. Items of interest in the output are: \item{pc }{the output from \code{\link{preProcess}}} \item{model }{the model generated from \code{\link[nnet]{nnet}}} \item{names }{if any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of \code{NULL}} } \references{ Ripley, B. D. (1996) \emph{Pattern Recognition and Neural Networks.} Cambridge. } \author{These are heavily based on the \code{nnet} code from Brian Ripley.} \seealso{ \code{\link[nnet]{nnet}}, \code{\link{preProcess}}} \examples{ data(BloodBrain) modelFit <- pcaNNet(bbbDescr[, 1:10], logBBB, size = 5, linout = TRUE, trace = FALSE) modelFit predict(modelFit, bbbDescr[, 1:10]) } \keyword{ neural } caret/man/twoClassSim.Rd0000644000175000017500000001512112453550065015343 0ustar rbalintrbalint\name{twoClassSim} \alias{twoClassSim} \alias{SLC14_1} \alias{SLC14_2} \alias{LPH07_1} \alias{LPH07_2} \title{ Simulation Functions } \description{ This function simulates regression and classification data with truly important predictors and irrelevant predictions. } \usage{ twoClassSim(n = 100, intercept = -5, linearVars = 10, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0, mislabel = 0) SLC14_1(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) SLC14_2(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) LPH07_1(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) LPH07_2(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) } \arguments{ \item{n}{ The number of simulated data points } \item{intercept}{ The intercept, which controls the class balance. The default value produces a roughly balanced data set when the other defaults are used. } \item{linearVars}{ The number of linearly important effects. See Details below. } \item{noiseVars}{ The number of uncorrelated irrelevant predictors to be included. } \item{corrVars}{ The number of correlated irrelevant predictors to be included. } \item{corrType}{ The correlation structure of the correlated irrelevant predictors. Values of "AR1" and "exch" are available (see Details below) } \item{corrValue}{ The correlation value. } \item{mislabel}{ The proportion of data that is possibly mislabeled. See Details below. } } \details{ The first function (\code{twoClassSim}) generates two class data. The data are simulated in different sets. First, two multivariate normal predictors (denoted here as \code{A} and \code{B}) are created with a correlation our about 0.65. They change the log-odds using main effects and an interaction: \preformatted{ intercept - 4A + 4B + 2AB } The intercept is a parameter for the simulation and can be used to control the amount of class imbalance. The second set of effects are linear with coefficients that alternate signs and have values between 2.5 and 0.025. For example, if there were six predictors in this set, their contribution to the log-odds would be \preformatted{ -2.50C + 2.05D -1.60E + 1.15F -0.70G + 0.25H } The third set is a nonlinear function of a single predictor ranging between [0, 1] called \code{J} here: \preformatted{ (J^3) + 2exp(-6(J-0.3)^2) } The fourth set of informative predictors are copied from one of Friedman's systems and use two more predictors (\code{K} and \code{L}): \preformatted{ 2sin(KL) } All of these effects are added up to model the log-odds. This is used to calculate the probability of a sample being in the first class and a random uniform number is used to actually make the assignment of the actual class. To mislabel the data, the probability is reversed (i.e. \code{p = 1 - p}) before the random number generation. The remaining functions simulate regression data sets. \code{LPH07_1} and \code{LPH07_2} are from van der Laan et al. (2007). The first function uses random Bernoulli variables that have a 40\% probability of being a value of 1. The true regression equation is: \preformatted{ 2*w_1*w_10 + 4*w_2*w_7 + 3*w_4*w_5 - 5*w_6*w_10 + 3*w_8*w_9 + w_1*w_2*w_4 - 2*w_7*(1-w_6)*w_2*w_9 - 4*(1 - w_10)*w_1*(1-w_4) } The simulated error term is a standard normal (i.e. Gaussian). The noise variables are simulated in the same manner as described above but are made binary based on whether the normal random variable is above or below 0. The second function (\code{LPH07_2}) uses 20 independent Gaussians with mean zero and variance 16. The functional form here is: \preformatted{ x_1*x_2 + x_10^2 - x_3*x_17 - x_15*x_4 + x_9*x_5 + x_19 - x_20^2 + x_9*x_8 } The error term is also Gaussian with mean zero and variance 16. The function \code{SLC14_1} simulates a system from Sapp et al. (2014). All informative predictors are independent Gaussian random variables with mean zero and a variance of 9. The prediction equation is: \preformatted{ x_1 + sin(x_2) + log(abs(x_3)) + x_4^2 + x_5*x_6 + I(x_7*x_8*x_9 < 0) + I(x_10 > 0) + x_11*I(x_11 > 0) + sqrt(abs(x_12)) + cos(x_13) + 2*x_14 + abs(x_15) + I(x_16 < -1) + x_17*I(x_17 < -1) - 2 * x_18 - x_19*x_20 } The random error here is also Gaussian with mean zero and a variance of 9. \code{SLC14_2} is also from Sapp et al. (2014). Two hundred independent Gaussian variables are generated, each having mean zero and variance 16. The functional form is \preformatted{ -1 + log(abs(x_1)) + ... + log(abs(x_200)) } and the error term is Gaussian with mean zero and a variance of 25. For each simulation, the user can also add non-informative predictors to the data. These are random standard normal predictors and can be optionally added to the data in two ways: a specified number of independent predictors or a set number of predictors that follow a particular correlation structure. The only two correlation structure that have been implemented are \itemize{ \item compound-symmetry (aka exchangeable) where there is a constant correlation between all the predictors \item auto-regressive 1 [AR(1)]. While there is no time component to these data, this structure can be used to add predictors of varying levels of correlation. For example, if there were 4 predictors and \code{r} was the correlation parameter, the between predictor correlation matrix would be } \preformatted{ | 1 sym | | r 1 | | r^2 r 1 | | r^3 r^2 r 1 | | r^4 r^3 r^2 r 1 | } } \value{ a data frame with columns: \item{Class }{A factor with levels "Class1" and "Class2"} \item{TwoFactor1, TwoFactor2 }{Correlated multivariate normal predictors (denoted as \code{A} and \code{B} above)} \item{Nonlinear1, Nonlinear2, Nonlinear3}{Uncorrelated random uniform predictors (\code{J}, \code{K} and \code{L} above).} \item{Linear1, \ldots}{Optional uncorrelated standard normal predictors (\code{C} through \code{H} above)} \item{Noise1, \ldots}{Optional uncorrelated standard normal predictions} \item{Corr1, \ldots}{Optional correlated multivariate normal predictors (each with unit variances)}. } \author{ Max Kuhn } \examples{ example <- twoClassSim(100, linearVars = 1) splom(~example[, 1:6], groups = example$Class) } \references{ van der Laan, M. J., & Polley Eric, C. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1), 1-23. Sapp, S., van der Laan, M. J., & Canny, J. (2014). Subsemble: an ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6), 1247-1259. } \keyword{models} caret/man/featurePlot.Rd0000644000175000017500000000250712453550065015371 0ustar rbalintrbalint\name{featurePlot} \alias{featurePlot} \title{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{ \item{x}{a matrix or data frame of continuous feature/probe/spectra data.} \item{y}{a factor indicating class membership.} \item{plot}{the type of plot. For classification: \code{box}, \code{strip}, \code{density}, \code{pairs} or \code{ellipse}. For regression, \code{pairs} or \code{scatter}} \item{labels}{a bad attempt at pre-defined axis labels} \item{\dots}{options passed to lattice calls.} } \details{ 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{Max Kuhn} \examples{ x <- matrix(rnorm(50*5),ncol=5) y <- factor(rep(c("A", "B"), 25)) trellis.par.set(theme = col.whitebg(), warn = FALSE) featurePlot(x, y, "ellipse") featurePlot(x, y, "strip", jitter = TRUE) featurePlot(x, y, "box") featurePlot(x, y, "pairs") } \keyword{hplot} caret/man/plotObsVsPred.Rd0000644000175000017500000000532112453550065015642 0ustar rbalintrbalint\name{plotObsVsPred} \alias{plotObsVsPred} \title{Plot Observed versus Predicted Results in Regression and Classification Models} \description{ This function takes an object (preferably from the function \code{\link{extractPrediction}}) and creates a lattice plot. For numeric outcomes, the observed and predicted data are plotted with a 45 degree reference line and a smoothed fit. For factor outcomes, a dotplot plot is produced with the accuracies for the different models. If the call to \code{\link{extractPrediction}} included test data, these data are shown, but if unknowns were also included, they are not plotted } \usage{ plotObsVsPred(object, equalRanges = TRUE, ...) } \arguments{ \item{object}{an object (preferably from the function \code{\link{extractPrediction}}. There should be columns named \code{obs}, \code{pred}, \code{model} (e.g. "rpart", "nnet" etc.) and \code{dataType} (e.g. "Training", "Test" etc) } \item{equalRanges}{a logical; should the x- and y-axis ranges be the same?} \item{\dots}{parameters to pass to \code{\link[lattice]{xyplot}} or \code{\link[lattice:xyplot]{dotplot}}, such as \code{auto.key}} } \value{ A lattice object. Note that the plot has to be printed to be displayed (especially in a loop). } \author{Max Kuhn} \examples{ \dontrun{ # regression example data(BostonHousing) rpartFit <- train(BostonHousing[1:100, -c(4, 14)], BostonHousing$medv[1:100], "rpart", tuneLength = 9) plsFit <- train(BostonHousing[1:100, -c(4, 14)], BostonHousing$medv[1:100], "pls") predVals <- extractPrediction(list(rpartFit, plsFit), testX = BostonHousing[101:200, -c(4, 14)], testY = BostonHousing$medv[101:200], unkX = BostonHousing[201:300, -c(4, 14)]) plotObsVsPred(predVals) #classification example data(Satellite) numSamples <- dim(Satellite)[1] set.seed(716) varIndex <- 1:numSamples trainSamples <- sample(varIndex, 150) varIndex <- (1:numSamples)[-trainSamples] testSamples <- sample(varIndex, 100) varIndex <- (1:numSamples)[-c(testSamples, trainSamples)] unkSamples <- sample(varIndex, 50) trainX <- Satellite[trainSamples, -37] trainY <- Satellite[trainSamples, 37] testX <- Satellite[testSamples, -37] testY <- Satellite[testSamples, 37] unkX <- Satellite[unkSamples, -37] knnFit <- train(trainX, trainY, "knn") rpartFit <- train(trainX, trainY, "rpart") predTargets <- extractPrediction(list(knnFit, rpartFit), testX = testX, testY = testY, unkX = unkX) plotObsVsPred(predTargets) } } \keyword{hplot} caret/man/findLinearCombos.Rd0000644000175000017500000000333012453550065016310 0ustar rbalintrbalint\name{findLinearCombos} \alias{findLinearCombos} \title{Determine linear combinations in a matrix} \description{ Enumerate and resolve the linear combinations in a numeric matrix } \usage{ findLinearCombos(x) } \arguments{ \item{x}{a numeric matrix} } \details{ The QR decomposition is used to determine if the matrix is full rank and then identify the sets of columns that are involved in the dependencies. To "resolve" them, columns are iteratively removed and the matrix rank is rechecked. The \code{\link[subselect:trim.matrix]{trim.matrix}} function in the \pkg{subselect} package can also be used to accomplish the same goal. } \value{ a list with elements: \item{linearCombos }{If there are linear combinations, this will be a list with elements for each dependency that contains vectors of column numbers. } \item{remove }{a list of column numbers that can be removed to counter the linear combinations} } \author{Kirk Mettler and Jed Wing (\code{enumLC}) and Max Kuhn (\code{findLinearCombos}) } \seealso{\code{\link[subselect:trim.matrix]{trim.matrix}}} \examples{ testData1 <- matrix(0, nrow=20, ncol=8) testData1[,1] <- 1 testData1[,2] <- round(rnorm(20), 1) testData1[,3] <- round(rnorm(20), 1) testData1[,4] <- round(rnorm(20), 1) testData1[,5] <- 0.5 * testData1[,2] - 0.25 * testData1[,3] - 0.25 * testData1[,4] testData1[1:4,6] <- 1 testData1[5:10,7] <- 1 testData1[11:20,8] <- 1 findLinearCombos(testData1) testData2 <- matrix(0, nrow=6, ncol=6) testData2[,1] <- c(1, 1, 1, 1, 1, 1) testData2[,2] <- c(1, 1, 1, 0, 0, 0) testData2[,3] <- c(0, 0, 0, 1, 1, 1) testData2[,4] <- c(1, 0, 0, 1, 0, 0) testData2[,5] <- c(0, 1, 0, 0, 1, 0) testData2[,6] <- c(0, 0, 1, 0, 0, 1) findLinearCombos(testData2) } \keyword{manip} caret/man/rfeControl.Rd0000644000175000017500000001727412453550065015223 0ustar rbalintrbalint\name{rfeControl} \alias{rfeControl} \title{Controlling the Feature Selection Algorithms} \description{ This function generates a control object that can be used to specify the details of the feature selection algorithms used in this package. } \usage{ rfeControl(functions = NULL, rerank = FALSE, method = "boot", saveDetails = FALSE, number = ifelse(method \%in\% c("cv", "repeatedcv"), 10, 25), repeats = ifelse(method \%in\% c("cv", "repeatedcv"), 1, number), verbose = FALSE, returnResamp = "final", p = .75, index = NULL, indexOut = NULL, timingSamps = 0, seeds = NA, allowParallel = TRUE) } \arguments{ \item{functions}{a list of functions for model fitting, prediction and variable importance (see Details below)} \item{rerank}{a logical: should variable importance be re-calculated each time features are removed? } \item{method}{The external resampling method: \code{boot}, \code{cv}, \code{LOOCV} or \code{LGOCV} (for repeated training/test splits} \item{number}{Either the number of folds or number of resampling iterations} \item{repeats}{For repeated k-fold cross-validation only: the number of complete sets of folds to compute} \item{saveDetails}{a logical to save the predictions and variable importances from the selection process} \item{verbose}{a logical to print a log for each external resampling iteration} \item{returnResamp}{A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'', ``all'' or ``none''} \item{p}{For leave-group out cross-validation: the training percentage} \item{index}{a list with elements for each external resampling iteration. Each list element is the sample rows used for training at that iteration.} \item{indexOut}{a list (the same length as \code{index}) that dictates which sample are held-out for each resample. If \code{NULL}, then the unique set of samples not contained in \code{index} is used.} \item{timingSamps}{the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated).} \item{seeds}{an optional set of integers that will be used to set the seed at each resampling iteration. This is useful when the models are run in parallel. A value of \code{NA} will stop the seed from being set within the worker processes while a value of \code{NULL} will set the seeds using a random set of integers. Alternatively, a list can be used. The list should have \code{B+1} elements where \code{B} is the number of resamples. The first \code{B} elements of the list should be vectors of integers of length \code{P} where \code{P} is the number of subsets being evaluated (including the full set). The last element of the list only needs to be a single integer (for the final model). See the Examples section below. } \item{allowParallel}{if a parallel backend is loaded and available, should the function use it?} } \details{ More details on this function can be found at \url{http://topepo.github.io/caret/featureselection.html#rfe}. Backwards selection requires function to be specified for some operations. The \code{fit} function builds the model based on the current data set. The arguments for the function must be: \itemize{ \item{\code{x}}{ the current training set of predictor data with the appropriate subset of variables} \item{\code{y}}{ the current outcome data (either a numeric or factor vector)} \item{\code{first}}{ a single logical value for whether the current predictor set has all possible variables} \item{\code{last}}{ similar to \code{first}, but \code{TRUE} when the last model is fit with the final subset size and predictors.} \item{\code{...}}{optional arguments to pass to the fit function in the call to \code{rfe}} } The function should return a model object that can be used to generate predictions. The \code{pred} function returns a vector of predictions (numeric or factors) from the current model. The arguments are: \itemize{ \item{\code{object}}{ the model generated by the \code{fit} function} \item{\code{x}}{ the current set of predictor set for the held-back samples} } The \code{rank} function is used to return the predictors in the order of the most important to the least important. Inputs are: \itemize{ \item{\code{object}}{ the model generated by the \code{fit} function} \item{\code{x}}{ the current set of predictor set for the training samples} \item{\code{y}}{ the current training outcomes} } The function should return a data frame with a column called \code{var} that has the current variable names. The first row should be the most important predictor etc. Other columns can be included in the output and will be returned in the final \code{rfe} object. The \code{selectSize} function determines the optimal number of predictors based on the resampling output. Inputs for the function are: \itemize{ \item{\code{x}}{a matrix with columns for the performance metrics and the number of variables, called "\code{Variables}"} \item{\code{metric}}{a character string of the performance measure to optimize (e.g. "RMSE", "Rsquared", "Accuracy" or "Kappa")} \item{\code{maximize}}{a single logical for whether the metric should be maximized} } This function should return an integer corresponding to the optimal subset size. \pkg{caret} comes with two examples functions for this purpose: \code{\link{pickSizeBest}} and \code{\link{pickSizeTolerance}}. After the optimal subset size is determined, the \code{selectVar} function will be used to calculate the best rankings for each variable across all the resampling iterations. Inputs for the function are: \itemize{ \item{\code{y}}{ a list of variables importance for each resampling iteration and each subset size (generated by the user--defined \code{rank} function). In the example, each each of the cross--validation groups the output of the \code{rank} function is saved for each of the subset sizes (including the original subset). If the rankings are not recomputed at each iteration, the values will be the same within each cross-validation iteration.} \item{\code{size}}{ the integer returned by the \code{selectSize} function} } This function should return a character string of predictor names (of length \code{size}) in the order of most important to least important Examples of these functions are included in the package: \code{\link{lmFuncs}}, \code{\link{rfFuncs}}, \code{\link{treebagFuncs}} and \code{\link{nbFuncs}}. Model details about these functions, including examples, are at \url{http://topepo.github.io/caret/featureselection.html}. . } \value{ A list } \author{Max Kuhn } \seealso{ \code{\link{rfe}}, \code{\link{lmFuncs}}, \code{\link{rfFuncs}}, \code{\link{treebagFuncs}}, \code{\link{nbFuncs}}, \code{\link{pickSizeBest}}, \code{\link{pickSizeTolerance}} } \examples{ \dontrun{ subsetSizes <- c(2, 4, 6, 8) set.seed(123) seeds <- vector(mode = "list", length = 51) for(i in 1:50) seeds[[i]] <- sample.int(1000, length(subsetSizes) + 1) seeds[[51]] <- sample.int(1000, 1) set.seed(1) rfMod <- rfe(bbbDescr, logBBB, sizes = subsetSizes, rfeControl = rfeControl(functions = rfFuncs, seeds = seeds, number = 50)) } } \keyword{ utilities } caret/src/0000755000175000017500000000000012522120154012605 5ustar rbalintrbalintcaret/src/caret.c0000644000175000017500000001376312522120154014061 0ustar rbalintrbalint/* * class/class.c by W. N. Venables and B. D. Ripley Copyright (C) 1994-2002 * modificatios by Andre Williams */ #include #define EPS 1e-4 /* relative test of equality of distances */ #define RANDIN GetRNGstate() #define RANDOUT PutRNGstate() #define UNIF unif_rand() #define MAX_TIES 1000 /* Not worth doing this dynamically -- limits k + # ties + fence, in fact */ void knn3(Sint *kin, Sint *lin, Sint *pntr, Sint *pnte, Sint *p, double *train, Sint *class, double *test, Sint *votes, Sint *nc, Sint *cv, Sint *use_all,double *all_vote) { int i, index, j, k, k1, kinit = *kin, kn, l = *lin, mm, npat, ntie, ntr = *pntr, nte = *pnte, extras; int pos[MAX_TIES], nclass[MAX_TIES]; int j1, j2, needed, t; double dist, tmp, nndist[MAX_TIES]; RANDIN; /* Use a 'fence' in the (k+1)st position to avoid special cases. Simple insertion sort will suffice since k will be small. */ for (npat = 0; npat < nte; npat++) { kn = kinit; for (k = 0; k < kn; k++) nndist[k] = 0.99 * DOUBLE_XMAX; for (j = 0; j < ntr; j++) { if ((*cv > 0) && (j == npat)) continue; dist = 0.0; for (k = 0; k < *p; k++) { tmp = test[npat + k * nte] - train[j + k * ntr]; dist += tmp * tmp; } /* Use 'fuzz' since distance computed could depend on order of coordinates */ if (dist <= nndist[kinit - 1] * (1 + EPS)) for (k = 0; k <= kn; k++) if (dist < nndist[k]) { for (k1 = kn; k1 > k; k1--) { nndist[k1] = nndist[k1 - 1]; pos[k1] = pos[k1 - 1]; } nndist[k] = dist; pos[k] = j; /* Keep an extra distance if the largest current one ties with current kth */ if (nndist[kn] <= nndist[kinit - 1]) if (++kn == MAX_TIES - 1) error("too many ties in knn"); break; } nndist[kn] = 0.99 * DOUBLE_XMAX; } for (j = 0; j <= *nc; j++) votes[j] = 0; if (*use_all) { for (j = 0; j < kinit; j++) votes[class[pos[j]]]++; extras = 0; for (j = kinit; j < kn; j++) { if (nndist[j] > nndist[kinit - 1] * (1 + EPS)) break; extras++; votes[class[pos[j]]]++; } } else { /* break ties at random */ extras = 0; for (j = 0; j < kinit; j++) { if (nndist[j] >= nndist[kinit - 1] * (1 - EPS)) break; votes[class[pos[j]]]++; } j1 = j; if (j1 == kinit - 1) { /* no ties for largest */ votes[class[pos[j1]]]++; } else { /* Use reservoir sampling to choose amongst the tied distances */ j1 = j; needed = kinit - j1; for (j = 0; j < needed; j++) nclass[j] = class[pos[j1 + j]]; t = needed; for (j = j1 + needed; j < kn; j++) { if (nndist[j] > nndist[kinit - 1] * (1 + EPS)) break; if (++t * UNIF < needed) { j2 = j1 + (int) (UNIF * needed); nclass[j2] = class[pos[j]]; } } for (j = 0; j < needed; j++) votes[nclass[j]]++; } } /* Use reservoir sampling to choose amongst the tied votes */ ntie = 1; if (l > 0) mm = l - 1 + extras; else mm = 0; index = 0; for (i = 1; i <= *nc; i++){ if (votes[i] > mm) { ntie = 1; index = i; mm = votes[i]; } else if (votes[i] == mm && votes[i] >= l) { if (++ntie * UNIF < 1.0) index = i; } all_vote[npat*(*nc) + (i-1)] = (double)votes[i]/(kinit+extras); } } RANDOUT; } void knn3reg(Sint *kin, Sint *pntr, Sint *pnte, Sint *p, double *train, double *y, double *test, double *means, Sint *cv, Sint *use_all) { int j, k, k1, kinit = *kin, kn, npat, ntr = *pntr, nte = *pnte, extras; int pos[MAX_TIES]; int j1, j2, needed, t; double dist, tmp, nndist[MAX_TIES], ny[MAX_TIES]; int count; double sum; RANDIN; /* Use a 'fence' in the (k+1)st position to avoid special cases. Simple insertion sort will suffice since k will be small. */ for (npat = 0; npat < nte; npat++) { sum = 0.0; count = 0; kn = kinit; for (k = 0; k < kn; k++) nndist[k] = 0.99 * DOUBLE_XMAX; for (j = 0; j < ntr; j++) { if ((*cv > 0) && (j == npat)) continue; dist = 0.0; for (k = 0; k < *p; k++) { tmp = test[npat + k * nte] - train[j + k * ntr]; dist += tmp * tmp; } /* Use 'fuzz' since distance computed could depend on order of coordinates */ if (dist <= nndist[kinit - 1] * (1 + EPS)) for (k = 0; k <= kn; k++) if (dist < nndist[k]) { for (k1 = kn; k1 > k; k1--) { nndist[k1] = nndist[k1 - 1]; pos[k1] = pos[k1 - 1]; } nndist[k] = dist; pos[k] = j; /* Keep an extra distance if the largest current one ties with current kth */ if (nndist[kn] <= nndist[kinit - 1]) if (++kn == MAX_TIES - 1) error("too many ties in knn"); break; } nndist[kn] = 0.99 * DOUBLE_XMAX; } if (*use_all) { for (j = 0; j < kinit; j++) { sum += y[pos[j]]; } count += kinit; extras = 0; for (j = kinit; j < kn; j++) { if (nndist[j] > nndist[kinit - 1] * (1 + EPS)) break; extras++; sum += y[pos[j]]; } count += extras; } else { /* break ties at random */ extras = 0; for (j = 0; j < kinit; j++) { if (nndist[j] >= nndist[kinit - 1] * (1 - EPS)) break; sum += y[pos[j]]; } count += j; j1 = j; if (j1 == kinit - 1) { /* no ties for largest */ sum += y[pos[j1]]; count += 1; } else { /* Use reservoir sampling to choose amongst the tied distances */ j1 = j; needed = kinit - j1; for (j = 0; j < needed; j++) ny[j] = y[pos[j1 + j]]; t = needed; for (j = j1 + needed; j < kn; j++) { if (nndist[j] > nndist[kinit - 1] * (1 + EPS)) break; if (++t * UNIF < needed) { j2 = j1 + (int) (UNIF * needed); ny[j2] = y[pos[j]]; } } for (j = 0; j < needed; j++) { sum += ny[j]; } count += j; } } means[npat] = sum /(double)count; } RANDOUT; } #include "R_ext/Rdynload.h" static const R_CMethodDef CEntries[] = { {"knn3", (DL_FUNC) &knn3, 13}, {"knn3reg", (DL_FUNC) &knn3reg, 10}, {NULL, NULL, 0} }; void R_init_caret(DllInfo *dll) { 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That package is not on CRAN and can be installed from github using the \cpkg{devtools} package and \code{install_github('dmlc/xgboost',subdir='R-package')}. \item \code{dratewka} enabled \code{rbf} models for regression. \item A summary function for the multinomial likelihood called \code{mnLogLoss} was added. \item The total object size for \code{preProces} objects that used bagged imputation was reduced almost 5-fold. \item A new option to \code{trainControl} called \code{trim} was added where, if implemented, will reduce the model's footprint. However, features beyond simple prediction may not work. \item A rarely occurring bug in \code{gbm} model code was fixed (thanks to Wade Cooper) \item \code{splom.resamples} now respects the \code{models} argument \item A new argument to \code{lift} called \code{cuts} was added to allow more control over what thresholds are used to calculate the curve. \item The \code{cuts} argument of \code{calibration} now accepts a vector of cut points. \item Jason Schadewald noticed and fixed a bug in the man page for \code{dummyVars} \item Call objects were removed from the following models: \code{avNNet}, \code{bagFDA}, \code{icr}, \code{knn3}, \code{knnreg}, \code{pcaNNet}, and \code{plsda}. \item An argument was added to \code{createTimeSlices} to thin the number of resamples \item The RFE-related functions \code{lrFuncs}, \code{lmFuncs}, and \code{gamFuncs} were updated so that \code{rfe} accepts a matrix \code{x} argument. \item Using the default grid generation with \code{train} and \code{glmnet}, an initial \code{glmnet} fit is created with \code{alpha = 0.50} to define the \code{lambda} values. \item \code{train} models for \code{"gbm"}, \code{"gam"}, \code{"gamSpline"}, and \code{"gamLoess"} now allow their respective arguments for the outcome probability distribution to be passed to the underlying function. \item A bug in \code{print.varImp.train} was fixed. \item \code{train} now returns an additional column called \code{rowIndex} that is exposed when calling the summary function during resampling. \item The ability to compute class probabilities was removed from the \code{rpartCost} model since they are unlikely to agree with the class predictions. \item \code{extractProb} no longer redundantly calls \code{extractPrediction} to generate the class predictions. \item A new function called \code{var_seq} was added that finds a sequence of integers that can be useful for some tuning parameters such as random forests \code{mtry}. Model modules were update to use the new function. \item \code{n.minobsinnode} was added as a tuning parameter to \code{gbm} models. \item For models using out-of-bag resampling, \code{train} now properly checks the \code{metric} argument against the names of the measured outcomes. \item Both \code{createDataParition} and \code{createFolds} were modified to better handle cases where one or more class have very low numbers of data points. } } \section{Changes in version 6.0-41}{ \itemize{ \item The license was changed to GPL (>= 2) to accommodate new code from the GA package. \item New feature selection functions \code{gafs} and \code{safs} were added, along with helper functions and objects, were added. The package HTML was updated to expand more about feature selection. \item From the \cpkg{adabag} package, two new models were added: \code{AdaBag} and \code{AdaBoost.M1}. \item Weighted subspace random forests from the \cpkg{wsrf} package was added. \item Additional bagged FDA and MARS models were added (model codes \code{bagFDAGCV} and \code{bagEarthGCV}) were added that use the GCV statistic to prune the model. This leads to memory reductions during training. \item The model code for \code{ada} had a bug fix applied and the code was adapted to use the "sub-model trick" so it should train faster. \item A bug was fixed related to imputation when the formula method is used with \code{train} \item The old \code{drop = FALSE} bug was fixed in \code{getTrainPerf} \item A bug was fixed for custom models with no labels. \item A bug fix was made for bagged MARS models when predicting probabilities. \item In \code{train}, the argument \code{last} was being incorrectly set for the last model. \item Reynald Lescarbeau refactored \code{findCorrelation} to make it faster. \item The apparent performance values are not reported by \code{print.train} when the bootstrap 632 estimate is used. \item When a required package is missing, the code stops earlier with a more explicit error message. } } \section{Changes in version 6.0-37}{ \itemize{ \item Brenton Kenkel added ordered logistic or probit regression to \code{train} using \code{method = "polr"} from \cpkg{MASS} \item \code{LPH07_1} now encodes the noise variables as binary \item Both \code{rfe} and \code{sbf} get arguments for \code{indexOut} for their control functions. \item A reworked version of \code{\link{nearZerVar}} based on code from Michael Benesty was added the old version is now called \code{nzv} that uses less memory and can be used in parallel. \item The adaptive mixture discriminant model from the \cpkg{adaptDA} package was added as well as a robust mixture discriminant model from the \cpkg{robustDA} package. \item The multi-class discriminant model using binary predictors in the \cpkg{binda} package was added. \item Ensembles of partial least squares models (via the \cpkg{enpls}) package was added. \item A bug using \code{gbm} with Poisson data was fixed (thanks to user eriklampa) \item \code{sbfControl} now has a \code{multivariate} option where all the predictors are exposed to the scoring function at once. \item A function \code{compare_models} was added that is a simple comparison of models via \code{diff.resamples)}. \item The row names for the \code{variables} component of \code{rfe} objects were simplified. \item Philipp Bergmeir found a bug that was fixed where \code{bag} would not run in parallel. \item \code{predictionBounds} was not implemented during resampling. } } \section{Changes in version 6.0-35}{ \itemize{ \item A few bug fixes to \code{preProcess} were made related to KNN imputation. \item The parameter labels for polynomial SVM models were fixed \item The tags for \code{dnn} models were fixed. \item The following functions were removed from the package: \code{generateExprVal.method.trimMean}, \code{normalize.AffyBatch.normalize2Reference}, \code{normalize2Reference}, and \code{PLS}. The original code and the man files can be found at \href{https://github.com/topepo/caret/tree/master/deprecated}{https://github.com/topepo/caret/tree/master/deprecated}. \item A number of changes to comply with section 1.1.3.1 of "Writing R Extensions" were made. } } \section{Changes in version 6.0-34}{ \itemize{ \item For the input data \code{x} to \code{train}, we now respect the class of the input value to accommodate other data types (such as sparse matrices). There are some complications though; for pre-processing we throw a warning if the data are not simple matrices or data frames since there is some infrastructure that does not exist for other classes( e.g. \code{complete.cases}). We also throw a warning if \code{returnData <- TRUE} and it cannot be converted to a data frame. This allows the use of sparse matrices and text corpus to be used as inputs into that function. \item \code{plsRglm} was added. \item From the \cpkg{frbs}, the following rule-based models were added: \code{ANFIS}, \code{DENFIS}, \code{FH.GBML}, \code{FIR.DM}, \code{FRBCS.CHI}, \code{FRBCS.W}, \code{FS.HGD}, \code{GFS.FR.MOGAL}, \code{GFS.GCCL}, \code{GFS.LTS}, \code{GFS.THRIFT}, \code{HYFIS}, \code{SBC} and \code{WM}. Thanks to Lala Riza for suggesting these and facilitating their addition to the package. \item From the \cpkg{kernlab} package, SVM models using string kernels were added: \code{svmBoundrangeString}, \code{svmExpoString}, \code{svmSpectrumString} \item A function \code{update.rfe} was added. \item \code{cluster.resamples} was added to the namespace. \item An option to choose the \code{metric} was added to \code{summary.resamples}. \item \code{prcomp.resamples} now passed \code{...} to \code{prcomp}. Also the call to \code{prcomp} uses the formula method so that \code{na.action} can be used. \item The function \code{resamples} was enhanced so that \code{train} and \code{rfe} models that used \code{returnResamp="all"} subsets the resamples to get the appropriate values and issues a warning. The function also fills in missing model names if one or more are not given. \item Several regression simulation functions were added: \code{SLC14_1}, \code{SLC14_2}, \code{LPH07_1} and \code{LPH07_2} \item \code{print.train} was re-factored so that \code{format.data.frame} is now used. This should behave better when using \cpkg{knitr}. \item The error message in \code{train.formula} was improved to provide more helpful feedback in cases where there is at least one missing value in each row of the data set. \item \code{ggplot.train} was modified so that groups are distinguished by color and shape. \item Options were added to \code{plot.train} and \code{ggplot.train} called \code{nameInStrip} that will print the name and value of any tuning parameters shown in panels. \item A bug was fixed by Jia Xu within the knn imputation code used by \code{preProcess}. } } \section{Changes in version 6.0-30}{ \itemize{ \item A missing piece of documentation in \code{trainControl} for adaptive models was filled in. \item A warning was added to \code{plot.train} and \code{ggplot.train} to note that the relationship between the resampled performance measures and the tuning parameters can be deceiving when using adaptive resampling. \item A check was added to \code{trainControl} to make sure that a value of \code{min} makes sense when using adaptive resampling. } } \section{Changes in version 6.0-29}{ \itemize{ \item A man page with the list of models available via \code{train} was added back into the package. See \code{?models}. \item Thoralf Mildenberger found and fixed a bug in the variable importance calculation for neural network models. \item The output of \code{varImp} for \code{pamr} models was updated to clarify the ordering of the importance scores. \item \code{getModelInfo} was updated to generate a more informative error message if the user looks for a model that is not in the package's model library. \item A bug was fixed related to how seeds were set inside of \code{train}. \item The model \code{"parRF"} (parallel random forest) was added back into the library. \item When case weights are specified in \code{train}, the hold-out weights are exposed when computing the summary function. \item A check was made to convert a \code{data.table} given to \code{train} to a data frame (see \url{http://stackoverflow.com/questions/23256177/r-caret-renames-column-in-data-table-after-training}). } } \section{Changes in version 6.0-25}{ \itemize{ \item Changes were made that stopped execution of \code{train} if there are no rows in the data (changes suggested by Andrew Ziem) \item Andrew Ziem also helped improve the documentation. } } \section{Changes in version 6.0-24}{ \itemize{ \item Several models were updated to work with case weights. \item A bug in \code{rfe} was found where the largest subset size have the same results as the full model. Thanks to Jose Seoane for reporting the bug. } } \section{Changes in version 6.0-22}{ \itemize{ \item For some parallel processing technologies, the package now export more internal functions. \item A bug was fixed in \code{rfe} that occurred when LOO CV was used. \item Another bug was fixed that occurred for some models when \code{tuneGrid} contained only a single model. } } \section{Changes in version 6.0-21}{ \itemize{ \item A new system for user-defined models has been added. See \href{http://caret.r-forge.r-project.org/custom_models.html}{http://caret.r-forge.r-project.org/custom_models.html}. \item When creating the grid of tuning parameter values, the column names no longer need to be preceded by a period. Periods can still be used as before but are not required. This isn't guaranteed to break backwards compatibility but it may in some cases. \item \code{trainControl} now has a \code{method = "none"} resampling option that bypasses model tuning and fits the model to the entire training set. Note that if more than one model is specified an error will occur. \item \code{logicForest} models were removed since the package is now archived. \item \code{CSimca} and \code{RSimca} models from the \cpkg{rrcovHD} package were added. \item Model \code{elm} from the \cpkg{elmNN} package was added. \item Models \code{rknn} and \code{rknnBel} from the \cpkg{rknn} package were added \item Model \code{brnn} from the \cpkg{brnn} package was added. \item \code{panel.lift2} and \code{xyplot.lift} now have an argument called \code{values} that show the percentages of samples found for the specified percentages of samples tested. \item \code{train}, \code{rfe} and \code{sbf} should no longer throw a warning that "executing %dopar% sequentially: no parallel backend registered". \item A \code{ggplot} method for \code{train} was added. \item Imputation via medians was added to \code{preProcess} by Zachary Mayer. \item A small change was made to \code{rpart} models. Previously, when the final model is determined, it would be fit by specifying the model using the \code{cp} argument of \code{rpart.control}. This could lead to duplicated Cp values in the final list of possible Cp values. The current version fits the final model slightly different. An initial model is fit using \code{cp = 0} then it is pruned using \code{prune.rpart} to the desired depth. This shouldn't be different for the vast majority of data sets. Thanks to Jeff Evans for pointing this out. \item The method for estimating sigma for SVM and RVM models was slightly changed to make them consistent with how \code{ksvm} and \code{rvm} does the estimation. \item The default behavior for \code{returnResamp} in \code{rfeControl} and \code{sbfControl} is now \code{returnResamp = "final"}. \item \code{cluster} was added as a general class with a specific method for \code{resamples} objects. \item The refactoring of model code resulted in a number of packages being eliminated from the depends field. Additionally, a few were moved to exports. } } \section{Changes in version 5.17-07}{ \itemize{ \item A bug in \code{spatialSign} was fixed for data frames with a single column. \item Pre-processing was not applied to the training data set prior to grid creation. This is now done but only for models that use the data when defining the grid. Thanks to Brad Buchsbaum for finding the bug. \item Some code was added to \code{rfe} to truncate the subset sizes in case the user over-specified them. \item A bug was fixed in \code{gamFuncs} for the \code{rfe} function. \item Option in \code{trainControl}, \code{rfeControl} and \code{sbfControl} were added so that the user can set the seed at each resampling iteration (most useful for parallel processing). Thanks to Allan Engelhardt for the recommendation. \item Some internal refactoring of the data was done to prepare for some upcoming resampling options. \item \code{predict.train} now has an explicit \code{na.action} argument defaulted to \code{na.omit}. If imputation is used in \code{train}, then \code{na.action = na.pass} is recommended. \item A bug was fixed in \code{dummyVars} that occured when missing data were in \code{newdata}. The function \code{contr.dummy} is now deprecated and \code{contr.ltfr} should be used (if you are using it at all). Thanks to stackexchange user mchangun for finding the bug. \item A check is now done inside \code{dummyVars} when \code{levelsOnly = TRUE} to see if any predictors share common levels. \item A new option \code{fullRank} was added to \code{dummyVars}. When true, \code{contr.treatment} is used. Otherwise, \code{contr.ltfr} is used. \item A bug in \code{train} was fixed with \code{gbm} models (thanks to stackoverflow user screechOwl for finding it). } } \section{Changes in version 5.16-24}{ \itemize{ \item The \code{protoclass} function in the \cpkg{protoclass} package was added. The model uses a distance matrix as input and the \code{train} method also uses the \cpkg{proxy} package to compute the distance using the Minkowski distance. The two tuning parameters is the neighborhood size (\code{eps}) and the Minkowski distance parameter (\code{p}). \item A bug was (hopefully) fixed that occurred when some type of parallel processing was used with \code{train}. The problem is that the \code{methods} package was not being loaded in the workers. While reproducible, it is unknown why this occurs and why it is only for some technologies and systems. The \code{methods} package is now a formal dependency and we coerce the workers to load it remotely. \item A bug was fixed where some calls were printed twice. \item For \code{rpart}, \code{C5.0} and \code{ksvm}, cost-sensitive versions of these models for two classes were added to \code{train}. The method values are \code{rpartCost}, \code{C5.0Cost} and \code{svmRadialWeights}. \item The prediction code for the \code{ksvm} models was changed. There are some cases where the class predictions and the predicted class probabilities disagree. This usually happens when the probabilities are close to 0.50 (in the two class case). A \cpkg{kernlab} bug has been filed. In the meantime, if the \code{ksvm} model uses a probability model, the class probabilities are generated first and the predicted class is assigned to the probability with the largest value. Thanks to Kjell Johnson for finding that one. \item \code{print.train} was changed so that tune parameters that are logicals are printed well. } } \section{Changes in version 5.16-13}{ \itemize{ \item Added a few exemptions to the logic that determines whether a model call should be scrubbed. \item An error trap was created to catch issues with missing importance scores in \code{rfe}. } } \section{Changes in version 5.16-03}{ \itemize{ \item A function \code{twoClassSim} was added for benchmarking classification models. \item A bug was fixed in \code{predict.nullModel} related to predicted class probabilities. \item The version requirement for \cpkg{gbm} was updated. \item The function \code{getTrainPerf} was made visible. \item The automatic tuning grid for \code{sda} models from the \cpkg{sda} package was changed to include \code{lambda}. \item When \code{randomForests} is used with \code{train} and \code{tuneLength == 1}, the \code{randomForests} default value for \code{mtry} is used. \item Maximum uncertainty linear discriminant analysis (\code{Mlda}) and factor-based linear discriminant analysis (\code{RFlda}) from the \cpkg{HiDimDA} package were added to \code{train}. } } \section{Changes in version 5.15-87}{ \itemize{ \item Added the Yeo-Johnson power transformation from the \cpkg{car} package to the \code{preProcess} function. \item A \code{train} bug was fixed for the \code{rrlda} model (found by Tiago Branquinho Oliveira). \item The \code{extraTrees} model in the \cpkg{extraTrees} package was added. \item The \code{kknn.train} model in the \cpkg{kknn} package was added. \item A bug was fixed in \code{lrFuncs} where the class threshold was improperly set (thanks to David Meyer). \item A bug related to newer versions of the \cpkg{gbm} package were fixed. Another \cpkg{gbm} bug was fixed related to using non-Bernoulli distributions with two class outcomes (thanks to Zachary Mayer). \item The old funciton \code{getTrainPerf} was finally made visible. \item Some models are created using "do.call" and may contain the entire data set in the call object. A function to "scrub" some model call objects was added to reduce their size. \item The tuning process for \code{sda:::sda} models was changed to add the \code{lambda} parameter. } } \section{Changes in version 5.15-60}{ \itemize{ \item A bug in \code{predictors.earth}, discovered by Katrina Bennett, was fixed. \item A bug induced by version 5.15-052 for the bootstrap 632 rule was fixed. \item The DESCRIPTION file as of 5.15-048 should have used a version-specific lattice dependency. \item \code{lift} can compute gain and lift charts (and defaults to gain) \item The \cpkg{gbm} model was updated to handle 3 or more classes. \item For bagged trees using \cpkg{ipred}, the code in \code{train} defaults to \code{keepX = FALSE} to save space. Pass in \code{keepX = TRUE} to use out-of-bag sampling for this model. \item Changes were made to support vector machines for classification models due to bugs with class probabilities in the latest version of \cpkg{kernlab}. The \code{prob.model} will default to the value of \code{classProbs} in the \code{trControl} function. If \code{prob.model} is passed in as an argument to \code{train}, this specification over-rides the default. In other words, to avoid generating a probability model, set either \code{classProbs = FALSE} or \code{prob.model = FALSE}. } } \section{Changes in version 5.15-052}{ \itemize{ \item Added \code{bayesglm} from the \cpkg{arm} package. \item A few bugs were fixed in \code{bag}, thanks to Keith Woolner. Most notably, out-of-bag estimates are now computed when the prediction function includes a column called \code{pred}. \item Parallel processing was implemented in \code{bag} and \code{avNNet}, which can be turned off using an optional arguments. \item \code{train}, \code{rfe}, \code{sbf}, \code{bag} and \code{avNNet} were given an additional argument in their respective control files called \code{allowParallel} that defaults to \code{TRUE}. When \code{Code}, the code will be executed in parallel if a parallel backend (e.g. \cpkg{doMC}) is registered. When \code{allowParallel = FALSE}, the parallel backend is always ignored. The use case is when \code{rfe} or \code{sbf} calls \code{train}. If a parallel backend with P processors is being used, the combination of these functions will create P^2 processes. Since some operations benefit more from parallelization than others, the user has the ability to concentrate computing resources for specific functions. \item A new resampling function called \code{createTimeSlices} was contributed by Tony Cooper that generates cross-validation indices for time series data. \item A few more options were added to \code{trainControl}. \code{initialWindow}, \code{horizon} and \code{fixedWindow} are applicable for when \code{method = "timeslice"}. Another, \code{indexOut} is an optional list of resampling indices for the hold-out set. By default, these values are the unique set of data points not in the training set. \item A bug was fixed in multiclass \code{glmnet} models when generating class probabilities (thanks to Bradley Buchsbaum for finding it). } } \section{Changes in version 5.15-048}{ \itemize{ \item The three vignettes were removed and two things were added: a smaller vignette and a large collection of help pages at \url{http://caret.r-forge.r-project.org/}. \item Minkoo Seo found a bug where \code{na.action} was not being properly set with train.formula(). \item \code{parallel.resamples} was changed to properly account for missing values. \item Some testing code was removed from \code{probFunction} and \code{predictionFunction}. \item Fixed a bug in \code{sbf} exposed by a new version of \cpkg{plyr}. \item Changed the package dependency on \cpkg{reshape} to \cpkg{reshape2}. \item To be more consistent with recent versions of \cpkg{lattice}, the \code{parallel.resamples} function was changed to \code{parallelplot.resamples}. \item Since \code{ksvm} now allows probabilities when class weights are used, the default behavior in \code{train} is to set \code{prob.model = TRUE} unless the user explicitly sets it to \code{FALSE}. However, I have reported a bug in \code{ksvm} that gives inconsistent results with class weights, so this is not advised at this point in time. \item Bugs were fix in \code{predict.bagEarth} and \code{predict.bagFDA}. \item When using \code{rfeControl(saveDetails = TRUE)} or \code{sbfControl(saveDetails = TRUE)} an additional column is added to \code{object$pred} called \code{rowIndex}. This indicates the row from the original data that is being held-out. } } \section{Changes in version 5.15-045}{ \itemize{ \item A bug was fixed that induced \code{NA} values in SVM model predictions. } } \section{Changes in version 5.15-042}{ \itemize{ \item Many examples are wrapped in dontrun to speed up cran checking. \item The \code{scrda} methods were removed from the package (on 6/30/12, R Core sent an email that "since we haven't got fixes for long standing warnings of the rda packages since more than half a year now, we set the package to ORPHANED.") \item \cpkg{C50} was added (model codes \code{C5.0}, \code{C5.0Tree} and \code{C5.0Rules}). \item Fixed a bug in \code{train} with NaiveBayes when \code{fL != 0} was used \item The output of \code{train} with \code{verboseIter = TRUE} was modified to show the resample label as well as logging when the worker started and stopped the task (better when using parallel processing). \item Added a long-hidden function \code{downSample} for class imbalances \item An \code{upSample} function was added for class imbalances. \item A new file, aaa.R, was added to be compiled first that tries to eliminate the dreaded 'no visible binding for global variable' false positives. Specific namespaces were used with several functions for avoid similar warnings. \item A bug was fixed with \code{icr.formula} that was so ridiculous, I now know that nobody has ever used that function. \item Fixed a bug when using \code{method = "oob"} with \code{train} \item Some exceptions were added to \code{plot.train} so that some tuning parameters are better labeled. \item \code{dotplot.resamples} and \code{bwplot.resamples} now order the models using the first metric. \item A few of the lattice plots for the \code{resamples} class were changed such that when only one metric is shown: the strip is not shown and the x-axis label displays the metric \item When using \code{trainControl(savePredictions = TRUE)} an additional column is added to \code{object$pred} called \code{rowIndex}. This indicates the row from the original data that is being held-out. \item A variable importance function for \code{nnet} objects was created based on Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. ecological modelling, 160(3), 249–264. \item The \code{predictor} function for \code{glmnet} was update and a variable importance function was also added. \item Raghu Nidagal found a bug in \code{predict.avNNet} that was fixed. \item \code{sensitivity} and \code{specificity} were given an \code{na.rm} argument. \item A first attempt at fault tolerance was added to \code{train}. If a model fit fails, the predictions are set to \code{NA} and a warning is issued (eg "model fit failed for Fold04: sigma=0.00392, C=0.25"). When \code{verboseIter = TRUE}, the warning is also printed to the log. Resampled performance is calculated on only the non-missing estimates. This can also be done during predictions, but must be done on a model by model basis. Fault tolerance was added for \cpkg{kernlab} models only at this time. \item \code{lift} was modified in two ways. First, \code{cuts} is no longer an argument. The function always uses cuts based on the number of unique probability estimates. Second, a new argument called \code{label} is available to use alternate names for the models (e.g. names that are not valid R variable names). \item A bug in \code{print.bag} was fixed. \item Class probabilities were not being generated for sparseLDA models. \item Bugs were fixed in the new varImp methods for PART and RIPPER \item Starting using namespaces for \code{ctree} and \code{cforest} to avoid conflicts between duplicate function names in the \cpkg{party} and \cpkg{partykit} package \item A set of functions for RFE and logistic regression (\code{lrFuncs}) was added. \item A bug in \code{train} with \code{method="glmStepAIC"} was fixed so that \code{direction} and other \code{stepAIC} arguments were honored. \item A bug was fixed in \code{preProcess} where the number of ICA components was not specified. (thanks to Alexander Lebedev) \item Another bug was fixed for oblique random forest methods in \code{train}. (thanks to Alexander Lebedev) } } \section{Changes in version 5.15-023}{ \itemize{ \item The list of models that can accept factor inputs directly was expanded to include the \cpkg{RWeka} models, \code{ctree}, \code{cforest} and custom models. \item Added model \code{lda2}, which tunes by the number of functions used during prediction. \item \code{predict.train} allows probability predictions for custom models now (thanks to Peng Zhang) \item \code{confusionMatrix.train} was updated to use the default \code{confusionMatrix} code when \code{norm = "none"} and only a single hold-out was used. \item Added variable importance metrics for PART and RIPPER in the \cpkg{RWeka} package. \item vignettes were moved from /inst/doc to /vignettes } } \section{Changes in version 5.14-023}{ \itemize{ \item The model details in \code{?train} was changed to be more readable \item Added two models from the \cpkg{RRF} package. \code{RRF} uses a penalty for each predictor based on the scaled variable importance scores from a prior random forest fit. \code{RRFglobal} sets a common, global penalty across all predictors. \item Added two models from the \cpkg{KRLS} package: \code{krlsRadial} and \code{krlsPoly}. Both have kernel parameters (\code{sigma} and \code{degree}) and a common regularization parameter \code{lambda}. The default for \code{lambda} is \code{NA}, letting the \code{krls} function estimate it internally. \code{lambda} can also be specified via \code{tuneGrid}. \item \code{twoClassSummary} was modified to wrap the call to \code{pROC:::roc} in a \code{try} command. In cases where the hold-out data are only from one class, this produced an error. Now it generates \code{NA} values for the AUC when this occurs and a general warning is issued. \item The underlying workflows for \code{train} were modified so that missing values for performance measures would not throw an error (but will issue a warning). } } \section{Changes in version 5.13-037}{ \itemize{ \item Models \code{mlp}, \code{mlpWeightDecay}, \code{rbf} and \code{rbfDDA} were added from \cpkg{RSNNS}. \item Functions \code{roc}, \code{rocPoint} and \code{aucRoc} finally met their end. The cake was a lie. \item This NEWS file was converted over to Rd format. } } \section{Changes in version 5.13-020}{ \itemize{ \item \code{\link{lift}} was expanded into \code{\link{lift.formula}} for calculating the plot points and \code{\link{xyplot.lift}} to create the plot. \item The package vignettes were altered to stop loading external RData files. \item A few \code{match.call} changes were made to pass new R CMD check tests. \item \code{\link{calibration}}, \code{\link{calibration.formula}} and \code{\link{xyplot.calibration}} were created to make probability calibration plots. \item Model types \code{xyf} and \code{bdk} from the \cpkg{kohonen} package were added. \item \code{\link{update.train}} was added so that tuning parameters can be manually set if the automated approach to setting their values is insufficient. } } \section{Changes in version 5.11-006}{ \itemize{ \item When using \code{method = "pls"} in \code{\link{train}}, the \code{\link[pls]{plsr}} function used the default PLS algorithm ("kernelpls"). Now, the full orthogonal scores method is used. This results in the same model, but a more extensive set of values are calculated that enable VIP calculations (without much of a loss in computational efficient). \item A check was added to \code{\link{preProcess}} to ensure valid values of \code{method} were used. \item A new method, \code{kernelpls}, was added. \item \code{residuals} and \code{summary} methods were added to \code{\link{train}} objects that pass the final model to their respective functions. } } \section{Changes in version 5.11-006}{ \itemize{ \item Bugs were fixed that prevented hold-out predictions from being returned. } } \section{Changes in version 5.11-003}{ \itemize{ \item A bug in \code{roc} was found when the classes were completely separable. \item The ROC calculations for \code{\link{twoClassSummary}} and \code{\link{filterVarImp}} were changed to use the \cpkg{pROC} package. This, and other changes, have increased efficiency. For \code{\link{filterVarImp}} on the cell segmentation data lead to a 54-fold decrease in execution time. For the Glass data in the \cpkg{mlbench} package, the speedup was 37-fold. Warnings were added for \code{roc}, \code{aucRoc} and \code{rocPoint} regarding their deprecation. \item random ferns (package \cpkg{rFerns}) were added \item Another sparse LDA model (from the penalizedLDA) was also added } } \section{Changes in version 5.09-002}{ \itemize{ \item Fixed a bug which occurred when \code{\link[pls]{plsda}} models were used with class probabilities \item As of 8/15/11, the \code{\link[glmnet]{glmnet}} function was updated to return a character vector. Because of this, \code{\link{train}} required modification and a version requirement was put in the package description file. } } \section{Changes in version 5.09-006}{ \itemize{ \item Shea X made a suggestion and provided code to improve the speed of prediction when sequential parameters are used for \code{\link[gbm]{gbm}} models. \item Andrew Ziem suggested an error check with \code{metric = "ROC"} and \code{classProbs = FALSE}. \item Andrew Ziem found a bug in how \code{\link{train}} obtained \code{\link[earth]{earth}} class probabilities } } \section{Changes in version 5.08-011}{ \itemize{ \item Andrew Ziem found another small bug with parallel processing and \code{\link{train}} (functions in the caret namespace cannot be found). \item Ben Hoffman found a bug in \code{\link{pickSizeTolerance}} that was fixed. \item Jiaye Yu found (and fixed) a bug in getting predictions back from \code{\link{rfe}} } } \section{Changes in version 5.07-024}{ \itemize{ \item Using \code{saveDetails = TRUE} in \code{\link{sbfControl}} or \code{\link{rfeControl}} will save the predictions on the hold-out sets (Jiaye Yu wins the prize for finding that one). \item \code{\link{trainControl}} now has a logical to save the hold-out predictions. } } \section{Changes in version 5.07-005}{ \itemize{ \item \code{type = "prob"} was added for \code{\link{avNNet}} prediction. \item A warning was added when a model from \cpkg{RWeka} is used with \code{\link{train}} and (it appears that) \cpkg{multicore} is being used for parallel processing. The session will crash, so don't do that. \item A bug was fixed where the extrapolation limits were being applied in \code{\link{predict.train}} but not in \code{\link{extractPrediction}}. Thanks to Antoine Stevens for finding this. \item Modifications were made to some of the workflow code to expose internal functions. When parallel processing was used with \cpkg{doMPI} or \cpkg{doSMP}, \cpkg{foreach} did not find some \cpkg{caret} internals (but \cpkg{doMC} did). } } \section{Changes in version 5.07-001}{ \itemize{ \item changed calls to \code{\link[pls]{predict.mvr}} since the \cpkg{pls} package now has a namespace. } } \section{Changes in version 5.06-002}{ \itemize{ \item a beta version of custom models with \code{\link{train}} is included. The "caretTrain" vignette was updated with a new section that defines how to make custom models. } } \section{Changes in version 5.05-004}{ \itemize{ \item laying some of the groundwork for custom models \item updates to get away from deprecated (mean and sd on data frames) \item The pre-processing in \code{\link{train}} bug of the last version was not entirely squashed. Now it is. } } \section{Changes in version 5.04-007}{ \itemize{ \item \code{\link{panel.lift}} was moved out of the examples in \code{?lift} and into the package along with another function, \code{\link{panel.lift2}}. \item \code{\link{lift}} now uses \code{\link{panel.lift2}} by default \item Added robust regularized linear discriminant analysis from the \cpkg{rrlda} package \item Added \code{evtree} from \cpkg{evtree} \item A weird bug was fixed that occurred when some models were run with sequential parameters that were fixed to single values (thanks to Antoine Stevens for finding this issue). item Another bug was fixed where pre-processing with \code{\link{train}} could fail } } \section{Changes in version 5.03-003}{ \itemize{ \item pre-processing in \code{\link{train}} did not occur for the final model fit } } \section{Changes in version 5.02-011}{ \itemize{ \item A function, \code{\link{lift}}, was added to create lattice objects for lift plots. \item Several models were added from the \cpkg{obliqueRF} package: 'ORFridge' (linear combinations created using L2 regularization), 'ORFpls' (using partial least squares), 'ORFsvm' (linear support vector machines), and 'ORFlog' (using logistic regression). As of now, the package only support classification. \item Added regression models \code{simpls} and \code{widekernelpls}. These are new models since both \code{\link{train}} and \code{\link[pls]{plsr}} have an argument called \code{method}, so the computational algorithm could not be passed through using the three dots. \item Model \code{rpart} was added that uses \code{cp} as the tuning parameter. To make the model codes more consistent, \code{rpart} and \code{ctree} correspond to the nominal tuning parameters (\code{cp} and \code{mincriterion}, respectively) and \code{rpart2} and \code{ctree2} are the alternate versions using \code{maxdepth}. \item The text for \code{ctree}'s tuning parameter was changed to '1 - P-Value Threshold' \item The argument \code{controls} was not being properly passed through in models \code{ctree} and \code{ctree2}. } } \section{Changes in version 5.01-001}{ \itemize{ \item \code{controls} was not being set properly for \code{cforest} models in \code{\link{train}} \item The print methods for \code{\link{train}}, \code{\link{rfe}} and \code{\link{sbf}} did not recognize LOOCV \item \code{\link{avNNet}} sometimes failed with categorical outcomes with \code{bag = FALSE} \item A bug in \code{\link{preProcess}} was fixed that was triggered by matrices without dimnames (found by Allan Engelhardt) \item bagged MARS models with factor outcomes now work \item \code{cforest} was using the argument \code{control} instead of \code{controls} \item A few bugs for class probabilities were fixed for \code{slda}, \code{hdda}, \code{glmStepAIC}, \code{nodeHarvest}, \code{avNNet} and \code{sda} \item When looping over models and resamples, the \cpkg{foreach} package is now being used. Now, when using parallel processing, the \cpkg{caret} code stays the same and parallelism is invoked using one of the "do" packages (eg. \cpkg{doMC}, \cpkg{doMPI}, etc). This affects \code{\link{train}}, \code{\link{rfe}} and \code{\link{sbf}}. Their respective man pages have been revised to illustrate this change. \item The order of the results produced by \code{\link{defaultSummary}} were changed so that the ROC AUC is first \item A few man and C files were updated to eliminate R CMD check warnings \item Now that we are using foreach, the verbose option in \code{\link{trainControl}}, \code{\link{rfeControl}} and \code{\link{sbfControl}} are now defaulted to \code{FALSE} \item \code{\link{rfe}} now returns the variable ranks in a single data frame (previously there were data frames in lists of lists) for each of use. This will will break code from previous versions. The built-in RFE functions were also modified \item confusionMatrix methods for \code{\link{rfe}} and \code{\link{sbf}} were added \item NULL values of 'method' in \code{\link{preProcess}} are no longer allowed \item a model for ridge regression was added (\code{method = 'ridge'}) based on \code{\link[eslasticnet]{enet}}. } } \section{Changes in version 4.98}{ \itemize{ \item A bug was fixed in a few of the bagging aggregation functions (found by Harlan Harris). \item Fixed a bug spotted by Richard Marchese Robinson in createFolds when the outcome was numeric. The issue is that \code{\link{createFolds}} is trying to randomize \code{n/4} numeric samples to \code{k} folds. With less than 40 samples, it could not always do this and would generate less than \code{k} folds in some cases. The change will adjust the number of groups based on \code{n} and \code{k}. For small samples sizes, it will not use stratification. For larger data sets, it will at most group the data into quartiles. \item A function \code{\link{confusionMatrix.train}} was added to get an average confusion matrices across resampled hold-outs when using the \code{\link{train}} function for classification. \item Added another model, \code{\link{avNNet}}, that fits several neural networks via the \cpkg{nnet} package using different seeds, then averages the predictions of the networks. There is an additional bagging option. \item The default value of the 'var' argument of \code{\link{bag}} was changed. \item As requested, most options can be passed from \code{\link{train}} to \code{\link{preProcess}}. The \code{\link{trainControl}} function was re-factored and several options (e.g. \code{k}, \code{thresh}) were combined into a single list option called \code{preProcOptions}. The default is consistent with the original configuration: \code{preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5)} \item nother option was added to \code{\link{preProcess}}. The \code{pcaComp} option can be used to set exactly how many components are used (as opposed to just a threshold). It defaults to \code{NULL} so that the threshold method is still used by default, but a non-null value of \code{pcaComp} over-rides \code{thresh}. \item When created within \code{\link{train}}, the call for \code{\link{preProcess}} is now modified to be a text string ("scrubed") because the call could be very large. \item Removed two deprecated functions: \code{applyProcessing} and \code{processData}. \item A new version of the cell segmentation data was saved and the original version was moved to the package website (see \code{\link{segmentationData}} for location). First, several discrete versions of some of the predictors (with the suffix \code{"Status"}) were removed. Second, there are several skewed predictors with minimum values of zero (that would benefit from some transformation, such as the log). A constant value of 1 was added to these fields: \code{AvgIntenCh2}, \code{FiberAlign2Ch3}, \code{FiberAlign2Ch4}, \code{SpotFiberCountCh4} and \code{TotalIntenCh2}. } } \section{Changes in version 4.92}{ \itemize{ \item Some tweaks were made to \code{\link{plot.train}} in a effort to get the group key to look less horrid. \item \code{\link{train}}, \code{\link{rfe}} and \code{\link{sbf}} are now able to estimate the time that these models take to predict new samples. Their respective control objects have a new option, \code{timingSamps}, that indicates how many of the training set samples should be used for prediction (the default of zero means do not estimate the prediction time). \item \code{\link{xyplot.resamples}} was modified. A new argument, \code{what}, has values: \code{"scatter"} plots the resampled performance values for two models; \code{"BlandAltman"} plots the difference between two models by the average (aka a MA plot) for two models; \code{"tTime"}, \code{"mTime"}, \code{"pTime"} plot the total model building and tuning; time (\code{"t"}) or the final model building time (\code{"m"}) or the time to produce predictions (\code{"p"}) against a confidence interval for the average performance. 2+ models can be used. \item Three new model types were added to \code{\link{train}} using \code{\link[leaps]{regsubsets}} in the \cpkg{leaps} package: \code{"leapForward"}, \code{"leapBackward"} and \code{"leapSeq"}. The tuning parameter, \code{nvmax}, is the maximum number of terms in the subset. \item The seed was accidentally set when \code{\link{preProcess}} used ICA (spotted by Allan Engelhardt) \item \code{\link{preProcess}} was always being called (even to do nothing) (found by Guozhu Wen) } } \section{Changes in version 4.91}{ \itemize{ \item Added a few new models associated with the \cpkg{bst} package: bstTree, bstLs and bstSm. \item A model denoted as \code{"M5"} that combines M5P and M5Rules from the \cpkg{RWeka} package. This new model uses either of these functions depending on the tuning parameter \code{"rules"}. } } \section{Changes in version 4.90}{ \itemize{ \item Fixed a bug with \code{\link{train}} and \code{method = "penalized"}. Thanks to Fedor for finding it. } } \section{Changes in version 4.89}{ \itemize{ \item A new tuning parameter was added for \code{M5Rules} controlling smoothing. \item The Laplace correction value for Naive Bayes was also added as a tuning parameter. \item \code{\link{varImp.RandomForest}} was updated to work. It now requires a recent version of the \cpkg{party} package. } } \section{Changes in version 4.88}{ \itemize{ \item A variable importance method was created for \cpkg{Cubist} models. } } \section{Changes in version 4.87}{ \itemize{ \item Altered the earth/MARS/FDA labels to be more exact. \item Added cubist models from the \cpkg{Cubist} package. \item A new option to \code{\link{trainControl}} was added to allow users to constrain the possible predicted values of the model to the range seen in the training set or a user-defined range. One-sided ranges are also allowed. } } \section{Changes in version 4.85}{ \itemize{ \item Two typos fixed in \code{\link{print.rfe}} and \code{\link{print.sbf}} (thanks to Jan Lammertyn) } } \section{Changes in version 4.83}{ \itemize{ \item \code{\link{dummyVars}} failed with formulas using \code{"."} (\code{all.vars} does not handle this well) \item \code{tree2} was failing for some classification models \item When SVM classification models are used with \code{class.weights}, the options \code{prob.model} is automatically set to \code{FALSE} (otherwise, it is always set to \code{TRUE}). A warning is issued that the model will not be able to create class probabilities. \item Also for SVM classification models, there are cases when the probability model generates negative class probabilities. In these cases, we assign a probability of zero then coerce the probabilities to sum to one. \item Several typos in the help pages were fixed (thanks to Andrew Ziem). \item Added a new model, \code{svmRadialCost}, that fits the SVM model and estimates the \code{sigma} parameter for each resample (to properly capture the uncertainty). \item \code{\link{preProcess}} has a new method called \code{"range"} that scales the predictors to [0, 1] (which is approximate for new samples if the training set ranges is narrow in comparison). \item A check was added to \code{\link{train}} to make sure that, when the user passes a data frame to \code{\link{tuneGrid}}, the names are correct and complete. \item \code{\link{print.train}} prints the number of classes and levels for classification models. } } \section{Changes in version 4.78}{ \itemize{ \item Added a few bagging modules. See ?bag. \item Added basic timings of the entire call to \code{\link{train}}, \code{\link{rfe}} and \code{\link{sbf}} as well as the fit time of the final model. These are stored in an element called "times". \item The data files were updated to use better compression, which added a higher R version dependency. \item \code{\link{plot.train}} was pretty much re-written to more effectively use trellis theme defaults and to allow arguments (e.g. axis labels, keys, etc) to be passed in to over-ride the defaults. \item Bug fix for lda bagging function \item Bug fix for \code{\link{print.train}} when \code{preProc} is \code{NULL} \item \code{\link{predict.BoxCoxTrans}} would go all klablooey if there were missing values \item \code{\link{varImp.rpart}} was failing with some models (thanks to Maria Delgado) } } \section{Changes in version 4.77}{ \itemize{ \item A new class was added or estimating and applying the Box-Cox transformation to data called BoxCoxTrans. This is also included as an option to transform predictor variables. Although the Box-Tidwell transformation was invented for this purpose, the Box-Cox transformation is more straightforward, less prone to numerical issues and just as effective. This method was also added to \code{\link{preProcess}}. \item Fixed mis-labelled x axis in \code{\link{plot.train}} when a transformation is applied for models with three tuning parameters. \item When plotting a \code{\link{train}} object with \code{method == "gbm"} and multiple values of the shrinkage parameter, the ordering of panels was improved. \item Fixed bugs for regression prediction using \code{partDSA} and \code{qrf}. \item Another bug, reported by Jan Lammertyn, related to \code{\link{extractPrediciton}} with a single predictor was also fixed. } } \section{Changes in version 4.76}{ \itemize{ \item Fixed a bug where linear SVM models were not working for classification } } \section{Changes in version 4.75}{ \itemize{ \item \code{'gcvEearth'} which is the basic MARS model. The pruning procedure is the nominal one based on GCV; only the degree is tuned by \code{\link{train}}. \item \code{'qrnn'} for quantile regression neural networks from the \cpkg{qrnn} package. \item \code{'Boruta'} for random forests models with feature selection via the \cpkg{Boruta} package. } } \section{Changes in version 4.74}{ \itemize{ \item Some changes to \code{\link{print.train}}: the call is not automatically printed (but can be when \code{\link{print.train}} is explicitly invoked); the "Selected" column is also not automatically printed (but can be); non-table text now respects \code{options("width")}; only significant digits are now printed when tuning parameters are kept at a constant value } } \section{Changes in version 4.73}{ \itemize{ \item Bug fixes to \code{\link{preProcess}} related to complete.cases and a single predictor. \item For knn models (knn3 and knnreg), added automatic conversion of data frames to matrices } } \section{Changes in version 4.72}{ \itemize{ \item A new function for \code{\link{rfe}} with \cpkg{gam} was added. \item "Down-sampling" was implemented with \code{\link{bag}} so that, for classification models, each class has the same number of classes as the smallest class. \item Added a new class, \code{\link{dummyVars}}, that creates an entire set of binary dummy variables (instead of the reduced, full rank set). The initial code was suggested by Gabor Grothendieck on R-Help. The predict method is used to create dummy variables for any data set. \item Added \code{\link{R2}} and \code{\link{RMSE}} functions for evaluating regression models \item \code{\link{varImp.gam}} failed to recognize objects from \cpkg{mgcv} \item a small fix to test a logical vector \code{\link{filterVarImp}} \item When \code{\link{diff.resamples}} calculated the number of comparisons, the \code{"models"} argument was ignored. \item \code{\link{predict.bag}} was ignoring \code{type = "prob"} \item Minor updates to conform to R 2.13.0 } } \section{Changes in version 4.70}{ \itemize{ \item Added a warning to \code{\link{train}} when class levels are not valid R variable names. \item Fixed a bug in the variable importance function for \code{multinom} objects. \item Added p-value adjustments to \code{\link{summary.diff.resamples}}. Confidence intervals in \code{\link{dotplot.diff.resamples}} are adjusted accordingly if the Bonferroni is used. \item For \code{\link{dotplot.resamples}}, no point was plotted when the upper and/or lower interval values were NaN. Now, the point is plotted but without the interval bars. \item Updated \code{\link{print.rfe}} to correctly describe new resampling methods. } } \section{Changes in version 4.69}{ \itemize{ \item Fixed a bug in \code{\link{predict.rfe}} where an error was thrown even though the required predictors were in \code{newdata}. \item Changed \code{\link{preProcess}} so that centering and scaling are both automatic when PCA or ICA are requested. } } \section{Changes in version 4.68}{ \itemize{ \item Added two functions, \code{\link{checkResamples}} and \code{\link{checkConditionalX}} that identify predictor data with degenerate distributions when conditioned on a factor. \item Added a high content screening data set (\code{\link{segmentedData}}) from Hill et al. Impact of image segmentation on high-content screening data quality for SK-BR-3 cells. BMC bioinformatics (2007) vol. 8 (1) pp. 340. \item Fixed bugs in how \code{\link{sbf}} objects were printed (when using repeated CV) and classification models with \cpkg{earth} and \code{classProbs = TRUE}. } } \section{Changes in version 4.67}{ \itemize{ \item Added \code{\link{predict.rfe}} \item Added imputation using bagged regression trees to \code{\link{preProcess}}. \item Fixed bug in \code{\link{varImp.rfe}} that caused incorrect results (thanks to Lawrence Mosley for the find). } } \section{Changes in version 4.65}{ \itemize{ \item Fixed a bug where \code{\link{train}} would not allow knn imputation. \item \code{\link{filterVarImp}} and \code{roc} now check for missing values and use complete data for each predictor (instead of case- wise deletion across all predictors). } } \section{Changes in version 4.64}{ \itemize{ \item Fixed bug introduced in the last version with \code{createDataPartition(... list = FALSE)}. \item Fixed a bug predicting class probabilities when using \cpkg{earth}/glm models \item Fixed a bug that occurred when \code{\link{train}} was used with \code{ctree} or \code{tree2} methods. \item Fixed bugs in \code{\link{rfe}} and \code{\link{sbf}} when running in parallel; not all the resampling results were saved } } \section{Changes in version 4.63}{ \itemize{ \item A p-value from McNemar's test was added to \code{\link{confusionMatrix}}. \item Updated \code{\link{print.train}} so that constant parameters are not shown in the table (but a note is written below the table instead). Also, the output was changed slightly to be more easily read (I hope) \item Adapted \code{\link{varImp.gam}} to work with either \cpkg{mgcv} or \cpkg{gam} packages. \item Expanded the tuning parameters for \code{lvq}. \item Some of the examples in the Model Building vignette were changed \item Added bootstrap 632 rule and repeated cross-validation to \code{\link{trainControl}}. \item A new function, \code{\link{createMultiFolds}}, is used to generate indices for repeated CV. \item The various resampling functions now have *named* lists as output (with prefixes "Fold" for cv and repeated cv and "Resample" otherwise) \item Pre-processing has been added to \code{\link{train}} with the \code{\link{preProcess}} argument. This has been tested when caret function are used with \code{\link{rfe}} and \code{\link{sbf}} (via \code{\link{caretFuncs}} and \code{\link{caretSBF}}, respectively). \item When \code{preProcess(method = "spatialSign")}, centering and scaling is done automatically too. Also, a bug was fixed that stopped the transformation from being executed. \item knn imputation was added to \code{\link{preProcess}}. The \cpkg{RANN} package is used to find the neighbors (the knn impute function in the impute library was consistently generating segmentation faults, so we wrote our own). \item Changed the behavior of \code{\link{preProcess}} in situations where scaling is requested but there is no variation in the predictor. Previously, the method would fail. Now a warning is issued and the value of the standard deviation is coerced to be one (so that scaling has no effect). } } \section{Changes in version 4.62}{ \itemize{ \item Added \code{gam} from \cpkg{mgcv} (with smoothing splines and feature selection) and \code{gam} from \cpkg{gam} (with basic splines and loess) smoothers. For these models, a formula is derived from the data where "near zero variance" predictors (see \code{\link{nearZerVar}}) are excluded and predictors with less than 10 distinct values are entered as linear (i.e. unsmoothed) terms. } } \section{Changes in version 4.61}{ \itemize{ \item Changed \cpkg{earth} fit for classification models to use the \code{glm} argument with a binomial family. \item Added \code{\link{varImp.multinom}}, which is based on the absolute values of the model coefficients } } \section{Changes in version 4.60}{ \itemize{ \item The feature selection vignette was updated slightly (again). } } \section{Changes in version 4.59}{ \itemize{ \item Updated \code{\link{rfe}} and \code{\link{sbf}} to include class probabilities in performance calculations. \item Also, the names of the resampling indices were harmonized across \code{\link{train}}, \code{\link{rfe}} and \code{\link{sbf}}. \item The feature selection vignette was updated slightly. } } \section{Changes in version 4.58}{ \itemize{ \item Added the ability to include class probabilities in performance calculations. See \code{\link{trainControl}} and \code{\link{twoClassSummary}}. \item Updated and restructured the main vignette. } } \section{Changes in version 4.57}{ \itemize{ \item Internal changes related to how predictions from models are stored and summarized. With the exception of loo, the model performance values are calculated by the workers instead of the main program. This should reduce i/o and lay some groundwork for upcoming changes. \item The default grid for \cpkg{relaxo} models were changed based on and initial model fit. \item \cpkg{partDSA} model predictions were modified; there were cases where the user might request X partitions, but the model only produced Y < X. In these cases, the partitions for missing models were replaced with the largest model that was fit. \item The function \code{\link{modelLookup}} was put in the namespace and a man file was added. \item The names of the resample indices are automatically reset, even if the user specified them. } } \section{Changes in version 4.56}{ \itemize{ \item Fixed a bug generated a few versions ago where \code{\link{varImp}} for \code{plsda} and \code{fda} objects crashed. } } \section{Changes in version 4.55}{ \itemize{ \item When computing the scale parameter for RBF kernels, the option to automatically scale the data was changed to \code{TRUE} } } \section{Changes in version 4.54}{ \itemize{ \item Added \code{logic.bagging} in \pkg{logicFT} with \code{method = "logicBag"} } } \section{Changes in version 4.53}{ \itemize{ \item Fixed a bug in \code{\link{varImp.train}} related to nearest shrunken centroid models. \item Added logic regression and logic forests } } \section{Changes in version 4.51}{ \itemize{ \item Added an option to \code{\link{splom.resamples}} so that the variables in the scatter plots are models or metrics. } } \section{Changes in version 4.50}{ \itemize{ \item Added \code{\link{dotplot.resamples}} plus acknowledgements to Hothorn et al. (2005) and Eugster et al. (2008) } } \section{Changes in version 4.49}{ \itemize{ \item Enhanced the \code{tuneGrid} option to allow a function to be passed in. } } \section{Changes in version 4.48}{ \itemize{ \item Added a \code{prcomp} method for the \code{resamples} class } } \section{Changes in version 4.47}{ \itemize{ \item Extended \code{\link{resamples}} to work with \code{\link{rfe}} and \code{\link{sbf}} } } \section{Changes in version 4.46}{ \itemize{ \item Cleaned up some of the man files for the resamples class and added \code{\link{parallel.resamples}}. \item Fixed a bug in \code{\link{diff.resamples}} where \code{...} were not being passed to the test statistic function. \item Added more log messages in \code{\link{train}} when running verbose. \item Added the German credit data set. } } \section{Changes in version 4.45}{ \itemize{ \item Added a general framework for bagging models via the \code{\link{bag}} function. Also, model type \code{"hdda"} from the \cpkg{HDclassif} package was added. } } \section{Changes in version 4.44}{ \itemize{ \item Added \cpkg{neuralnet}, \code{quantregForest} and \code{rda} (from \cpkg{rda}) to \code{\link{train}}. Since there is a naming conflict with \code{rda} from \cpkg{mda}, the \cpkg{rda} model was given a method value of \code{"scrda"}. } } \section{Changes in version 4.43}{ \itemize{ \item Tthe resampling estimate of the standard deviation given by \code{\link{train}} since v 4.39 was wrong \item A new field was added to \code{\link{varImp.mvr}} called \code{"estimate"}. In cases where the mvr model had multiple estimates of performance (e.g. training set, CV, etc) the user can now select which estimate they want to be used in the importance calculation (thanks to Sophie Bréand for finding this) } } \section{Changes in version 4.42}{ \itemize{ \item Added \code{\link{predict.sbf}} and modified the structure of the \code{\link{sbf}} helper functions. The \code{"score"} function only computes the metric used to filter and the filter function does the actual filtering. This was changed so that FDR corrections or other operations that use all of the p-values can be computed. \item Also, the formatting of p-values in \code{\link{print.confusionMatrix}} was changed \item An argument was added to \code{\link{maxDissim}} so that the variable name is returned instead of the index. \item Independent component analysis was added to the list of pre-processing operations and a new model ("icr") was added to fit a pcr-like model with the ICA components. } } \section{Changes in version 4.40}{ \itemize{ \item Added \code{hda} and cleaned up the \cpkg{caret} training vignette } } \section{Changes in version 4.39}{ \itemize{ \item Added several classes for examining the resampling results. There are methods for estimating pair-wise differences and lattice functions for visualization. The training vignette has a new section describing the new features. } } \section{Changes in version 4.38}{ \itemize{ \item Added \cpkg{partDSA} and \code{stepAIC} for linear models and generalized linear models } } \section{Changes in version 4.37}{ \itemize{ \item Fixed a new bug in how resampling results are exported } } \section{Changes in version 4.36}{ \itemize{ \item Added penalized linear models from the \cpkg{foba} package } } \section{Changes in version 4.35}{ \itemize{ \item Added \code{rocc} classification and fixed a typo. } } \section{Changes in version 4.34}{ \itemize{ \item Added two new data sets: \code{\link{dhfr}} and \code{\link{cars}} } } \section{Changes in version 4.33}{ \itemize{ \item Added GAMens (ensembles using gams) \item Fixed a bug in \code{roc} that, for some data cases, would reverse the "positive" class and report sensitivity as specificity and vice-versa. } } \section{Changes in version 4.32}{ \itemize{ \item Added a parallel random forest method in \code{\link{train}} using the \cpkg{foreach} package. \item Also added penalized logistic regression using the \code{plr} function in the \cpkg{stepPlr} package. } } \section{Changes in version 4.31}{ \itemize{ \item Added a new feature selection function, \code{\link{sbf}} (for selection by filter). \item Fixed bug in \code{\link{rfe}} that did not affect the results, but did produce a warning. \item A new model function, \code{\link{nullModel}}, was added. This model fits either the mean only model for regression or the majority class model for classification. \item Also, ldaFuncs had a bug fixed. \item Minor changes to Rd files } } \section{Changes in version 4.30}{ \itemize{ \item For whatever reason, there is now a function in the \cpkg{spls} package by the name of splsda that does the same thing. A few functions and a man page were changed to ensure backwards compatibility. } } \section{Changes in version 4.29}{ \itemize{ \item Added stepwise variable selection for \code{lda} and \code{qda} using the \code{stepclass} function in \cpkg{klaR} } } \section{Changes in version 4.28}{ \itemize{ \item Added robust linear and quadratic discriminant analysis functions from \cpkg{rrcov}. \item Also added another column to the output of \code{\link{extractProb}} and \code{\link{extractPrediction}} that saves the name of the model object so that you can have multiple models of the same type and tell which predictions came from which model. \item Changes were made to \code{plotClassProbs}: new parameters were added and densityplots can now be produced. } } \section{Changes in version 4.27}{ \itemize{ \item Added \cpkg{nodeHarvest} } } \section{Changes in version 4.26}{ \itemize{ \item Fixed a bug in \code{\link{caretFunc}} that led to NaN variable rankings, so that the first k terms were always selected. } } \section{Changes in version 4.25}{ \itemize{ \item Added parallel processing functionality for \code{\link{rfe}} } } \section{Changes in version 4.24}{ \itemize{ \item Added the ability to use custom metrics with \code{\link{rfe}} } } \section{Changes in version 4.22}{ \itemize{ \item Many Rd changes to work with updated parser. } } \section{Changes in version 4.21}{ \itemize{ \item Re-saved data in more compressed format } } \section{Changes in version 4.20}{ \itemize{ \item Added \code{pcr} as a method } } \section{Changes in version 4.19}{ \itemize{ \item Weights argument was added to \code{\link{train}} for models that accept weights \item Also, a bug was fixed for lasso regression (wrong lambda specification) and other for prediction in naive Bayes models with a single predictor. } } \section{Changes in version 4.18}{ \itemize{ \item Fixed bug in new \code{\link{nearZeroVar}} and updated \code{format.earth} so that it does not automatically print the formula } } \section{Changes in version 4.17}{ \itemize{ \item Added a new version of \code{\link{nearZeroVar}} from Allan Engelhardt that is much faster } } \section{Changes in version 4.16}{ \itemize{ \item Fixed bugs in \code{\link{extractProb}} (for glmnet) and \code{\link{filterVarImp}}. \item For glmnet, the user can now pass in their own value of family to \code{\link{train}} (otherwise \code{\link{train}} will set it depending on the mode of the outcome). However, glmnet doesn't have much support for families at this time, so you can't change links or try other distributions. } } \section{Changes in version 4.15}{ \itemize{ \item Fixed bug in \code{\link{createFolds}} when the smallest y value is more than 25% of the data } } \section{Changes in version 4.14}{ \itemize{ \item Fixed bug in \code{\link{print.train}} } } \section{Changes in version 4.13}{ \itemize{ \item Added vbmp from \cpkg{vbmp} package } } \section{Changes in version 4.12}{ \itemize{ \item Added additional error check to \code{\link{confusionMatrix}} \item Fixed an absurd typo in \code{\link{print.confusionMatrix}} } } \section{Changes in version 4.11}{ \itemize{ \item Added: linear kernels for svm, rvm and Gaussian processes; \code{rlm} from \cpkg{MASS}; a knn regression model, knnreg \item A set of functions (class "\code{\link{classDist}}") to computes the class centroids and covariance matrix for a training set for determining Mahalanobis distances of samples to each class centroid was added \item a set of functions (\code{\link{rfe}}) for doing recursive feature selection (aka backwards selection). A new vignette was added for more details } } \section{Changes in version 4.10}{ \itemize{ \item Added \code{OneR} and \code{PART} from \cpkg{RWeka} } } \section{Changes in version 4.09}{ \itemize{ \item Fixed error in documentation for \code{confusionMatrix}. The old doc had \code{"Detection Prevalence = A/(A+B)"} and the new one has \code{"Detection Prevalence =(A+B)(A+B+C+D)"}. The underlying code was correct. \item Added \code{lars} (\code{fraction} and \code{step} as parameters) } } \section{Changes in version 4.08}{ \itemize{ \item Updated \code{\link{train}} and \code{bagEarth} to allow \code{earth} for classification models } } \section{Changes in version 4.07}{ \itemize{ \item Added \cpkg{glmnet} models } } \section{Changes in version 4.06}{ \itemize{ \item Added code for sparse PLS classification. \item Fix a bug in prediction for \code{caTools::LogitBoost} } } \section{Changes in version 4.05}{ \itemize{ \item Updated again for more stringent R CMD check tests in R-devel 2.9 } } \section{Changes in version 4.04}{ \itemize{ \item Updated for more stringent R CMD check tests in R-devel 2.9 } } \section{Changes in version 4.03}{ \itemize{ \item Significant internal changes were made to how the models are fit. Now, the function used to compute the models is passed in as a parameter (defaulting to \code{lapply}). In this way, users can use their own parallel processing software without new versions of \cpkg{caret}. Examples are given in \code{\link{train}}. \item Also, fixed a bug where the MSE (instead of RMSE) was reported for random forest OOB resampling \item There are more examples in \code{\link{train}}. \item Changes to \code{confusionMatrix}, \code{sensitivity}, \code{specificity} and the predictive value functions: each was made more generic with default and \code{table} methods; \code{confusionMatrix} "extractor" functions for matrices and tables were added; the pos/neg predicted value computations were changed to incorporate prevalence; prevalence was added as an option to several functions; detection rate and prevalence statistics were added to \code{confusionMatrix}; and the examples were expanded in the help files. \item This version of caret will break compatibility with \pkg{caretLSF} and \pkg{caretNWS}. However, these packages will not be needed now and will be deprecated. } } \section{Changes in version 3.51}{ \itemize{ \item Updated the man files and manuals. } } \section{Changes in version 3.50}{ \itemize{ \item Added \code{qda}, \code{mda} and \code{pda}. } } \section{Changes in version 3.49}{ \itemize{ \item Fixed bug in \code{resampleHist}. Also added a check in the \code{\link{train}} functions that error trapped with \code{glm} models and > 2 classes } } \section{Changes in version 3.48}{ \itemize{ \item Added \code{glm}s. Also, added \code{varImp.bagEarth} to the namespace. } } \section{Changes in version 3.47}{ \itemize{ \item Added \code{sda} from the \cpkg{sda} package. There was a naming conflict between \code{sda::sda} and \code{sparseLDA:::sda}. The method value for \code{sparseLDA} was changed from "sda" to "sparseLDA". } } \section{Changes in version 3.46}{ \itemize{ \item Added \code{spls} from the \cpkg{spls} package } } \section{Changes in version 3.45}{ \itemize{ \item Added caching of \cpkg{RWeka} objects to that they can be saved to the file system and used in other sessions. (changes per Kurt Hornik on 2008-10-05) } } \section{Changes in version 3.44}{ \itemize{ \item Added \code{sda} from the \cpkg{sparseLDA} package (not on CRAN). \item Also, a bug was fixed where the ellipses were not passed into a few of the newer models (such as \code{penalized} and \code{ppr}) } } \section{Changes in version 3.43}{ \itemize{ \item Added the penalized model from the \cpkg{penalized} package. In \cpkg{caret}, it is regression only although the package allows for classification via glm models. However, it does not allow the user to pass the classes in (just an indicator matrix). Because of this, it doesn't really work with the rest of the classification tools in the package. } } \section{Changes in version 3.42}{ \itemize{ \item Added a little more formatting to \code{\link{print.train}} } } \section{Changes in version 3.41}{ \itemize{ \item For \code{gbm}, let the user over-ride the default value of the \code{distribution} argument (brought us by Peter Tait via RHelp). } } \section{Changes in version 3.40}{ \itemize{ \item Changed \code{predict.preProcess} so that it doesn't crash if \code{newdata} does not have all of the variables used to originally pre-process *unless* PCA processing was requested. } } \section{Changes in version 3.39}{ \itemize{ \item Fixed bug in \code{varImp.rpart} when the model had only primary splits. \item Minor changes to the Affy normalization code \item Changed typo in \code{predictors} man page } } \section{Changes in version 3.38}{ \itemize{ \item Added a new class called \code{predictors} that returns the names of the predictors that were used in the final model. \item Also added \code{ppr} from the \code{stats} package. \item Minor update to the project web page to deal with IE issues } } \section{Changes in version 3.37}{ \itemize{ \item Added the ability of \code{\link{train}} to use custom made performance functions so that the tuning parameters can be chosen on the basis of things other than RMSE/R-squared and Accuracy/Kappa. \item A new argument was added to \code{\link{trainControl}} called "summaryFunction" that is used to specify the function used to compute performance metrics. The default function preserves the functionality prior to this new version \item a new argument to \code{\link{train}} is "maximize" which is a logical for whether the performance measure specified in the "metric" argument to \code{\link{train}} should be maximized or minimized. \item The selection function specified in \code{\link{trainControl}} carries the maximize argument with it so that customized performance metrics can be used. \item A bug was fixed in \code{confusionMatrix} (thanks to Gabor Grothendieck) \item Another bug was fixed related to predictions from least square SVMs } } \section{Changes in version 3.36}{ \itemize{ \item Added \code{superpc} from the \cpkg{superpc} package. One note: the \code{data} argument that is passed to \code{superpc} is saved in the object that results from \code{superpc.train}. This is used later in the prediction function. } } \section{Changes in version 3.35}{ \itemize{ \item Added \code{slda} from \cpkg{ipred}. } } \section{Changes in version 3.34}{ \itemize{ \item Fixed a few bugs related to the lattice plots from version 3.33. \item Also added the ripper (aka \code{JRip}) and logistic model trees from \cpkg{RWeka} } } \section{Changes in version 3.33}{ \itemize{ \item Added \code{xyplot.train}, \code{densityplot.train}, \code{histogram.train} and \code{stripplot.train}. These are all functions to plot the resampling points. There is some overlap between these functions, \code{plot.train} and \code{resampleHist}. \code{plot.train} gives the average metrics only while these plot all of the resampled performance metrics. \code{resampleHist} could plot all of the points, but only for the final optimal set of predictors. \item To use these functions, there is a new argument in \code{\link{trainControl}} called \code{\link{returnResamp}} which should have values "none", "final" and "all". The default is "final" to be consistent with previous versions, but "all" should be specified to use these new functions to their fullest. } } \section{Changes in version 3.32}{ \itemize{ \item The functions \code{\link{predict.train}} and \code{\link{predict.list}} were added to use as alternatives to the \code{\link{extractPrediction}} and \code{\link{extractProbs}} functions. \item Added C4.5 (aka \code{J48}) and rules-based models (M5 prime) from \cpkg{RWeka}. \item Also added \code{logitBoost} from the \cpkg{caTools} package. This package doesn't have a namespace and \cpkg{RWeka} has a function with the same name. It was suggested to use the "::" prefix to differentiate them (but we'll see how this works). } } caret/inst/doc/0000755000175000017500000000000012633002545013546 5ustar rbalintrbalintcaret/inst/doc/caret.Rnw0000644000175000017500000003665212522120154015342 0ustar rbalintrbalint% \VignetteIndexEntry{ A Short Introduction to the caret Package} % \VignetteDepends{caret} % \VignettePackage{caret} \documentclass[12pt]{article} \usepackage{colortbl} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{Sweave} \SweaveOpts{keep.source=TRUE} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\codeheading}[1]{\mbox{\color{darkblue}\texttt{#1}}} \newcommand{\code}[1]{\mbox{\footnotesize\color{darkblue}\texttt{#1}}} \newcommand{\pkg}[1]{{\fontseries{b}\selectfont #1}} \renewcommand{\pkg}[1]{{\textsf{#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \providecommand{\SetAlgoLined}{\SetLine} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0truein} \setlength{\parskip}{0.10truein} \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontshape=sl,formatcom=\color{darkblue}} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} \fvset{fontsize=\footnotesize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{The {\tt caret} Package} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{A Short Introduction to the \pkg{caret} Package} \author{Max Kuhn \\ max.kuhn@pfizer.com} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \thispagestyle{empty} \vspace{.2in} \renewcommand{\baselinestretch}{1} <>= library(caret) library(mlbench) data(Sonar) library(pROC) library(pls) options(useFancyQuotes = FALSE) getInfo <- function(what = "Suggests") { text <- packageDescription("caret")[what][[1]] text <- gsub("\n", ", ", text, fixed = TRUE) text <- gsub(">=", "$\\\\ge$", text, fixed = TRUE) eachPkg <- strsplit(text, ", ", fixed = TRUE)[[1]] eachPkg <- gsub(",", "", eachPkg, fixed = TRUE) #out <- paste("\\\\pkg{", eachPkg[order(tolower(eachPkg))], "}", sep = "") #paste(out, collapse = ", ") length(eachPkg) } @ The \pkg{caret} package (short for {\bf{\color{blue}{c}}}lassification {\bf{\color{blue}{a}}}nd {\bf{\color{blue}{re}}}gression {\bf{\color{blue}{t}}}raining) contains functions to streamline the model training process for complex regression and classification problems. The package utilizes a number of R packages but tries not to load them all at package start-up\footnote{By adding formal package dependencies, the package startup time can be greatly decreased}. The package ``suggests'' field includes \Sexpr{getInfo("Suggests")} packages. \pkg{caret} loads packages as needed and assumes that they are installed. Install \pkg{caret} using <>= install.packages("caret", dependencies = c("Depends", "Suggests")) @ to ensure that all the needed packages are installed. The {\bf main help pages} for the package are at: \begin{center} \url{http://caret.r-forge.r-project.org/} \end{center} Here, there are extended examples and a large amount of information that previously found in the package vignettes. \pkg{caret} has several functions that attempt to streamline the model building and evaluation process, as well as feature selection and other techniques. One of the primary tools in the package is the \code{train} function which can be used to \begin{itemize} \item evaluate, using resampling, the effect of model tuning parameters on performance \item choose the ``optimal'' model across these parameters \item estimate model performance from a training set \end{itemize} More formally: \begin{center} \includegraphics[clip, width = .9\textwidth]{train_algo} \end{center} There are options for customizing almost every step of this process (e.g. resampling technique, choosing the optimal parameters etc). To demonstrate this function, the Sonar data from the \pkg{mlbench} package will be used. The Sonar data consist of \Sexpr{nrow(Sonar)} data points collected on \Sexpr{ncol(Sonar)-1} predictors. The goal is to predict the two classes (\texttt{M} for metal cylinder or \texttt{R} for rock). First, we split the data into two groups: a training set and a test set. To do this, the \code{createDataPartition} function is used: <>= library(caret) library(mlbench) data(Sonar) set.seed(107) inTrain <- createDataPartition(y = Sonar$Class, ## the outcome data are needed p = .75, ## The percentage of data in the ## training set list = FALSE) ## The format of the results ## The output is a set of integers for the rows of Sonar ## that belong in the training set. str(inTrain) @ Bt default, \code{createDataPartition} does a stratified random split of the data. To partition the data: <>= training <- Sonar[ inTrain,] testing <- Sonar[-inTrain,] nrow(training) nrow(testing) @ To tune a model using Algorithm \ref{A:tune}, the \code{train} function can be used. More details on this function can be found at: \begin{center} \url{http://caret.r-forge.r-project.org/training.html} \end{center} Here, a partial least squares discriminant analysis (PLSDA) model will be tuned over the number of PLS components that should be retained. The most basic syntax to do this is: <>= plsFit <- train(Class ~ ., data = training, method = "pls", ## Center and scale the predictors for the training ## set and all future samples. preProc = c("center", "scale")) @ However, we would probably like to customize it in a few ways: \begin{itemize} \item expand the set of PLS models that the function evaluates. By default, the function will tune over three values of each tuning parameter. \item the type of resampling used. The simple bootstrap is used by default. We will have the function use three repeats of 10--fold cross--validation. \item the methods for measuring performance. If unspecified, overall accuracy and the Kappa statistic are computed. For regression models, root mean squared error and $R^2$ are computed. Here, the function will be altered to estimate the area under the ROC curve, the sensitivity and specificity \end{itemize} To change the candidate values of the tuning parameter, either of the \code{tuneLength} or \code{tuneGrid} arguments can be used. The \code{train} function can generate a candidate set of parameter values and the \code{tuneLength} argument controls how many are evaluated. In the case of PLS, the function uses a sequence of integers from 1 to \code{tuneLength}. If we want to evaluate all integers between 1 and 15, setting \code{tuneLength = 15} would achieve this. The \code{tuneGrid} argument is used when specific values are desired. A data frame is used where each row is a tuning parameter setting and each column is a tuning parameter. An example is used below to illustrate this. The syntax for the model would then be: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > plsFit <- train(Class ~ ., + data = training, + method = "pls", + \textcolor{red}{tuneLength = 15}, + preProc = c("center", "scale")) \end{Verbatim} To modify the resampling method, a \code{trainControl} function is used. The option \code{method} controls the type of resampling and defaults to \code{"boot"}. Another method, \code{"repeatedcv"}, is used to specify repeated $K$--fold cross--validation (and the argument \code{repeats} controls the number of repetitions). $K$ is controlled by the \code{number} argument and defaults to 10. The new syntax is then: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > \textcolor{red}{ctrl <- trainControl(method = "repeatedcv",} + \textcolor{red}{ repeats = 3)} > plsFit <- train(Class ~ ., + data = training, + method = "pls", + tuneLength = 15, + \textcolor{red}{trControl = ctrl}, + preProc = c("center", "scale")) \end{Verbatim} Finally, to choose different measures of performance, additional arguments are given to \code{trainControl}. The \code{summaryFunction} argument is used to pas in a function that takes the observed and predicted values and estimate some measure of performance. Two such functions are already included in the package: \code{defaultSummary} and \code{twoClassSummary}. The latter will compute measures specific to two--class problems, such as the area under the ROC curve, the sensitivity and specificity. Since the ROC curve is based on the predicted class probabilities (which are not computed automatically), another option is required. The \code{classProbs = TRUE} option is used to include these calculations. Lastly, the function will pick the tuning parameters associated with the best results. Since we are using custom performance measures, the criterion that should be optimized must also be specified. In the call to \code{train}, we can use \code{metric = "ROC"} to do this. The final model fit would then be: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > ctrl <- trainControl(method = "repeatedcv", + repeats = 3, + \textcolor{red}{ classProbs = TRUE}, + \textcolor{red}{ summaryFunction = twoClassSummary}) > plsFit <- train(Class ~ ., + data = training, + method = "pls", + tuneLength = 15, + trControl = ctrl, + \textcolor{red}{metric = "ROC"}, + preProc = c("center", "scale")) \end{Verbatim} <>= ctrl <- trainControl(method = "repeatedcv", repeats = 3, classProbs = TRUE, summaryFunction = twoClassSummary) set.seed(123) plsFit <- train(Class ~ ., data = training, method = "pls", tuneLength = 15, trControl = ctrl, metric = "ROC", preProc = c("center", "scale")) @ <>= plsFit @ In this output the grid of results are the average resampled estimates of performance. The note at the bottom tells the user that \Sexpr{plsFit$bestTune$.ncomp} PLS components were found to be optimal. Based on this value, a final PLS model is fit to the whole data set using this specification and this is the model that is used to predict future samples. The package has several functions for visualizing the results. One method for doing this is the \code{plot} function for \code{train} objects. The command \code{plot(plsFit)} produced the results seen in Figure \ref{F:pls} and shows the relationship between the resampled performance values and the number of PLS components. \setkeys{Gin}{width=.65\textwidth} \begin{figure} \begin{center} <>= trellis.par.set(caretTheme()) print(plot(plsFit)) @ \caption{ \code{plot(plsFit)} shows the relationship between the number of PLS components and the resampled estimate of the area under the ROC curve. } \label{F:pls} \end{center} \end{figure} To predict new samples, \code{predict.train} can be used. For classification models, the default behavior is to calculated the predicted class. Using the option \code{type = "prob"} can be used to compute class probabilities from the model. For example: <>= plsClasses <- predict(plsFit, newdata = testing) str(plsClasses) plsProbs <- predict(plsFit, newdata = testing, type = "prob") head(plsProbs) @ \pkg{caret} contains a function to compute the confusion matrix and associated statistics for the model fit: <>= confusionMatrix(data = plsClasses, testing$Class) @ To fit an another model to the data, \code{train} can be invoked with minimal changes. Lists of models available can be found at: \begin{center} \url{http://caret.r-forge.r-project.org/modelList.html} \url{http://caret.r-forge.r-project.org/bytag.html} \end{center} For example, to fit a regularized discriminant model to these data, the following syntax can be used: <>= ## To illustrate, a custom grid is used rdaGrid = data.frame(gamma = (0:4)/4, lambda = 3/4) set.seed(123) rdaFit <- train(Class ~ ., data = training, method = "rda", tuneGrid = rdaGrid, trControl = ctrl, metric = "ROC") rdaFit rdaClasses <- predict(rdaFit, newdata = testing) confusionMatrix(rdaClasses, testing$Class) @ How do these models compare in terms of their resampling results? The \code{resamples} function can be used to collect, summarize and contrast the resampling results. Since the random number seeds were initialized to the same value prior to calling \code{train}, the same folds were used for each model. To assemble them: <>= resamps <- resamples(list(pls = plsFit, rda = rdaFit)) summary(resamps) @ There are several functions to visualize these results. For example, a Bland--Altman type plot can be created using \code{xyplot(resamps, what = "BlandAltman")} (see Figure \ref{F:BA}). The results look similar. Since, for each resample, there are paired results a paired $t$--test can be used to assess whether there is a difference in the average resampled area under the ROC curve. The \code{diff.resamples} function can be used to compute this: <>= diffs <- diff(resamps) summary(diffs) @ Based on this analysis, the difference between the models is \Sexpr{round(diffs$statistics$ROC[[1]]$estimate, 3)} ROC units (the RDA model is slightly higher) and the two--sided $p$--value for this difference is \Sexpr{format.pval(diffs$statistics$ROC[[1]]$p.value)}. \setkeys{Gin}{width=.65\textwidth} \begin{figure} \begin{center} <>= plotTheme <- caretTheme() plotTheme$plot.symbol$col <- rgb(.2, .2, .2, .5) plotTheme$plot.symbol$pch <- 16 trellis.par.set(plotTheme) print(xyplot(resamps, what = "BlandAltman")) @ \caption{A Bland--Altman plot of the resampled ROC values produced using \code{xyplot(resamps, what = "BlandAltman")}. } \label{F:BA} \end{center} \end{figure} \end{document} caret/inst/doc/caret.R0000644000175000017500000001152112522120154014761 0ustar rbalintrbalint### R code from vignette source 'caret.Rnw' ################################################### ### code chunk number 1: loadLibs ################################################### library(caret) library(mlbench) data(Sonar) library(pROC) library(pls) options(useFancyQuotes = FALSE) getInfo <- function(what = "Suggests") { text <- packageDescription("caret")[what][[1]] text <- gsub("\n", ", ", text, fixed = TRUE) text <- gsub(">=", "$\\\\ge$", text, fixed = TRUE) eachPkg <- strsplit(text, ", ", fixed = TRUE)[[1]] eachPkg <- gsub(",", "", eachPkg, fixed = TRUE) #out <- paste("\\\\pkg{", eachPkg[order(tolower(eachPkg))], "}", sep = "") #paste(out, collapse = ", ") length(eachPkg) } ################################################### ### code chunk number 2: install (eval = FALSE) ################################################### ## install.packages("caret", dependencies = c("Depends", "Suggests")) ################################################### ### code chunk number 3: SonarSplit ################################################### library(caret) library(mlbench) data(Sonar) set.seed(107) inTrain <- createDataPartition(y = Sonar$Class, ## the outcome data are needed p = .75, ## The percentage of data in the ## training set list = FALSE) ## The format of the results ## The output is a set of integers for the rows of Sonar ## that belong in the training set. str(inTrain) ################################################### ### code chunk number 4: SonarDatasets ################################################### training <- Sonar[ inTrain,] testing <- Sonar[-inTrain,] nrow(training) nrow(testing) ################################################### ### code chunk number 5: plsTune1 (eval = FALSE) ################################################### ## plsFit <- train(Class ~ ., ## data = training, ## method = "pls", ## ## Center and scale the predictors for the training ## ## set and all future samples. ## preProc = c("center", "scale")) ################################################### ### code chunk number 6: plsFit ################################################### ctrl <- trainControl(method = "repeatedcv", repeats = 3, classProbs = TRUE, summaryFunction = twoClassSummary) set.seed(123) plsFit <- train(Class ~ ., data = training, method = "pls", tuneLength = 15, trControl = ctrl, metric = "ROC", preProc = c("center", "scale")) ################################################### ### code chunk number 7: plsPrint ################################################### plsFit ################################################### ### code chunk number 8: baPlot ################################################### trellis.par.set(caretTheme()) print(plot(plsFit)) ################################################### ### code chunk number 9: plsPred ################################################### plsClasses <- predict(plsFit, newdata = testing) str(plsClasses) plsProbs <- predict(plsFit, newdata = testing, type = "prob") head(plsProbs) ################################################### ### code chunk number 10: plsCM ################################################### confusionMatrix(data = plsClasses, testing$Class) ################################################### ### code chunk number 11: rdaFit ################################################### ## To illustrate, a custom grid is used rdaGrid = data.frame(gamma = (0:4)/4, lambda = 3/4) set.seed(123) rdaFit <- train(Class ~ ., data = training, method = "rda", tuneGrid = rdaGrid, trControl = ctrl, metric = "ROC") rdaFit rdaClasses <- predict(rdaFit, newdata = testing) confusionMatrix(rdaClasses, testing$Class) ################################################### ### code chunk number 12: rs ################################################### resamps <- resamples(list(pls = plsFit, rda = rdaFit)) summary(resamps) ################################################### ### code chunk number 13: diffs ################################################### diffs <- diff(resamps) summary(diffs) ################################################### ### code chunk number 14: plsPlot ################################################### plotTheme <- caretTheme() plotTheme$plot.symbol$col <- rgb(.2, .2, .2, .5) plotTheme$plot.symbol$pch <- 16 trellis.par.set(plotTheme) print(xyplot(resamps, what = "BlandAltman")) caret/build/0000755000175000017500000000000012522120154013115 5ustar rbalintrbalintcaret/build/vignette.rds0000644000175000017500000000034012522120154015451 0ustar rbalintrbalint‹mQÑ ‚0½¦Y Aàìµÿ!!zë¡×¡3%Ûd.¤·¾<»šF®÷n»çž³sÙÉ€˜&f 0=L6Æà Ü„J¦ü˜7°Ù’C.¤";®¤Ho‰*'J•3Ò“HD“ =³ÿŠUšiÀbx íx}y Ï{Àš}9½²Z XÅxÚ•Ÿš¼ùF«Q–{vo„9“{è±Â¢dã»ÇB}.f„Ãшu?úÿŽ?ΰêþã©Å¥š”´ÖuSª¨ŸIäw¾_þ¦œ¡Ñcaret/tests/0000755000175000017500000000000012453550065013173 5ustar rbalintrbalintcaret/tests/testthat/0000755000175000017500000000000012522435234015030 5ustar rbalintrbalintcaret/tests/testthat/trim_glm.R0000644000175000017500000000353112516214271016766 0ustar rbalintrbalintlibrary(caret) test_that('glm classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "glm", tuneLength=1, trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "glm", tuneLength=1, trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('glm regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "glm", tuneLength=1, trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "glm", tuneLength=1, trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/test_mnLogLoss.R0000644000175000017500000000175012520743335020134 0ustar rbalintrbalintcontext('mnLogLoss') eps <- 1e-15 classes <- LETTERS[1:3] test_dat1 <- data.frame(obs = c("A", "A", "A", "B", "B", "C"), pred = c("A", "A", "A", "B", "B", "C"), A = c(1, .80, .51, .1, .2, .3), B = c(0, .05, .29, .8, .6, .3), C = c(0, .15, .20, .1, .2, .4)) expected1 <- log(1-eps) + log(.8) + log(.51) + log(.8) + log(.6) + log(.4) expected1 <- c(logLoss = -expected1/nrow(test_dat1)) result1 <- mnLogLoss(test_dat1, lev = classes) test_dat2 <- test_dat1 test_dat2$A[1] <- NA expected2 <- log(.8) + log(.51) + log(.8) + log(.6) + log(.4) expected2 <- c(logLoss = -expected2/sum(complete.cases(test_dat2))) result2 <- mnLogLoss(test_dat2, lev = classes) test_dat3 <- test_dat1 test_dat3 <- test_dat3[, rev(1:5)] expected3 <- expected1 result3 <- mnLogLoss(test_dat3, lev = classes[c(2, 3, 1)]) expect_equal(result1, expected1) expect_equal(result2, expected2) expect_equal(result3, expected3) caret/tests/testthat/test_confusionMatrix.R0000644000175000017500000000314112516214271021400 0ustar rbalintrbalintcontext('Test confusionMatrix') set.seed(442) test_that("Confusion matrix works", { library(caret) train <- twoClassSim(n = 1000, intercept = -8, linearVars = 3, noiseVars = 10, corrVars = 4, corrValue = 0.6) ctrl <- trainControl(method = "cv", classProbs = TRUE) fullModel <- train(Class ~ ., data = train, method = "knn", preProc = c("center", "scale"), tuneLength = 4, trControl = ctrl) dat <- train$Class ref <- predict(fullModel) dat2 <- as.character(dat) ref2 <- as.character(ref) dat2 <- factor(dat2, levels = c("Class2", "Class1")) ref2 <- factor(ref2, levels = c("Class1", "Class2")) dat3 <- rep("Class1", length(ref2)) dat3 <- factor(dat3) dat4 <- factor(dat3, levels = c("Class1", "Class4")) dat5 <- as.character(dat3) dat5[200] <- "Class4" dat5 <- factor(dat5, levels = c("Class1", "Class4")) cm1 <- confusionMatrix(dat, ref) cm2 <- confusionMatrix(dat2, ref2) cm3 <- confusionMatrix(dat3, ref2) cm4 <- confusionMatrix(dat4, ref2) expect_true(class(cm1) == "confusionMatrix") expect_true(class(cm2) == "confusionMatrix") expect_true(class(cm3) == "confusionMatrix") expect_true(class(cm4) == "confusionMatrix") expect_warning(confusionMatrix(dat2, ref2)) expect_warning(confusionMatrix(dat3, ref2)) expect_error(confusionMatrix(dat5, ref2)) expect_error(confusionMatrix(dat5, ref1)) expect_identical(cm1$overall, cm2$overall) expect_identical(cm4$overall, cm3$overall) expect_true(identical(cm1, cm2)) expect_true(identical(cm3, cm4)) })caret/tests/testthat/trim_bayesglm.R0000644000175000017500000000356712516214271020023 0ustar rbalintrbalintlibrary(caret) test_that('bayesglm classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "bayesglm", tuneLength=1, trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "bayesglm", tuneLength=1, trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('bayesglm regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "bayesglm", tuneLength=1, trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "bayesglm", tuneLength=1, trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/trim_treebag.R0000644000175000017500000000356512516214271017627 0ustar rbalintrbalintlibrary(caret) test_that('treebag classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "treebag", nbagg = 3, trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "treebag", nbagg = 3, trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('rpart regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "treebag", nbagg = 3, trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "treebag", nbagg = 3, trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/trim_glmnet.R0000644000175000017500000000376312516214271017504 0ustar rbalintrbalintlibrary(caret) test_that('glmnet classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "glmnet", tuneGrid = data.frame(lambda = .1, alpha = .5), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "glmnet", tuneGrid = data.frame(lambda = .1, alpha = .5), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('glmnet regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "glmnet", tuneGrid = data.frame(lambda = .1, alpha = .5), trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "glmnet", tuneGrid = data.frame(lambda = .1, alpha = .5), trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/trim_C5.R0000644000175000017500000001266412516214271016465 0ustar rbalintrbalintlibrary(caret) test_that('single tree', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 1, model = "tree", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 1, model = "tree", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('single rule', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 1, model = "rules", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 1, model = "rules", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('boosted tree', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 5, model = "tree", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 5, model = "tree", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('boosted rule', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 5, model = "rules", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "C5.0", tuneGrid = data.frame(trials = 5, model = "rules", winnow = FALSE), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) caret/tests/testthat/test_minimal.R0000644000175000017500000000040212453550065017637 0ustar rbalintrbalintcontext('Minimal Tests') stats <- basic2x2Stats(factor(0:1), factor(0:1), pos='1', neg='0') expect_equal(stats[['Sensitivity']], 1) expect_equal(stats[['Specificity']], 1) expect_equal(stats[['Pos Pred Value']], 1) expect_equal(stats[['Neg Pred Value']], 1) caret/tests/testthat/preProcess.R0000644000175000017500000000210212453550065017276 0ustar rbalintrbalintcontext('Test preProcess') # Helper function check.medianImpute <- function(x) { # Reference column medians for checks med <- apply(x, 2, median, na.rm=TRUE) anyEmptyColumn <- any(is.na(med)) med[is.na(med)] <- 0 if(anyEmptyColumn) { expect_warning( pp <- preProcess(x, method = "medianImpute"), "never filled" ) } else { pp <- preProcess(x, method = "medianImpute") } expect_equal(pp$median, med) x.filled <- predict(pp, newdata=x) expect_false(any(is.na(x.filled))) expect_equal(x[!is.na(x)], x.filled[!is.na(x)]) med.filled <- apply(x.filled, 2, median) expect_equal(med.filled, med) } # Tested data matrix set.seed(1) x <- matrix(rnorm(20, mean=10,sd=5), nrow=4) x[2,1] <- x[3,4] <- x[2,5] <- x[4,5] <- NA x[,3] <- NA test_that("median Impute works for matrix with unnamed columns", { check.medianImpute(x[,3,drop=FALSE]) }) colnames(x) <- paste0("Var.",1:ncol(x)) test_that("median Impute works for matrix with named columns", { check.medianImpute(x) }) test_that("median Impute works for data.frames", { check.medianImpute(as.data.frame(x)) }) caret/tests/testthat/trim_rpart.R0000644000175000017500000000762312516214271017345 0ustar rbalintrbalintlibrary(caret) test_that('rpart classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.22), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.22), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('rpart regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.12), trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.12), trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) test_that('rpart2 classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "rpart2", tuneGrid = data.frame(maxdepth = 3), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "rpart2", tuneGrid = data.frame(maxdepth = 3), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('rpart2 regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "rpart2", tuneGrid = data.frame(maxdepth = 3), trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "rpart2", tuneGrid = data.frame(maxdepth = 3), trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/trim.R0000644000175000017500000000416412453550065016136 0ustar rbalintrbalintcontext('Test model object trimming') library(rpart) library(ipred) ################################################################### ## rpart tests functions check_rpart_reg <- function() { skip_on_cran() set.seed(1) train_dat <- SLC14_1(100) train_dat$factor_var <- factor(sample(letters[1:2], nrow(train_dat), replace = TRUE)) test_dat <- SLC14_1(1000) test_dat$factor_var <- factor(sample(letters[1:2], nrow(test_dat), replace = TRUE)) library(rpart) rpart_full <- rpart(y ~ ., data = train_dat) rpart_trim <- caret:::trim(rpart_full) predict(rpart_full, test_dat) - predict(rpart_trim, test_dat) } check_rpart_class <- function() { skip_on_cran() set.seed(1) train_dat <- twoClassSim(100) train_dat$factor_var <- factor(sample(letters[1:2], nrow(train_dat), replace = TRUE)) test_dat <- twoClassSim(1000) test_dat$factor_var <- factor(sample(letters[1:2], nrow(test_dat), replace = TRUE)) library(rpart) rpart_full <- rpart(Class ~ ., data = train_dat) rpart_trim <- caret:::trim(rpart_full) predict(rpart_full, test_dat)[, "Class1"] - predict(rpart_trim, test_dat)[, "Class1"] } ################################################################### ## bagging tests functions check_bag_reg <- function() { skip_on_cran() set.seed(1) train_dat <- SLC14_1(100) train_dat$factor_var <- factor(sample(letters[1:2], nrow(train_dat), replace = TRUE)) test_dat <- SLC14_1(1000) test_dat$factor_var <- factor(sample(letters[1:2], nrow(test_dat), replace = TRUE)) library(rpart) bag_full <- bagging(y ~ ., data = train_dat) bag_trim <- caret:::trim(bag_full) predict(bag_full, test_dat) - predict(bag_trim, test_dat) } ################################################################### ## Tests test_that("trimmed rpart regression produces identical predicted values", { expect_that(sum(check_rpart_reg()), equals(0)) }) test_that("trimmed rpart classification produces identical predicted values", { expect_that(sum(check_rpart_class()), equals(0)) }) test_that("trimmed bagging regression produces identical predicted values", { expect_that(sum(check_bag_reg()), equals(0)) }) caret/tests/testthat/trim_train.R0000644000175000017500000001030012516214271017314 0ustar rbalintrbalintlibrary(caret) test_that('train classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.22), preProc = c("center", "bagImpute"), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) class_trim <- caret:::trim.train(class_trim) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.22), preProc = c("center", "bagImpute"), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('train regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.12), trControl = trainControl(method = "none", trim = TRUE)) reg_trim <- caret:::trim.train(reg_trim) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "rpart", tuneGrid = data.frame(cp = 0.12), trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) test_that('train/earth classification', { skip_on_cran() set.seed(1) tr_dat <- twoClassSim(200) te_dat <- twoClassSim(200) set.seed(2) class_trim <- train(Class ~ ., data = tr_dat, method = "earth", tuneGrid = data.frame(nprune = 3, degree = 1), trControl = trainControl(method = "none", classProbs = TRUE, trim = TRUE)) class_trim <- caret:::trim.train(class_trim) set.seed(2) class_notrim <- train(Class ~ ., data = tr_dat, method = "earth", tuneGrid = data.frame(nprune = 3, degree = 1), trControl = trainControl(method = "none", classProbs = TRUE, trim = FALSE)) expect_equal(predict(class_trim, te_dat), predict(class_notrim, te_dat)) expect_equal(predict(class_trim, te_dat, type = "prob"), predict(class_notrim, te_dat, type = "prob")) expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) }) test_that('train/earth regression', { skip_on_cran() set.seed(1) tr_dat <- SLC14_1(200) te_dat <- SLC14_1(200) set.seed(2) reg_trim <- train(y ~ ., data = tr_dat, method = "earth", tuneGrid = data.frame(nprune = 3, degree = 1), trControl = trainControl(method = "none", trim = TRUE)) set.seed(2) reg_notrim <- train(y ~ ., data = tr_dat, method = "earth", tuneGrid = data.frame(nprune = 3, degree = 1), trControl = trainControl(method = "none", trim = FALSE)) expect_equal(predict(reg_trim, te_dat), predict(reg_notrim, te_dat)) expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0) }) caret/tests/testthat/test_models_bagEarth.R0000644000175000017500000000312712453550065021300 0ustar rbalintrbalint# This is an extremely basic test that would catch serious kinds of errors # such as the bagEarth() not returning the right kind of object, that one of # the functions (bagEarth, format, predict) crash during normal usage, or that # bagEarth cannot model a simplistic kind of linear equation. test_that('bagEarth simple regression', { skip_on_cran() data <- data.frame(X = 1:100) data$Y <- data$X * 2 data$training <- data$X %% 2 fit <- bagEarth(Y ~ X, data=data[1==data$training,], B=3) expect_that(format(fit, cat=FALSE), is_a('character')) expect_that(fit, is_a('bagEarth')) data$pred <- predict(fit, newdata=data) data$resid <- with(data, Y - pred) mae <- mean(abs(data$resid)) expect_equal(mae, 0) }) test_that('bagEarth simple classification', { skip_on_cran() data <- twoClassSim(n=1000) fit <- bagEarth(Class ~ ., data=data, B=3, glm=list(family=binomial)) expect_that(format(fit, cat=FALSE), is_a('character')) expect_that(fit, is_a('bagEarth')) pred_response <- predict(fit, newdata=data) expect_is(pred_response, "numeric") expect_equal(length(pred_response), 1000) expect_true(0 <= min(pred_response)) expect_true(max(pred_response) <= 1) pred_class <- predict(fit, newdata=data, type="class") expect_is(pred_class, "factor") expect_equal(length(pred_class), 1000) pred_prob <- predict(fit, newdata=data, type="prob") expect_is(pred_prob, "matrix") expect_equal(ncol(pred_prob), 2) expect_equal(nrow(pred_prob), 1000) expect_true(0 <= min(pred_prob)) expect_true(max(pred_prob) <= 1) }) caret/tests/testthat.R0000644000175000017500000000006612453550065015160 0ustar rbalintrbalintlibrary(testthat) library(caret) test_check("caret") caret/MD50000644000175000017500000002621312522435234012342 0ustar rbalintrbalintd561bdf5328b0c51b1c20c684bdb3f20 *DESCRIPTION a89d6f10b358a5b4dbb3079da77af15d *NAMESPACE f9cfbda572ebf462c54e6bb99b426dc6 *R/BoxCoxTrans.R 22c5f2728af7e42bc4c7272873d8339b *R/aaa.R b050ca28a1e6246c2c43f335a6f8e20d *R/adaptive.R 0217ce3d44c3f71206343ac5a5eea914 *R/additive.R 07d8f468c708ec80ae4b60670b3ba527 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function (x, ...) UseMethod("plsda") predict.plsda <- function(object, newdata = NULL, ncomp = NULL, type = "class", ...){ requireNamespaceQuietStop('pls') if(is.null(ncomp)) if(!is.null(object$ncomp)) ncomp <- object$ncomp else stop("specify ncomp") if(!is.null(newdata)) { if(!is.matrix(newdata)) newdata <- as.matrix(newdata) } ## make sure that the prediction function from pls is used class(object) <- "mvr" tmpPred <- predict(object, newdata = newdata)[,,ncomp,drop = FALSE] if(type == "raw") return(tmpPred) if(is.null(object$probModel)) { ## use softmax switch(type, class = { if(length(dim(tmpPred)) < 3) { ##only requested one component out <- object$obsLevels[apply(tmpPred, 1, which.max)] out <- factor(out, levels = object$obsLevels) } else { ## more than one component tmpOut <- matrix("", nrow = dim(tmpPred)[1], ncol = dim(tmpPred)[3]) for(i in 1:dim(tmpPred)[3]) { tmpOut[,i] <- object$obsLevels[apply(tmpPred[,,i,drop=FALSE], 1, which.max)] } out <- as.data.frame(tmpOut) out <- as.data.frame(lapply(out, function(x, y) factor(x, levels = y), y = object$obsLevels)) names(out) <- paste("ncomp", ncomp, sep = "") rownames(out) <- rownames(newdata) if(length(ncomp) == 1) out <- out[,1] } }, prob = { ## fix prob names if(length(dim(tmpPred)) < 3) { out <- t(apply(tmpPred, 1, function(data) exp(data)/sum(exp(data)))) } else { ## more than one component out <- tmpPred * NA for(i in 1:dim(tmpPred)[3]) { out[,,i] <- t(apply(tmpPred[,,i,drop=FALSE], 1, function(data) exp(data)/sum(exp(data)))) } } }) } else { ## Bayes rule loadNamespace("klaR") tmp <- vector(mode = "list", length = length(ncomp)) for(i in seq(along = ncomp)) { tmp[[i]] <- predict(object$probModel[[ ncomp[i] ]], as.data.frame(tmpPred[,-length(object$obsLevels),i])) } if(type == "class") { out <- t(do.call("rbind", lapply(tmp, function(x) as.character(x$class)))) rownames(out) <- names(tmp[[1]]$class) colnames(out) <- paste("ncomp", ncomp, sep = "") out <- as.data.frame(out) out <- as.data.frame(lapply(out, function(x, y) factor(x, levels = y), y = object$obsLevels)) if(length(ncomp) == 1) out <- out[,1] } else { out <- array(dim = c(dim(tmp[[1]]$posterior), length(ncomp)), dimnames = list( rownames(tmp[[1]]$posterior), colnames(tmp[[1]]$posterior), paste("ncomp", ncomp, sep = ""))) for(i in seq(along = ncomp)) out[,,i] <- tmp[[i]]$posterior } } out } plsda.default <- function(x, y, ncomp = 2, probMethod = "softmax", prior = NULL, ...) { requireNamespaceQuietStop('pls') funcCall <- match.call(expand.dots = TRUE) if(!is.matrix(x)) x <- as.matrix(x) if(length(ncomp) > 1) { ncomp <- max(ncomp) warning(paste( "A value single ncomp must be specified.", "max(ncomp) was used.", "Predictions can be obtained for values <= ncomp")) } if(probMethod == "softmax") { if(!is.null(prior)) warning("Priors are ignored unless probMethod = \"Bayes\"") } if(is.factor(y)) { obsLevels <- levels(y) oldY <- y y <- class2ind(y) } else { if(is.matrix(y)) { test <- apply(y, 1, sum) if(any(test != 1)) stop("the rows of y must be 0/1 and sum to 1") obsLevels <- colnames(y) if(is.null(obsLevels)) stop("the y matrix must have column names") oldY <- obsLevels[apply(y, 1, which.max)] } else stop("y must be a matrix or a factor") } if(!is.matrix(x)) x <- as.matrix(x) tmpData <- data.frame(n = paste("row", 1:nrow(y), sep = "")) tmpData$y <- y tmpData$x <- x out <- pls::plsr(y ~ x, data = tmpData, ncomp = ncomp, ...) out$obsLevels <- obsLevels out$probMethod <- probMethod if(probMethod == "Bayes") { requireNamespaceQuietStop('klaR') makeModels <- function(x, y, pri) { probModel <- klaR::NaiveBayes(x, y, prior = pri, usekernel = TRUE) probModel$train <- predict(probModel)$posterior probModel$x <- NULL probModel } cls <- class(out) class(out) <- "mvr" train <- predict(out, as.matrix(tmpData$x), ncomp = 1:ncomp) ## Get the raw model predictions, but leave one behind since the ## final class probs sum to one train <- train[, -length(obsLevels),, drop = FALSE] out$probModel <- apply(train, 3, makeModels, y = oldY, pri = prior) } else out$probModel <- NULL ##out$call <- funcCall class(out) <- c("plsda", class(out)) out } print.plsda <- function (x, ...) { ## minor change to print.mvr switch(x$method, kernelpls = { regr = "Partial least squares" alg = "kernel" }, simpls = { regr = "Partial least squares" alg = "simpls" }, oscorespls = { regr = "Partial least squares" alg = "orthogonal scores" }, svdpc = { regr = "Principal component" alg = "singular value decomposition" }, stop("Unknown fit method.")) cat(regr, "classification, fitted with the", alg, "algorithm.") if (!is.null(x$validation)) cat("\nCross-validated using", length(x$validation$segments), attr(x$validation$segments, "type"), "segments.") switch(x$probMethod, softmax = cat("\nThe softmax function was used to compute class probabilities.\n"), Bayes = cat("\nBayes rule was used to compute class probabilities.\n")) invisible(x) } caret/R/BoxCoxTrans.R0000644000175000017500000000444212453550064014572 0ustar rbalintrbalintBoxCoxTrans <- function(y, ...) UseMethod("BoxCoxTrans") ##TODO add an epsilon? ## TODO add exclusion list to preProc? BoxCoxTrans.default <- function(y, x = rep(1, length(y)), fudge = .2, numUnique = 3, na.rm = FALSE, ...) { requireNamespaceQuietStop("MASS") requireNamespaceQuietStop("e1071") if(na.rm && (any(is.na(y)) | any(is.na(x)))) { rmv <- is.na(y) | is.na(x) y <- y[!rmv] x <- x[!rmv] } if(!is.numeric(y) | is.factor(y) | is.character(y)) stop("y must be numeric") if(any(y <= 0) | length(unique(y)) < numUnique) { out <- list(lambda = NA, summary = summary(y), ratio = NA, n = length(y)) } else { bc <- MASS::boxcox(y~x, plotit = FALSE, ...) out <- list(lambda = bc$x[which.max(bc$y)]) } out$fudge <- fudge out$n <- length(y) out$summary <- summary(y) out$ratio <- max(y)/min(y) out$skewness <- e1071::skewness(y) class(out) <- "BoxCoxTrans" out } print.BoxCoxTrans <- function(x, newdata, digits = 3, ...){ cat("Box-Cox Transformation\n\n") cat(x$n, "data points used to estimate Lambda\n\n") cat("Input data summary:\n") print(x$summary) if(!is.na(x$lambda)) { cat("\nLargest/Smallest:", signif(x$ratio, digits), "\n") cat("Sample Skewness:", signif(x$skewness, digits), "\n") cat("\nEstimated Lambda:", signif(x$lambda, digits), "\n") if(x$lambda < x$fudge & x$lambda > -x$fudge) cat("With fudge factor, Lambda = 0 will be used for transformations\n") if(x$lambda < 1+x$fudge & x$lambda > 1-x$fudge) cat("With fudge factor, no transformation is applied\n") } else cat("\nLambda could not be estimated; no transformation is applied\n") cat("\n") invisible(x) } predict.BoxCoxTrans <- function(object, newdata, ...) { if(!is.vector(newdata) || !is.numeric(newdata)) stop("newdata should be a numeric vector") if(is.na(object$lambda)) { out <- newdata } else { if(object$lambda < object$fudge & object$lambda > -object$fudge) { if(any(newdata[!is.na(newdata)] <= 0)) warning("newdata should have values > 0") out <- log(newdata) } else { if(object$lambda < 1+object$fudge & object$lambda > 1-object$fudge) { out <- newdata } else out <- (newdata^object$lambda - 1)/object$lambda } } out } caret/R/resampleHist.R0000644000175000017500000000236412453550064015021 0ustar rbalintrbalint resampleHist <- function(object, type = "density", ...) { if(object$control$method == "oob") stop("out-of-bag error rate was selected. This plot cannot be created") if(is.null(object$resample)) stop("No resample values were found. This plot cannot be created") resample <- object$resample tuneNames <- as.character(object$modelInfo$parameter$parameter) if(any(names(resample) %in% tuneNames)) { bestTune <- object$bestTune colnames(bestTune) <- gsub("^\\.", "", colnames(bestTune)) resample <- merge(bestTune, resample) resample <- resample[, !(names(resample) %in% tuneNames), drop = FALSE] } results <- melt(resample, id.vars = "Resample") if(type == "density") { out <- densityplot(~ value|variable, data = results, scales = list(relation = "free"), xlab = "", as.table = TRUE, ...) } else { out <- histogram(~ value|variable, data = results, scales = list(relation = "free"), as.table = TRUE, xlab = "", ...) } out } caret/R/basic2x2Stats.R0000644000175000017500000000051612453550064015012 0ustar rbalintrbalintbasic2x2Stats <- function(x, y, pos, neg) { out <- vector(length = 4, mode = "numeric") out[1] <- sensitivity(x, y, pos) out[2] <- specificity(x, y, neg) out[3] <- posPredValue(x, y, pos) out[4] <- negPredValue(x, y, neg) names(out) <- c("Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value") out } caret/R/additive.R0000644000175000017500000000322712453550064014151 0ustar rbalintrbalint additivePlot <- function(x, data, n = 100, quant = 0, plot = TRUE, ...) { if(any(class(x) == "earth")) { data <- data[, predictors(x), drop = FALSE] } seqs <- lapply(data, function(x, len, q) list(seq = seq( quantile(x, na.rm = TRUE, probs = q), quantile(x, na.rm = TRUE, probs = 1 - q), length = len), var = ""), len = n, q = quant) for(i in seq(along = seqs)) seqs[[i]]$var <- colnames(data)[i] meds <- lapply(data, function(x, len) rep(median(x, na.rm = TRUE), len), len = n) meds <- as.data.frame(meds) predGrid <- lapply(seqs, function(x, m) { m[, x$var] <- x$seq m$variable <- x$var m }, m = meds) predGrid <- do.call("rbind", predGrid) predGrid$predicted <- predict(x, predGrid[, colnames(data), drop = FALSE], ...) predGrid$x <- unlist(lapply(seqs, function(x) x$seq)) if(plot) { out <- xyplot(predicted ~ x|variable, data = predGrid, between = list(x = 2), scales = list(x = list(relation = "free")), as.table = TRUE, xlab = "", ylab = "Predicted", type = "l") } else out <- predGrid out } caret/R/print.train.R0000644000175000017500000002035112453550064014625 0ustar rbalintrbalintstringFunc <- function (x) { if (!is.character(x)) x <- format(x) numElements <- length(x) out <- if (length(x) > 0) { switch(min(numElements, 3), x, paste(x, collapse = " and "), { x <- paste0(x, c(rep(",", numElements - 2), " and", "")) paste(x, collapse = " ") }) } else "" out } "print.train" <- function(x, printCall = FALSE, details = FALSE, selectCol = FALSE, ...) { if(!is.null(x$modelInfo$label)) cat(x$modelInfo$label, "\n\n") if(printCall) printCall(x$call) if(!is.null(x$trainingData)) { chDim <- dim(x$trainingData) chDim[2] <- chDim[2] - 1 if(x$modelType == "Classification") { lev <- levels(x) if(is.character(lev)) chDim <- c(chDim, length(lev)) } else lev <- NULL chDim <- format(chDim) cat(chDim[1], " samples\n", chDim[2], " predictor", ifelse(chDim[2] > 1, "s\n", "\n"), sep = "") if(is.character(lev)){ cat(chDim[3], "classes:", paste("'", lev, "'", sep = "", collapse = ", "), "\n") } cat("\n") } if(!is.null(x$preProc)){ ## Make things look a little nicer: pp <- x$preProc$method pp <- gsub("BoxCox", "Box-Cox transformation", pp) pp <- gsub("YeoJohnson", "Yeo-Johnson transformation", pp) pp <- gsub("expoTrans", "exponential transformation", pp) pp <- gsub("scale", "scaled", pp) pp <- gsub("center", "centered", pp) pp <- gsub("pca", "principal component signal extraction", pp) pp <- gsub("ica", "independent component signal extraction", pp) pp <- gsub("spatialSign", "spatial sign transformation", pp) pp <- gsub("knnImpute", paste(x$k, "nearest neighbor imputation"), pp) pp <- gsub("bagImpute", "bagged tree imputation", pp) pp <- gsub("medianImpute", "median imputation", pp) pp <- gsub("range", "re-scaling to [0, 1]", pp) if(length(pp) == 0) pp <- "None" ppText <- paste("Pre-processing:", paste(pp, collapse = ", ")) cat(truncateText(ppText), "\n") } else cat("No pre-processing\n") if(!is.null(x$control$index)) { resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(x) cat("Resampling:", resampText, "\n\n") if(x$control$method != "none") { outLabel <- x$metric resampleN <- as.character(resampleN) if(numResamp > 5) resampleN <- c(resampleN[1:6], "...") cat("Summary of sample sizes:", paste(resampleN, collapse = ", "), "\n\n") } } if(x$control$method != "none") { tuneAcc <- x$results tuneAcc <- tuneAcc[, names(tuneAcc) != "parameter"] cat("Resampling results") if(dim(tuneAcc)[1] > 1) cat(" across tuning parameters:\n") else cat("\n") cat("\n") if(dim(tuneAcc)[1] > 1) { numParam <- length(x$bestTune) finalTune <- x$bestTune optValues <- paste(names(finalTune), "=", format(finalTune, ...)) optString <- paste0("The final ", ifelse(numParam > 1, "values", "value"), " used for the model ", ifelse(numParam > 1, "were ", "was "), stringFunc(optValues), ".") finalTune$Selected <- "*" tuneAcc <- merge(tuneAcc, finalTune, all.x = TRUE) tuneAcc$Selected[is.na(tuneAcc$Selected)] <- "" } else optString <- "" sdCols <- grep("SD$", colnames(tuneAcc)) sdCheck <- unlist(lapply(tuneAcc[, sdCols, drop = FALSE], function(u) all(is.na(u)))) if(any(sdCheck)) { rmCols <- names(sdCheck)[sdCheck] tuneAcc <- tuneAcc[, !(names(tuneAcc) %in% rmCols)] } params <- names(x$bestTune) if(!all(params == "parameter")){ numVals <- apply(tuneAcc[, params, drop = FALSE], 2, function(x) length(unique(x))) if(any(numVals < 2)) { constString <- NULL for(i in seq(along = numVals)) { if(numVals[i] == 1) constString <- c(constString, paste0("Tuning parameter '", names(numVals)[i], "' was held constant at a value of ", stringFunc(tuneAcc[1,names(numVals)[i]]))) } discard <- names(numVals)[which(numVals == 1)] tuneAcc <- tuneAcc[, !(names(tuneAcc) %in% discard), drop = FALSE] } else constString <- NULL } else constString <- NULL tuneAcc <- tuneAcc[,!grepl("Apparent$", names(tuneAcc)),drop = FALSE] theDots <- list(...) theDots$x <- tuneAcc # if(!(any(names(theDots) == "digits"))) theDots$digits <- min(3, getOption("digits")) printMat <- do.call("format.data.frame", theDots) printMat <- as.matrix(printMat) rownames(printMat) <- rep("", dim(printMat)[1]) colnames(printMat) <- gsub("SD", " SD", colnames(printMat)) if(!selectCol) printMat <- printMat[, colnames(printMat) != "Selected", drop = FALSE] print(printMat, quote = FALSE, print.gap = 2) cat("\n") if(!is.null(constString)){ cat(truncateText(paste(constString, collapse = "\n"))) cat("\n") } if(dim(tuneAcc)[1] > 1) { if(is.null(x$update)) { met <- paste(x$metric, "was used to select the optimal model using") if(is.function(x$control$selectionFunction)) { met <- paste(met, " a custom selection rule.\n") } else { met <- paste(met, switch(x$control$selectionFunction, best = paste( " the", ifelse(x$maximize, "largest", "smallest"), "value.\n"), oneSE = " the one SE rule.\n", tolerance = " a tolerance rule.\n")) } } else { met <- paste("The tuning", ifelse(ncol(x$bestTune) > 1, "parameters", "parameter"), "was set manually.\n") } cat(truncateText(met)) } cat(truncateText(optString), "\n") } else printMat <- NULL if(details) { if(!(x$method %in% c("gbm", "treebag", "nb", "lvq", "knn"))) { cat("\n----------------------------------------------------------\n") cat("\nThe final model:\n\n") switch(x$method, lm =, nnet =, multinom =, pls =, earth =, lmStepAIC =, bagEarth =, bagFDA = print(summary(x$finalModel)), rpart =, ctree =, ctree2=, cforest =, glmboost =, gamboost =, blackboost =, ada =, randomForest =, pcaNNet =, svmradial =, svmpoly =, svmRadial =, svmPoly =, rvmRadial =, rvmPoly =, lssvmRadial =, lssvmPoly =, gaussprRadial =, gaussprPoly =, enet =, lasso =, LMT =, JRip =, lda =, rda =, pamr =, gpls =, J48 =, ppr = print(x$finalModel), fda = { print(x$finalModel) cat("\n Summary of Terms\n\n") print(x$finalModel$fit) }) } } invisible(printMat) } truncateText <- function(x){ if(length(x) > 1) x <- paste(x, collapse = "") w <- options("width")$width if(nchar(x) <= w) return(x) cont <- TRUE out <- x while(cont){ tmp <- out[length(out)] tmp2 <- substring(tmp, 1, w) spaceIndex <- gregexpr("[[:space:]]", tmp2)[[1]] stopIndex <- spaceIndex[length(spaceIndex) - 1] - 1 tmp <- c(substring(tmp2, 1, stopIndex), substring(tmp, stopIndex + 1)) out <- if(length(out) == 1) tmp else c(out[1:(length(x)-1)], tmp) if(all(nchar(out) <= w)) cont <- FALSE } paste(out, collapse = "\n") } caret/R/extractPrediction.R0000644000175000017500000001350012453550064016046 0ustar rbalintrbalint extractPrediction <- function(models, testX = NULL, testY = NULL, unkX = NULL, unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE) { objectNames <- names(models) if(is.null(objectNames)) objectNames <- paste("Object", 1:length(models), sep = "") if(!unkOnly) { trainX <- models[[1]]$trainingData[,!(colnames(models[[1]]$trainingData) %in% ".outcome"), drop = FALSE] trainY <- models[[1]]$trainingData$.outcome } obsLevels <- levels(models[[1]]) if(verbose) { cat("Number of training samples:", length(trainY), "\n") cat("Number of test samples: ", length(testY), "\n\n") } pred <- obs <- modelName <- dataType <- objName <- NULL if(!is.null(testX)) { #if(!is.data.frame(testX)) testX <- as.data.frame(testX) hasNa <- apply(testX, 1, function(data) any(is.na(data))) if(verbose) cat("There were ", sum(hasNa), "rows with missing values\n\n") } for(i in seq(along = models)) { if(!unkOnly) { tempTrainPred <- predictionFunction(models[[i]]$modelInfo, models[[i]]$finalModel, trainX, models[[i]]$preProcess) if(verbose) cat(models[[i]]$method, ":", length(tempTrainPred), "training predictions were added\n") if(models[[i]]$modelType == "Classification") { pred <- c(pred, as.character(tempTrainPred)) obs <- c(obs, as.character(trainY)) } else { tempTrainPred <- trimPredictions(mod_type = models[[i]]$modelType, bounds = models[[i]]$control$predictionBounds, limits = models[[i]]$yLimit, pred = tempTrainPred) pred <- c(pred, tempTrainPred) obs <- c(obs, trainY) } modelName <- c(modelName, rep(models[[i]]$method, length(tempTrainPred))) objName <- c(objName, rep(objectNames[[i]], length(tempTrainPred))) dataType <- c(dataType, rep("Training", length(tempTrainPred))) if(!is.null(testX) & !is.null(testY)) { if(any(colnames(testX) == ".outcome")) testX <- testX[, colnames(testX) != ".outcome", drop = FALSE] tempTestPred <- predictionFunction(models[[i]]$modelInfo, models[[i]]$finalModel, testX, models[[i]]$preProcess) if(verbose) cat(models[[i]]$method, ":", length(tempTestPred), "test predictions were added\n") if(models[[i]]$modelType == "Classification") { pred <- c(pred, as.character(tempTestPred)) obs <- c(obs, as.character(testY)) } else { tempTestPred <- trimPredictions(mod_type = models[[i]]$modelType, bounds = models[[i]]$control$predictionBounds, limits = models[[i]]$yLimit, pred = tempTestPred) pred <- c(pred, tempTestPred) obs <- c(obs, testY) } modelName <- c(modelName, rep(models[[i]]$method, length(tempTestPred))) objName <- c(objName, rep(objectNames[[i]], length(tempTestPred))) dataType <- c(dataType, rep("Test", length(tempTestPred))) } if(verbose) cat("\n") } if(!is.null(unkX)) { if(any(colnames(unkX) == ".outcome")) unkX <- unkX[, colnames(unkX) != ".outcome", drop = FALSE] tempUnkPred <- predictionFunction(models[[i]]$modelInfo, models[[i]]$finalModel, unkX, models[[i]]$preProcess) if(verbose) cat(models[[i]]$method, ":", length(tempUnkPred), "unknown predictions were added\n") if(models[[i]]$modelType == "Classification") { pred <- c(pred, as.character(tempUnkPred)) obs <- c(obs, rep("", length(tempUnkPred))) } else { tempUnkPred <- trimPredictions(mod_type = models[[i]]$modelType, bounds = models[[i]]$control$predictionBounds, limits = models[[i]]$yLimit, pred = tempUnkPred) pred <- c(pred, tempUnkPred) obs <- c(obs, rep(NA, length(tempUnkPred))) } modelName <- c(modelName, rep(models[[i]]$method, length(tempUnkPred))) objName <- c(objName, rep(objectNames[[i]], length(tempUnkPred))) dataType <- c(dataType, rep("Unknown", length(tempUnkPred))) } if(verbose) cat("\n") } if(models[[1]]$modelType == "Classification") { pred <- factor(pred, levels = obsLevels) obs <- factor(obs, levels = obsLevels) } data.frame(obs = obs, pred = pred, model = modelName, dataType = dataType, object = objName) } trimPredictions <- function(pred, mod_type, bounds, limits) { if(mod_type == "Regression" && is.logical(bounds) && any(bounds)) { if(bounds[1]) pred <- ifelse(pred < limits[1], limits[1], pred) if(bounds[2]) pred <- ifelse(pred > limits[2], limits[2], pred) } if(mod_type == "Regression" && is.numeric(bounds) && any(!is.na(bounds))) { if(!is.na(bounds[1])) pred <- ifelse(pred < bounds[1], bounds[1], pred) if(!is.na(bounds[2])) pred <- ifelse(pred > bounds[2], bounds[2], pred) } pred } caret/R/resampleSummary.R0000644000175000017500000000454712453550064015554 0ustar rbalintrbalintresampleSummary <- function(obs, resampled, index = NULL, keepData = TRUE) { numPred <- apply(resampled, 2, function(u) sum(!is.na(u))) # for everything but LOO, we should get multiple predictions per resample if(all(numPred >= 2)) { # calculate performance metrics for each resample performanceStats <- apply( resampled, 2, postResample, obs = obs) #summarize resample dists out <- c( apply(performanceStats, 1, mean, na.rm = TRUE), apply(performanceStats, 1, sd, na.rm = TRUE)) # optionally returen the data in "vertical" format # to conserve space remove the many missing values if(keepData) { # stack has issues when there are a lot of missing values, # so we'll use lapply to stack the columns of resampled if(is.factor(obs)) { outResample <- data.frame( obs = rep(obs, dim(resampled)[2]), pred = factor(unlist(lapply(resampled, as.character)), levels = levels(obs)), group = paste( "Resample", rep( 1:dim(resampled)[2], each = dim(resampled)[1], sep = ""))) } else { outResample <- data.frame( obs = rep(obs, dim(resampled)[2]), pred = unlist(lapply(resampled, I)), group = paste( "Resample", rep( 1:dim(resampled)[2], each = dim(resampled)[1], sep = ""))) } } else outResample <- NULL } else { pred <- apply(resampled, 2, function(u) u[!is.na(u)]) if(is.factor(obs)) pred <- factor(as.character(pred), levels = levels(obs)) tmp <- postResample(pred, obs) tmp2 <- tmp * 0 out <- c( tmp, tmp * 0) outResample <- data.frame( obs = obs, pred = pred, group = "Resample1") } if(keepData) outResample <- outResample[!is.na(outResample$pred),] list(metrics = out, data = outResample) } caret/R/print.varImp.train.R0000644000175000017500000000120612516214271016055 0ustar rbalintrbalint"print.varImp.train" <- function(x, top = min(20, dim(x$importance)[1]), digits = max(3, getOption("digits") - 3), ...) { cat(x$model, "variable importance\n\n") printObj <- sortImp(x, top) if(dim(x$importance)[2] > 2) cat(" variables are sorted by maximum importance across the classes\n") if(top < dim(x$importance)[1]) cat(" only ", top, " most important variables shown (out of ", dim(x$importance)[1], ")\n\n", sep = "") if(dim(printObj)[2] == 2) { printObj <- printObj[,1,drop = FALSE] names(printObj) <- "Importance" } print(printObj, digits = digits, ...) invisible(x) } caret/R/classLevels.R0000644000175000017500000000113012453550064014627 0ustar rbalintrbalint levels.train <- function(x, ...) { if(x$modelType == "Classification") { if(!isS4(x$finalModel) && !is.null(x$finalModel$obsLevels)) return(x$finalModel$obsLevels) if(is.null(x$modelInfo)) { code <- getModelInfo(x$method, regex = FALSE)[[1]] } else code <- x$modelInfo if(!is.null(code$levels)){ checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) out <- code$levels(x$finalModel, ...) } else out <- NULL } else out <- NULL out } caret/R/reg_sims.R0000644000175000017500000001012712516214271014162 0ustar rbalintrbalint make_noise <- function(n, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0, binary = FALSE) { requireNamespaceQuietStop("MASS") if(noiseVars > 0) { tmpData <- matrix(rnorm(n * noiseVars), ncol = noiseVars) colnames(tmpData) <- well_numbered("Noise", noiseVars) } if(corrVars > 0) { loadNamespace("MASS") if(corrType == "exch") { vc <- matrix(corrValue, ncol = corrVars, nrow = corrVars) diag(vc) <- 1 } if(corrType == "AR1") { vcValues <- corrValue^(seq(0, corrVars - 1, by = 1)) vc <- toeplitz(vcValues) } tmpData2 <- MASS::mvrnorm(n, mu = rep(0, corrVars), Sigma = vc) colnames(tmpData2) <- well_numbered("Corr", corrVars) } if(noiseVars == 0 & corrVars > 0) out <- tmpData2 if(noiseVars > 0 & corrVars == 0) out <- tmpData if(noiseVars > 0 & corrVars > 0) out <- cbind(tmpData, tmpData2) if(binary) out <- ifelse(out > 0, 1, 0) as.data.frame(out) } SLC14_1 <- function(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) { dat <- matrix(rnorm(n*20, sd = 3), ncol = 20) foo <- function(x) x[1] + sin(x[2]) + log(abs(x[3])) + x[4]^2 + x[5]*x[6] + ifelse(x[7]*x[8]*x[9] < 0, 1, 0) + ifelse(x[10] > 0, 1, 0) + x[11]*ifelse(x[11] > 0, 1, 0) + sqrt(abs(x[12])) + cos(x[13]) + 2*x[14] + abs(x[15]) + ifelse(x[16] < -1, 1, 0) + x[17]*ifelse(x[17] < -1, 1, 0) - 2 * x[18] - x[19]*x[20] dat <- as.data.frame(dat) colnames(dat) <- well_numbered("Var", ncol(dat)) if(noiseVars > 0 | corrVars > 0) dat <- cbind(dat, make_noise(n = n, noiseVars = noiseVars, corrVars = corrVars, corrType = corrType, corrValue = corrValue)) dat$y <- apply(dat[, 1:20], 1, foo) + rnorm(n, sd = 3) dat } SLC14_2 <- function(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) { dat <- matrix(rnorm(n*200, sd = 4), ncol = 200) dat <- as.data.frame(dat) colnames(dat) <- well_numbered("Var", ncol(dat)) if(noiseVars > 0 | corrVars > 0) dat <- cbind(dat, make_noise(n = n, noiseVars = noiseVars, corrVars = corrVars, corrType = corrType, corrValue = corrValue)) dat$y <- apply(dat[, 1:200], 1, function(x) sum(log(abs(x)))) + rnorm(n, sd = 5) - 1 dat } LPH07_1 <- function(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) { dat <- matrix(rbinom(n*10, size = 1, prob = .4), ncol = 10) dat <- as.data.frame(dat) colnames(dat) <- well_numbered("Var", ncol(dat)) foo <- function(w) 2*w[1]*w[10] + 4*w[2]*w[7] + 3*w[4]*w[5] - 5*w[6]*w[10] + 3*w[8]*w[9] + w[1]*w[2]*w[4] - 2*w[7]*(1-w[6])*w[2]*w[9] - 4*(1 - w[10])*w[1]*(1-w[4]) if(noiseVars > 0 | corrVars > 0) dat <- cbind(dat, make_noise(n = n, noiseVars = noiseVars, corrVars = corrVars, corrType = corrType, corrValue = corrValue, binary = TRUE)) dat$y <- apply(dat[, 1:10], 1, foo) + rnorm(n) dat } LPH07_2 <- function(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0) { dat <- matrix(rnorm(n*20, sd = 4), ncol = 20) dat <- as.data.frame(dat) colnames(dat) <- well_numbered("Var", ncol(dat)) foo <- function(x) x[1]*x[2] + x[10]^2 - x[3]*x[17] - x[15]*x[4] + x[9]*x[5] + x[19] - x[20]^2 + x[9]*x[8] if(noiseVars > 0 | corrVars > 0) dat <- cbind(dat, make_noise(n = n, noiseVars = noiseVars, corrVars = corrVars, corrType = corrType, corrValue = corrValue)) dat$y <- apply(dat[, 1:20], 1, foo) + rnorm(n, sd = 4) dat } caret/R/workflows.R0000644000175000017500000007242212516214271014415 0ustar rbalintrbalint ### In this file, there are a lot of functions form caret that are ### references using the explicit namespace operator (:::). For some ### reason, with some parallel processing technologies and foreach, ### functions inside of caret cannot be found despite using the ### ".packages" argument and calling the caret package via library(). getOper <- function(x) if(x) `%dopar%` else `%do%` getTrainOper <- function(x) if(x) `%dopar%` else `%do%` progress <- function(x, names, iter, start = TRUE) { text <- paste(ifelse(start, "+ ", "- "), names[iter], ": ", paste(colnames(x), x, sep = "=", collapse = ", "), sep = "") cat(text, "\n") } MeanSD <- function(x, exclude = NULL) { if(!is.null(exclude)) x <- x[, !(colnames(x) %in% exclude), drop = FALSE] out <- c(colMeans(x, na.rm = TRUE), sapply(x, sd, na.rm = TRUE)) names(out)[-(1:ncol(x))] <- paste(names(out)[-(1:ncol(x))], "SD", sep = "") out } expandParameters <- function(fixed, seq) { if(is.null(seq)) return(fixed) isSeq <- names(fixed) %in% names(seq) out <- fixed for(i in 1:nrow(seq)) { tmp <- fixed tmp[,isSeq] <- seq[i,] out <- rbind(out, tmp) } out } nominalTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) printed <- format(info$loop, digits = 4) colnames(printed) <- gsub("^\\.", "", colnames(printed)) ## For 632 estimator, add an element to the index of zeros to trick it into ## fitting and predicting the full data set. resampleIndex <- ctrl$index if(ctrl$method %in% c("boot632")) { resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex) ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut) } `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) pkgs <- c("methods", "caret") if(!is.null(method$library)) pkgs <- c(pkgs, method$library) result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:% foreach(parm = 1:nrow(info$loop), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op% { testing <- FALSE if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) loadNamespace("caret") if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter) if(names(resampleIndex)[iter] != "AllData") { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] } else { modelIndex <- 1:nrow(x) holdoutIndex <- modelIndex } if(testing) cat("pre-model\n") if(is.null(info$submodels[[parm]]) || nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod <- try( createModel(x = x[modelIndex,,drop = FALSE ], y = y[modelIndex], wts = wts[modelIndex], method = method, tuneValue = info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...), silent = TRUE) if(class(mod)[1] != "try-error") { predicted <- try( predictionFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod), silent = TRUE) if(class(predicted)[1] == "try-error") { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("predictions failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(predicted), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } } else { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("model fit failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(mod), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } if(testing) print(head(predicted)) if(ctrl$classProbs) { if(class(mod)[1] != "try-error") { probValues <- probFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod) } else { probValues <- as.data.frame(matrix(NA, nrow = nPred, ncol = length(lev))) colnames(probValues) <- lev if(!is.null(submod)) { tmp <- probValues probValues <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = probValues)) probValues[[i]] <- tmp rm(tmp) } } if(testing) print(head(probValues)) } ################################## if(is.numeric(y)) { if(is.logical(ctrl$predictionBounds) && any(ctrl$predictionBounds)) { if(is.list(predicted)) { predicted <- lapply(predicted, trimPredictions, mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimits) } else { predicted <- trimPredictions(mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimit, pred = predicted) } } else { if(is.numeric(ctrl$predictionBounds) && any(!is.na(ctrl$predictionBounds))) { if(is.list(predicted)) { predicted <- lapply(predicted, trimPredictions, mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimits) } else { predicted <- trimPredictions(mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimit, pred = predicted) } } } } if(!is.null(submod)) { ## merge the fixed and seq parameter values together allParam <- expandParameters(info$loop[parm,,drop = FALSE], info$submodels[[parm]]) allParam <- allParam[complete.cases(allParam),, drop = FALSE] ## collate the predicitons across all the sub-models predicted <- lapply(predicted, function(x, y, wts, lv, rows) { if(!is.factor(x) & is.character(x)) x <- factor(as.character(x), levels = lv) out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE) if(!is.null(wts)) out$weights <- wts out$rowIndex <- rows out }, y = y[holdoutIndex], wts = wts[holdoutIndex], lv = lev, rows = holdoutIndex) if(testing) print(head(predicted)) ## same for the class probabilities if(ctrl$classProbs) { for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) } if(ctrl$savePredictions) { tmpPred <- predicted for(modIndex in seq(along = tmpPred)) { tmpPred[[modIndex]]$rowIndex <- holdoutIndex tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex,,drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ## get the performance for this resample for each sub-model thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if(testing) print(head(thisResample)) ## for classification, add the cell counts if(length(lev) > 1) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { if(is.factor(y)) predicted <- factor(as.character(predicted), levels = lev) tmp <- data.frame(pred = predicted, obs = y[holdoutIndex], stringsAsFactors = FALSE) ## Sometimes the code above does not coerce the first ## columnn to be named "pred" so force it names(tmp)[1] <- "pred" if(!is.null(wts)) tmp$weights <- wts[holdoutIndex] if(ctrl$classProbs) tmp <- cbind(tmp, probValues) tmp$rowIndex <- holdoutIndex if(ctrl$savePredictions) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, info$loop[parm,,drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ################################## thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) ## if classification, get the confusion matrix if(length(lev) > 1) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample)) thisResample <- cbind(thisResample, info$loop[parm,,drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter, FALSE) list(resamples = thisResample, pred = tmpPred) } resamples <- rbind.fill(result[names(result) == "resamples"]) pred <- if(ctrl$savePredictions) rbind.fill(result[names(result) == "pred"]) else NULL if(ctrl$method %in% c("boot632")) { perfNames <- names(ctrl$summaryFunction(data.frame(obs = y, pred = sample(y), weights = 1), lev = lev, model = method)) apparent <- subset(resamples, Resample == "AllData") apparent <- apparent[,!grepl("^\\.cell|Resample", colnames(apparent)),drop = FALSE] names(apparent)[which(names(apparent) %in% perfNames)] <- paste(names(apparent)[which(names(apparent) %in% perfNames)], "Apparent", sep = "") names(apparent) <- gsub("^\\.", "", names(apparent)) if(any(!complete.cases(apparent[,!grepl("^cell|Resample", colnames(apparent)),drop = FALSE]))) { warning("There were missing values in the apparent performance measures.") } resamples <- subset(resamples, Resample != "AllData") } names(resamples) <- gsub("^\\.", "", names(resamples)) if(any(!complete.cases(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE]))) { warning("There were missing values in resampled performance measures.") } out <- ddply(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE], ## TODO check this for seq models gsub("^\\.", "", colnames(info$loop)), MeanSD, exclude = gsub("^\\.", "", colnames(info$loop))) if(ctrl$method %in% c("boot632")) { out <- merge(out, apparent) for(p in seq(along = perfNames)) { const <- 1-exp(-1) out[, perfNames[p]] <- (const * out[, perfNames[p]]) + ((1-const) * out[, paste(perfNames[p],"Apparent", sep = "")]) } } list(performance = out, resamples = resamples, predictions = if(ctrl$savePredictions) pred else NULL) } looTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) printed <- format(info$loop) colnames(printed) <- gsub("^\\.", "", colnames(printed)) `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) pkgs <- c("methods", "caret") if(!is.null(method$library)) pkgs <- c(pkgs, method$library) result <- foreach(iter = seq(along = ctrl$index), .combine = "rbind", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:% foreach(parm = 1:nrow(info$loop), .combine = "rbind", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op% { if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) if(testing) cat("after loops\n") loadNamespace("caret") if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(ctrl$index), iter, TRUE) if(is.null(info$submodels[[parm]]) || nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod <- createModel(x = x[ctrl$index[[iter]],,drop = FALSE ], y = y[ctrl$index[[iter]] ], wts = wts[ctrl$index[[iter]] ], method = method, tuneValue = info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...) holdoutIndex <- -unique(ctrl$index[[iter]]) predicted <- predictionFunction(method = method, modelFit = mod$fit, newdata = x[-ctrl$index[[iter]],, drop = FALSE], preProc = mod$preProc, param = submod) if(is.numeric(y)) { if(is.logical(ctrl$predictionBounds) && any(ctrl$predictionBounds)) { if(is.list(predicted)) { predicted <- lapply(predicted, trimPredictions, mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimits) } else { predicted <- trimPredictions(mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimit, pred = predicted) } } else { if(is.numeric(ctrl$predictionBounds) && any(!is.na(ctrl$predictionBounds))) { if(is.list(predicted)) { predicted <- lapply(predicted, trimPredictions, mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimits) } else { predicted <- trimPredictions(mod_type = "Regression", bounds = ctrl$predictionBounds, limits = ctrl$yLimit, pred = predicted) } } } } if(testing) print(head(predicted)) if(ctrl$classProbs) { probValues <- probFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod) if(testing) print(head(probValues)) } ################################## if(!is.null(info$submodels)) { ## collate the predictions across all the sub-models predicted <- lapply(predicted, function(x, y, wts, lv, rows) { if(!is.factor(x) & is.character(x)) x <- factor(as.character(x), levels = lv) out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE) if(!is.null(wts)) out$weights <- wts out$rowIndex <- rows out }, y = y[holdoutIndex], wts = wts[holdoutIndex], lv = lev, rows = seq(along = y)[holdoutIndex]) if(testing) print(head(predicted)) ## same for the class probabilities if(ctrl$classProbs) { for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) } predicted <- do.call("rbind", predicted) allParam <- expandParameters(info$loop[parm,,drop = FALSE], submod) rownames(predicted) <- NULL predicted <- cbind(predicted, allParam) ## if saveDetails then save and export 'predicted' } else { if(is.factor(y)) predicted <- factor(as.character(predicted), levels = lev) predicted <- data.frame(pred = predicted, obs = y[holdoutIndex], stringsAsFactors = FALSE) if(!is.null(wts)) predicted$weights <- wts[holdoutIndex] if(ctrl$classProbs) predicted <- cbind(predicted, probValues) predicted$rowIndex <- seq(along = y)[holdoutIndex] predicted <- cbind(predicted, info$loop[parm,,drop = FALSE]) } if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(ctrl$index), iter, FALSE) predicted } names(result) <- gsub("^\\.", "", names(result)) out <- ddply(result, as.character(method$parameter$parameter), ctrl$summaryFunction, lev = lev, model = method) list(performance = out, predictions = result) } oobTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) printed <- format(info$loop) colnames(printed) <- gsub("^\\.", "", colnames(printed)) `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) pkgs <- c("methods", "caret") if(!is.null(method$library)) pkgs <- c(pkgs, method$library) result <- foreach(parm = 1:nrow(info$loop), .packages = pkgs, .combine = "rbind") %op% { loadNamespace("caret") if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], "", 1, TRUE) mod <- createModel(x = x, y = y, wts = wts, method = method, tuneValue = info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...) out <- switch(class(mod$fit)[1], randomForest = rfStats(mod$fit), RandomForest = cforestStats(mod$fit), bagEarth =, bagFDA = bagEarthStats(mod$fit), regbagg =, classbagg = ipredStats(mod$fit)) if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], "", 1, FALSE) cbind(as.data.frame(t(out)), info$loop[parm,,drop = FALSE]) } names(result) <- gsub("^\\.", "", names(result)) result } ################################################################################################ nominalSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) resampleIndex <- ctrl$index if(ctrl$method %in% c("boot632")){ resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex) ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut) } `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op% { if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[iter]) loadNamespace("caret") if(names(resampleIndex)[iter] != "AllData") { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] } else { modelIndex <- 1:nrow(x) holdoutIndex <- modelIndex } sbfResults <- sbfIter(x[modelIndex,,drop = FALSE], y[modelIndex], x[holdoutIndex,,drop = FALSE], y[holdoutIndex], ctrl, ...) if(ctrl$saveDetails) { tmpPred <- sbfResults$pred tmpPred$Resample <- names(resampleIndex)[iter] tmpPred$rowIndex <- seq(along = y)[unique(holdoutIndex)] } else tmpPred <- NULL resamples <- ctrl$functions$summary(sbfResults$pred, lev = lev) if(is.factor(y)) resamples <- c(resamples, flatTable(sbfResults$pred$pred, sbfResults$pred$obs)) resamples <- data.frame(t(resamples)) resamples$Resample <- names(resampleIndex)[iter] list(resamples = resamples, selectedVars = sbfResults$variables, pred = tmpPred) } resamples <- rbind.fill(result[names(result) == "resamples"]) pred <- if(ctrl$saveDetails) rbind.fill(result[names(result) == "pred"]) else NULL performance <- MeanSD(resamples[,!grepl("Resample", colnames(resamples)),drop = FALSE]) if(ctrl$method %in% c("boot632")) { modelIndex <- 1:nrow(x) holdoutIndex <- modelIndex appResults <- sbfIter(x[modelIndex,,drop = FALSE], y[modelIndex], x[holdoutIndex,,drop = FALSE], y[holdoutIndex], ctrl, ...) apparent <- ctrl$functions$summary(appResults$pred, lev = lev) perfNames <- names(apparent) const <- 1-exp(-1) for(p in seq(along = perfNames)) performance[perfNames[p]] <- (const * performance[perfNames[p]]) + ((1-const) * apparent[perfNames[p]]) } list(performance = performance, everything = result, predictions = if(ctrl$saveDetails) pred else NULL) } looSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) resampleIndex <- ctrl$index vars <- vector(mode = "list", length = length(y)) `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op% { if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[iter]) loadNamespace("caret") modelIndex <- resampleIndex[[iter]] holdoutIndex <- -unique(resampleIndex[[iter]]) sbfResults <- sbfIter(x[modelIndex,,drop = FALSE], y[modelIndex], x[holdoutIndex,,drop = FALSE], y[holdoutIndex], ctrl, ...) sbfResults } resamples <- do.call("rbind", result[names(result) == "pred"]) performance <- ctrl$functions$summary(resamples, lev = lev) list(performance = performance, everything = result, predictions = if(ctrl$saveDetails) resamples else NULL) } ################################################################################################ nominalRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) resampleIndex <- ctrl$index if(ctrl$method %in% c("boot632")) { resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex) ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut) } `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret", "plyr"), .errorhandling = "stop") %op% { loadNamespace("caret") if(names(resampleIndex)[iter] != "AllData") { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] } else { modelIndex <- 1:nrow(x) holdoutIndex <- modelIndex } seeds <- if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) ctrl$seeds[[iter]] else NA rfeResults <- rfeIter(x[modelIndex,,drop = FALSE], y[modelIndex], x[holdoutIndex,,drop = FALSE], y[holdoutIndex], sizes, ctrl, label = names(resampleIndex)[iter], seeds = seeds, ...) resamples <- ddply(rfeResults$pred, .(Variables), ctrl$functions$summary, lev = lev) if(ctrl$saveDetails) { rfeResults$pred$Resample <- names(resampleIndex)[iter] ## If the user did not have nrow(x) in 'sizes', rfeIter added it. ## So, we need to find out how many set of predictions there are: nReps <- length(table(rfeResults$pred$Variables)) rfeResults$pred$rowIndex <- rep(seq(along = y)[unique(holdoutIndex)], nReps) } if(is.factor(y)) { cells <- ddply(rfeResults$pred, .(Variables), function(x) flatTable(x$pred, x$obs)) resamples <- merge(resamples, cells) } resamples$Resample <- names(resampleIndex)[iter] vars <- do.call("rbind", rfeResults$finalVariables) vars$Resample <- names(resampleIndex)[iter] list(resamples = resamples, selectedVars = vars, predictions = if(ctrl$saveDetails) rfeResults$pred else NULL) } resamples <- do.call("rbind", result[names(result) == "resamples"]) rownames(resamples) <- NULL if(ctrl$method %in% c("boot632")) { perfNames <- names(ctrl$functions$summary(data.frame(obs =y, pred = sample(y)), lev = lev, model = method)) apparent <- subset(resamples, Resample == "AllData") apparent <- apparent[,!grepl("^\\.cell|Resample", colnames(apparent)),drop = FALSE] names(apparent)[which(names(apparent) %in% perfNames)] <- paste(names(apparent)[which(names(apparent) %in% perfNames)], "Apparent", sep = "") names(apparent) <- gsub("^\\.", "", names(apparent)) resamples <- subset(resamples, Resample != "AllData") } externPerf <- ddply(resamples[,!grepl("\\.cell|Resample", colnames(resamples)),drop = FALSE], .(Variables), MeanSD, exclude = "Variables") if(ctrl$method %in% c("boot632")) { externPerf <- merge(externPerf, apparent) for(p in seq(along = perfNames)) { const <- 1-exp(-1) externPerf[, perfNames[p]] <- (const * externPerf[, perfNames[p]]) + ((1-const) * externPerf[, paste(perfNames[p],"Apparent", sep = "")]) } externPerf <- externPerf[, !(names(externPerf) %in% paste(perfNames,"Apparent", sep = ""))] } list(performance = externPerf, everything = result) } looRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) resampleIndex <- ctrl$index `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op% { loadNamespace("caret") modelIndex <- resampleIndex[[iter]] holdoutIndex <- -unique(resampleIndex[[iter]]) seeds <- if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) ctrl$seeds[[iter]] else NA rfeResults <- rfeIter(x[modelIndex,,drop = FALSE], y[modelIndex], x[holdoutIndex,,drop = FALSE], y[holdoutIndex], sizes, ctrl, seeds = seeds, ...) rfeResults } preds <- do.call("rbind", result[names(result) == "pred"]) resamples <- ddply(preds, .(Variables), ctrl$functions$summary, lev = lev) list(performance = resamples, everything = result) } caret/R/findCorrelation.R0000644000175000017500000000240012453550064015472 0ustar rbalintrbalintfindCorrelation <- function(x, cutoff = 0.90, verbose = FALSE) { varnum <- dim(x)[1] if (!isTRUE(all.equal(x, t(x)))) stop("correlation matrix is not symmetric") if (varnum == 1) stop("only one variable given") x <- abs(x) # re-ordered columns based on max absolute correlation originalOrder <- 1:varnum averageCorr <- function(x) mean(x, na.rm = TRUE) tmp <- x diag(tmp) <- NA maxAbsCorOrder <- order(apply(tmp, 2, averageCorr), decreasing = TRUE) x <- x[maxAbsCorOrder, maxAbsCorOrder] newOrder <- originalOrder[maxAbsCorOrder] deletecol <- rep(F, varnum) for (i in 1:(varnum - 1)) { if (deletecol[i]) next for (j in (i + 1):varnum) { if (!deletecol[i] & !deletecol[j]) { if(verbose) cat("Considering row\t", newOrder[i], "column\t", newOrder[j], "value\t", round(x[i,j], 3), "\n") if (x[i, j] > cutoff) { if (mean(x[i, -i]) > mean(x[-j, j])) { deletecol[i] <- T if (verbose) cat(" Flagging column\t", newOrder[i], "\n") } else { deletecol[j] <- T if (verbose) cat(" Flagging column\t", newOrder[j], "\n") } } } } } newOrder[which(deletecol)] } caret/R/postResample.R0000644000175000017500000000216312453550064015034 0ustar rbalintrbalintpostResample <- function(pred, obs) { isNA <- is.na(pred) pred <- pred[!isNA] obs <- obs[!isNA] if(!is.factor(obs) & is.numeric(obs)) { if(length(obs) + length(pred) == 0) { out <- rep(NA, 2) } else { if(length(unique(pred)) < 2 || length(unique(obs)) < 2) { resamplCor <- NA } else { resamplCor <- try(cor(pred, obs, use = "pairwise.complete.obs"), silent = TRUE) if(class(resamplCor) == "try-error") resamplCor <- NA } mse <- mean((pred - obs)^2) n <- length(obs) out <- c(sqrt(mse), resamplCor^2) } names(out) <- c("RMSE", "Rsquared") } else { if(length(obs) + length(pred) == 0) { out <- rep(NA, 2) } else { pred <- factor(pred, levels = levels(obs)) requireNamespaceQuietStop("e1071") out <- unlist(e1071::classAgreement(table(obs, pred)))[c("diag", "kappa")] } names(out) <- c("Accuracy", "Kappa") } if(any(is.nan(out))) out[is.nan(out)] <- NA out } caret/R/resamples.R0000644000175000017500000010035612516214271014351 0ustar rbalintrbalint"resamples" <- function(x, ...) UseMethod("resamples") resamples.default <- function(x, modelNames = names(x), ...) { ## Do a lot of checkin of the object types and make sure ## that they actually used the samples samples in the resamples if(length(x) < 2) stop("at least two train objects are needed") classes <- unlist(lapply(x, function(x) class(x)[1])) # if(!all(classes %in% c("sbf", "rfe", "train"))) stop("all objects in x must of class train, sbf or rfe") if(is.null(modelNames)){ modelNames <- well_numbered("Model", length(x)) } else { if(any(modelNames == "")) { no_name <- which(modelNames == "") modelNames[no_name] <- well_numbered("Model", length(x))[no_name] } } numResamp <- unlist(lapply(x, function(x) length(x$control$index))) if(length(unique(numResamp)) > 1) stop("There are different numbers of resamples in each model") for(i in 1:numResamp[1]){ indices <- lapply(x, function(x, i) sort(x$control$index[[1]]), i = i) uniqueIndex <- length(table(table(unlist(indices)))) if(length(uniqueIndex) > 1) stop("The samples indices are not equal across resamples") } getTimes <- function(x){ out <- rep(NA, 3) if(all(names(x) != "times")) return(out) if(any(names(x$times) == "everything")) out[1] <- x$time$everything[3] if(any(names(x$times) == "final")) out[2] <- x$time$final[3] if(any(names(x$times) == "prediction")) out[3] <- x$time$prediction[3] out } rs_values <- vector(mode = "list", length = length(x)) for(i in seq(along = x)) { if(class(x[[i]])[1] == "rfe" && x[[i]]$control$returnResamp == "all"){ warning(paste0("'", modelNames[i], "' did not have 'returnResamp=\"final\"; the optimal subset is used")) } if(class(x[[i]])[1] == "train" && x[[i]]$control$returnResamp == "all"){ warning(paste0("'", modelNames[i], "' did not have 'returnResamp=\"final\"; the optimal tuning parameters are used")) } if(class(x[[i]])[1] == "sbf" && x[[i]]$control$returnResamp == "all"){ warning(paste0("'", modelNames[i], "' did not have 'returnResamp=\"final\"; the optimal subset is used")) } rs_values[[i]] <- get_resample_perf(x[[i]]) } all_names <- lapply(rs_values, function(x) names(x)[names(x) != "Resample"]) all_names <- table(unlist(all_names)) if(length(all_names) == 0 || any(all_names == 0)) { warning("Could not find performance measures") } if(any(all_names < length(x))) { warning(paste("Some performance measures were not computed for each model:", paste(names(all_names)[all_names < length(x)], collapse = ", "))) } pNames <- names(all_names)[all_names == length(x)] rs_values <- lapply(rs_values, function(x, n) x[,n,drop = FALSE], n = c(pNames, "Resample")) for(mod in seq(along = modelNames)) { names(rs_values[[mod]])[names(rs_values[[mod]]) %in% pNames] <- paste(modelNames[mod], names(rs_values[[mod]])[names(rs_values[[mod]]) %in% pNames], sep = "~") out <- if(mod == 1) rs_values[[mod]] else merge(out, rs_values[[mod]]) } timings <- do.call("rbind", lapply(x, getTimes)) colnames(timings) <- c("Everything", "FinalModel", "Prediction") out <- structure( list( call = match.call(), values = out, models = modelNames, metrics = pNames, timings = as.data.frame(timings), methods = unlist(lapply(x, function(x) x$method))), class = "resamples") out } prcomp.resamples <- function(x, metric = x$metrics[1], ...) { if(length(metric) != 1) stop("exactly one metric must be given") tmpData <- x$values[, grep(paste("~", metric, sep = ""), names(x$value), fixed = TRUE), drop = FALSE] names(tmpData) <- gsub(paste("~", metric, sep = ""), "", names(tmpData), fixed = TRUE) tmpData <- as.data.frame(t(tmpData)) colnames(tmpData) <- paste("Resample", gsub(" ", "0", format(1:ncol(tmpData))), sep = "") out <- prcomp(~., data = tmpData, ...) out$metric <- metric out$call <- match.call() class(out) <- c("prcomp.resamples", "prcomp") out } "cluster" <- function(x, ...) UseMethod("cluster") cluster.default <- function(x, ...) stop("only implemented for resamples objects") cluster.resamples <- function(x, metric = x$metrics[1], ...) { if(length(metric) != 1) stop("exactly one metric must be given") tmpData <- x$values[, grep(paste("~", metric, sep = ""), names(x$value), fixed = TRUE), drop = FALSE] names(tmpData) <- gsub(paste("~", metric, sep = ""), "", names(tmpData), fixed = TRUE) tmpData <- t(tmpData) dt <- dist(tmpData) out <- hclust(dt, ...) out$metric <- metric out$call <- match.call() class(out) <- c("cluster.resamples", "hclust") out } plot.prcomp.resamples <- function(x, what = "scree", dims = max(2, ncol(x$rotation)), ...) { if(length(what) > 1) stop("one plot at a time please") switch(what, scree = { barchart(x$sdev ~ paste("PC", gsub(" ", "0", format(seq(along = x$sdev))), sep = ""), ylab = "Standard Deviation", ...) }, cumulative = { barchart(cumsum(x$sdev^2)/sum(x$sdev^2) ~ paste("PC", gsub(" ", "0", format(seq(along = x$sdev))), sep = ""), ylab = "Culmulative Percent of Variance", ...) }, loadings = { panelRange <- extendrange(x$rotation[, 1:dims,drop = FALSE]) if(dims > 2) { splom(~x$rotation[, 1:dims,drop = FALSE], main = useMathSymbols(x$metric), prepanel.limits = function(x) panelRange, type = c("p", "g"), ...) } else { xyplot(PC2~PC1, data = as.data.frame(x$rotation), main = useMathSymbols(x$metric), xlim = panelRange, ylim = panelRange, type = c("p", "g"), ...) } }, components = { panelRange <- extendrange(x$x[, 1:dims,drop = FALSE]) if(dims > 2) { splom(~x$x[, 1:dims,drop = FALSE], main = useMathSymbols(x$metric), prepanel.limits = function(x) panelRange, groups = rownames(x$x), type = c("p", "g"), ...) } else { xyplot(PC2~PC1, data = as.data.frame(x$x), main = useMathSymbols(x$metric), xlim = panelRange, ylim = panelRange, groups = rownames(x$x), type = c("p", "g"), ...) } }) } print.prcomp.resamples <- function (x, digits = max(3, getOption("digits") - 3), print.x = FALSE, ...) { printCall(x$call) cat("Metric:", x$metric, "\n") sds <- rbind(x$sdev, cumsum(x$sdev^2)/sum(x$sdev^2)) rownames(sds) <- c("Std. Dev. ", "Cum. Percent Var. ") colnames(sds) <- rep("", ncol(sds)) print(sds, digits = digits, ...) cat("\nRotation:\n") print(x$rotation, digits = digits, ...) if (print.x && length(x$x)) { cat("\nRotated variables:\n") print(x$x, digits = digits, ...) } invisible(x) } print.resamples <- function(x, ...) { printCall(x$call) cat("Models:", paste(x$models, collapse = ", "), "\n") cat("Number of resamples:", nrow(x$values), "\n") cat("Performance metrics:", paste(x$metrics, collapse = ", "), "\n") hasTimes <- apply(x$timing, 2, function(a) !all(is.na(a))) if(any(hasTimes)) { names(hasTimes) <- gsub("FinalModel", "final model fit", names(hasTimes)) cat("Time estimates for:", paste(tolower(names(hasTimes))[hasTimes], collapse = ", "), "\n") } invisible(x) } summary.resamples <- function(object, metric = object$metrics, ...){ vals <- object$values[, names(object$values) != "Resample", drop = FALSE] out <- vector(mode = "list", length = length(metric)) for(i in seq(along = metric)) { tmpData <- vals[, grep(paste("~", metric[i], sep = ""), names(vals), fixed = TRUE), drop = FALSE] out[[i]] <- do.call("rbind", lapply(tmpData, function(x) summary(x)[1:6])) naSum <- matrix(unlist(lapply(tmpData, function(x) sum(is.na(x)))), ncol = 1) colnames(naSum) <- "NA's" out[[i]] <- cbind(out[[i]], naSum) rownames(out[[i]]) <- gsub(paste("~", metric[i], sep = ""), "", rownames(out[[i]]), fixed = TRUE) } names(out) <- metric out <- structure( list(values = vals, call = match.call(), statistics = out, models = object$models, metrics = metric, methods = object$methods), class = "summary.resamples") out } print.summary.resamples <- function(x, ...) { printCall(x$call) cat("Models:", paste(x$models, collapse = ", "), "\n") cat("Number of resamples:", nrow(x$values), "\n") cat("\n") for(i in seq(along = x$statistics)) { cat(names(x$statistics)[i], "\n") print(x$statistics[[i]]) cat("\n") } invisible(x) } xyplot.resamples <- function (x, data = NULL, what = "scatter", models = NULL, metric = x$metric[1], units = "min", ...) { if(!(units %in% c("min", "sec", "hour"))) stop("units should be 'sec', 'min' or 'hour'") if(!(what %in% c("scatter", "BlandAltman", "tTime", "mTime", "pTime"))) stop("the what arg should be 'scatter', 'BlandAltman', 'tTime', 'mTime' or 'pTime'") if(is.null(models)) models <- if(what %in% c("tTime", "mTime", "pTime")) x$models else x$models[1:2] if(length(metric) != 1) stop("exactly one metric must be given") if(what == "BlandAltman" & length(models) != 2) stop("exactly two model names must be given") if(what == "BlandAltman") { tmpData <- x$values[, paste(models, metric, sep ="~")] tmpData$Difference <- tmpData[,1] - tmpData[,2] tmpData$Average <- (tmpData[,1] + tmpData[,2])/2 ylm <- extendrange(c(tmpData$Difference, 0)) out <- xyplot(Difference ~ Average, data = tmpData, ylab = paste(models, collapse = " - "), ylim = ylm, main = useMathSymbols(metric), panel = function(x, y, ...) { panel.abline(h = 0, col = "darkgrey", lty = 2) panel.xyplot(x, y, ...) }) } if(what == "scatter") { tmpData <- x$values[, paste(models, metric, sep ="~")] colnames(tmpData) <- gsub(paste("~", metric, sep = ""), "", colnames(tmpData)) ylm <- extendrange(c(tmpData[,1], tmpData[,2])) out <- xyplot(tmpData[,1] ~ tmpData[,2], ylab = colnames(tmpData)[1], xlab = colnames(tmpData)[2], xlim = ylm, ylim = ylm, main = useMathSymbols(metric), panel = function(x, y, ...) { panel.abline(0, 1, col = "darkgrey", lty = 2) panel.xyplot(x, y, ...) }) } if(what %in% c("tTime", "mTime", "pTime")) { ## the following is based on ## file.show(system.file("demo/intervals.R", package = "lattice") prepanel.ci <- function(x, y, lx, ux, subscripts, ...) { x <- as.numeric(x) lx <- as.numeric(lx[subscripts]) ux <- as.numeric(ux[subscripts]) list(xlim = extendrange(c(x, ux, lx)), ylim = extendrange(y)) } panel.ci <- function(x, y, lx, ux, groups, subscripts, pch = 16, ...) { theme <- trellis.par.get() x <- as.numeric(x) y <- as.numeric(y) lx <- as.numeric(lx[subscripts]) ux <- as.numeric(ux[subscripts]) gps <- unique(groups) for(i in seq(along = gps)) { panel.arrows(lx[groups == gps[i]], y[groups == gps[i]], ux[groups == gps[i]], y[groups == gps[i]], col = theme$superpose.line$col[i], length = .01, unit = "npc", angle = 90, code = 3) panel.xyplot(x[groups == gps[i]], y[groups == gps[i]], type = "p", col = theme$superpose.symbol$col[i], cex = theme$superpose.symbol$cex[i], pch = theme$superpose.symbol$pch[i], , ...) } } tmpData <- apply(x$values[,-1], 2, function(x) { tmp <- t.test(x) c(tmp$conf.int[1], tmp$estimate, tmp$conf.int[2]) }) rownames(tmpData) <- c("lower", "point", "upper") tmpData <- tmpData[, paste(models, metric, sep ="~")] colnames(tmpData) <- gsub(paste("~", metric, sep = ""), "", colnames(tmpData)) tmpData <- data.frame(t(tmpData)) tmpData$Model <- rownames(tmpData) tm <- switch(what, tTime = x$timings[,"Everything"], mTime = x$timings[,"FinalModel"], pTime = x$timings[,"Prediction"]) lab <- switch(what, tTime = "Total Time (", mTime = "Final Model Time (", pTime = "Prediction Time (") lab <- paste(lab, units, ")", sep = "") times <- data.frame(Time = tm, Model = rownames(x$timings)) plotData <- merge(times, tmpData) if(units == "min") plotData$Time <- plotData$Time/60 if(units == "hour") plotData$Time <- plotData$Time/60/60 out <- with(plotData, xyplot(Time ~ point, lx = lower, ux = upper, xlab = useMathSymbols(metric), ylab = lab, prepanel = prepanel.ci, panel = panel.ci, groups = Model, ...)) } out } parallelplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric[1], ...) { if(length(metric) != 1) stop("exactly one metric must be given") tmpData <- x$values[, grep(paste("~", metric, sep = ""), names(x$value), fixed = TRUE), drop = FALSE] names(tmpData) <- gsub(paste("~", metric, sep = ""), "", names(tmpData), fixed = TRUE) rng <- range(unlist(lapply(lapply(tmpData, as.numeric), range, na.rm = TRUE))) prng <- pretty(rng) reord <- order(apply(tmpData, 2, median, na.rm = TRUE)) tmpData <- tmpData[, reord] parallelplot(~tmpData, common.scale = TRUE, scales = list(x = list(at = (prng-min(rng))/diff(rng), labels = prng)), xlab = useMathSymbols(metric), ...) } splom.resamples <- function (x, data = NULL, variables = "models", models = x$models, metric = NULL, panelRange = NULL, ...) { if(variables == "models") { if(is.null(metric)) metric <- x$metric[1] if(length(metric) != 1) stop("exactly one metric must be given") tmpData <- x$values[, grep(paste("~", metric, sep = ""), names(x$value), fixed = TRUE), drop = FALSE] names(tmpData) <- gsub(paste("~", metric, sep = ""), "", names(tmpData), fixed = TRUE) tmpData <- tmpData[, models,drop = FALSE] if(is.null(panelRange)) panelRange <- extendrange(tmpData) splom(~tmpData, panel = function(x, y) { panel.splom(x, y, ...) panel.abline(0, 1, lty = 2, col = "darkgrey") }, main = useMathSymbols(metric), prepanel.limits = function(x) panelRange, ...) } else{ if(variables == "metrics") { if(is.null(metric)) metric <- x$metric if(length(metric) < 2) stop("There should be at least two metrics") plotData <- melt(x$values, id.vars = "Resample") tmp <- strsplit(as.character(plotData$variable), "~", fixed = TRUE) plotData$Model <- unlist(lapply(tmp, function(x) x[1])) plotData$Metric <- unlist(lapply(tmp, function(x) x[2])) plotData <- subset(plotData, Model %in% models & Metric %in% metric) means <- dcast(plotData, Model ~ Metric, mean, na.rm = TRUE) splom(~means[, metric], groups = means$Model, ...) } else stop ("'variables' should be either 'models' or 'metrics'") } } densityplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric, ...) { plotData <- melt(x$values, id.vars = "Resample") tmp <- strsplit(as.character(plotData$variable), "~", fixed = TRUE) plotData$Model <- unlist(lapply(tmp, function(x) x[1])) plotData$Metric <- unlist(lapply(tmp, function(x) x[2])) plotData <- subset(plotData, Model %in% models & Metric %in% metric) metricVals <- unique(plotData$Metric) plotForm <- if(length(metricVals) > 1) as.formula(~value|Metric) else as.formula(~value) densityplot(plotForm, data = plotData, groups = Model, xlab = if(length(unique(plotData$Metric)) > 1) "" else metricVals, ...) } bwplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric, ...) { plotData <- melt(x$values, id.vars = "Resample") tmp <- strsplit(as.character(plotData$variable), "~", fixed = TRUE) plotData$Model <- unlist(lapply(tmp, function(x) x[1])) plotData$Metric <- unlist(lapply(tmp, function(x) x[2])) plotData <- subset(plotData, Model %in% models & Metric %in% metric) avPerf <- ddply(subset(plotData, Metric == metric[1]), .(Model), function(x) c(Median = median(x$value, na.rm = TRUE))) avPerf <- avPerf[order(avPerf$Median),] plotData$Model <- factor(as.character(plotData$Model), levels = avPerf$Model) metricVals <- unique(plotData$Metric) plotForm <- if(length(metricVals) > 1) as.formula(Model~value|Metric) else as.formula(Model~value) bwplot(plotForm, data = plotData, xlab = if(length(unique(plotData$Metric)) > 1) "" else metricVals, ...) } dotplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric, conf.level = 0.95, ...) { plotData <- melt(x$values, id.vars = "Resample") tmp <- strsplit(as.character(plotData$variable), "~", fixed = TRUE) plotData$Model <- unlist(lapply(tmp, function(x) x[1])) plotData$Metric <- unlist(lapply(tmp, function(x) x[2])) plotData <- subset(plotData, Model %in% models & Metric %in% metric) plotData$variable <- factor(as.character(plotData$variable)) plotData <- split(plotData, plotData$variable) results <- lapply(plotData, function(x, cl) { ttest <- try(t.test(x$value, conf.level = cl), silent = TRUE) if(class(ttest)[1] == "htest") { out <- c(ttest$conf.int, ttest$estimate) names(out) <- c("LowerLimit", "UpperLimit", "Estimate") } else out <- rep(NA, 3) out }, cl = conf.level) results <- do.call("rbind", results) results <- melt(results) results <- results[!is.nan(results$value),] tmp <- strsplit(as.character(results$Var1), "~", fixed = TRUE) results$Model <- unlist(lapply(tmp, function(x) x[1])) results$Metric <- unlist(lapply(tmp, function(x) x[2])) ## to avoid "no visible binding for global variable 'Var2'" Var2 <- NULL avPerf <- ddply(subset(results, Metric == metric[1] & Var2 == "Estimate"), .(Model), function(x) c(Median = median(x$value, na.rm = TRUE))) avPerf <- avPerf[order(avPerf$Median),] results$Model <- factor(as.character(results$Model), levels = avPerf$Model) metricVals <- unique(results$Metric) plotForm <- if(length(metricVals) > 1) as.formula(Model~value|Metric) else as.formula(Model~value) dotplot(plotForm, data = results, xlab = if(length(unique(plotData$Metric)) > 1) "" else metricVals, panel = function(x, y) { plotTheme <- trellis.par.get() y <- as.numeric(y) vals <- aggregate(x, list(group = y), function(x) c(min = min(x), mid = median(x), max = max(x))) panel.dotplot(vals$x[,"mid"], vals$group, pch = plotTheme$plot.symbol$pch[1], col = plotTheme$plot.symbol$col[1], cex = plotTheme$plot.symbol$cex[1]) panel.segments(vals$x[,"min"], vals$group, vals$x[,"max"], vals$group, lty = plotTheme$plot.line$lty[1], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1]) len <- .03 panel.segments(vals$x[,"min"], vals$group+len, vals$x[,"min"], vals$group-len, lty = plotTheme$plot.line$lty[1], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1]) panel.segments(vals$x[,"max"], vals$group+len, vals$x[,"max"], vals$group-len, lty = plotTheme$plot.line$lty[1], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1]) }, sub = paste("Confidence Level:", conf.level), ...) } diff.resamples <- function(x, models = x$models, metric = x$metrics, test = t.test, confLevel = 0.95, adjustment = "bonferroni", ...) { allDif <- vector(mode = "list", length = length(metric)) names(allDif) <- metric x$models <- x$models[x$models %in% models] p <- length(x$models) ncomp <- choose(p, 2) if(adjustment == "bonferroni") confLevel <- 1 - ((1 - confLevel)/ncomp) allStats <- allDif for(h in seq(along = metric)) { index <- 0 dif <- matrix(NA, nrow = nrow(x$values), ncol = choose(length(models), 2)) stat <- vector(mode = "list", length = choose(length(models), 2)) colnames(dif) <- paste("tmp", 1:ncol(dif), sep = "") for(i in seq(along = models)) { for(j in seq(along = models)) { if(i < j) { index <- index + 1 left <- paste(models[i], metric[h], sep = "~") right <- paste(models[j], metric[h], sep = "~") dif[,index] <- x$values[,left] - x$values[,right] colnames(dif)[index] <- paste(models[i], models[j], sep = ".diff.") } } } stats <- apply(dif, 2, function(x, tst, ...) tst(x, ...), tst = test, conf.level = confLevel, ...) allDif[[h]] <- dif allStats[[h]] <- stats } out <- structure( list( call = match.call(), difs = allDif, confLevel = confLevel, adjustment = adjustment, statistics = allStats, models = models, metric = metric), class = "diff.resamples") out } densityplot.diff.resamples <- function(x, data, metric = x$metric, ...) { plotData <- lapply(x$difs, function(x) stack(as.data.frame(x))) plotData <- do.call("rbind", plotData) plotData$Metric <- rep(x$metric, each = length(x$difs[[1]])) plotData$ind <- gsub(".diff.", " - ", plotData$ind, fixed = TRUE) plotData <- subset(plotData, Metric %in% metric) metricVals <- unique(plotData$Metric) plotForm <- if(length(metricVals) > 1) as.formula(~values|Metric) else as.formula(~values) densityplot(plotForm, data = plotData, groups = ind, xlab = if(length(unique(plotData$Metric)) > 1) "" else metricVals, ...) } bwplot.diff.resamples <- function(x, data, metric = x$metric, ...) { plotData <- lapply(x$difs, function(x) stack(as.data.frame(x))) plotData <- do.call("rbind", plotData) plotData$Metric <- rep(x$metric, each = length(x$difs[[1]])) plotData$ind <- gsub(".diff.", " - ", plotData$ind, fixed = TRUE) plotData <- subset(plotData, Metric %in% metric) metricVals <- unique(plotData$Metric) plotForm <- if(length(metricVals) > 1) as.formula(ind ~ values|Metric) else as.formula(ind ~ values) bwplot(plotForm, data = plotData, xlab = if(length(unique(plotData$Metric)) > 1) "" else metricVals, ...) } print.diff.resamples <- function(x, ...) { printCall(x$call) cat("Models:", paste(x$models, collapse = ", "), "\n") cat("Metrics:", paste(x$metric, collapse = ", "), "\n") cat("Number of differences:", ncol(x$difs[[1]]), "\n") cat("p-value adjustment:", x$adjustment, "\n") invisible(x) } summary.diff.resamples <- function(object, digits = max(3, getOption("digits") - 3), ...) { all <- vector(mode = "list", length = length(object$metric)) names(all) <- object$metric for(h in seq(along = object$metric)) { pvals <- matrix(NA, nrow = length(object$models), ncol = length(object$models)) meanDiff <- pvals index <- 0 for(i in seq(along = object$models)) { for(j in seq(along = object$models)) { if(i < j) { index <- index + 1 meanDiff[i, j] <- object$statistics[[h]][index][[1]]$estimate } } } index <- 0 for(i in seq(along = object$models)) { for(j in seq(along = object$models)) { if(i < j) { index <- index + 1 pvals[j, i] <- object$statistics[[h]][index][[1]]$p.value } } } pvals[lower.tri(pvals)] <- p.adjust(pvals[lower.tri(pvals)], method = object$adjustment) asText <- matrix("", nrow = length(object$models), ncol = length( object$models)) meanDiff2 <- format(meanDiff, digits = digits) pvals2 <- matrix(format.pval(pvals, digits = digits), nrow = length( object$models)) asText[upper.tri(asText)] <- meanDiff2[upper.tri(meanDiff2)] asText[lower.tri(asText)] <- pvals2[lower.tri(pvals2)] diag(asText) <- "" colnames(asText) <- object$models rownames(asText) <- object$models all[[h]] <- asText } out <- structure( list( call = match.call(), adjustment = object$adjustment, table = all), class = "summary.diff.resamples") out } levelplot.diff.resamples <- function(x, data = NULL, metric = x$metric[1], what = "pvalues", ...) { comps <- ncol(x$difs[[1]]) if(length(metric) != 1) stop("exactly one metric must be given") all <- vector(mode = "list", length = length(x$metric)) names(all) <- x$metric for(h in seq(along = x$metric)) { temp <- matrix(NA, nrow = length(x$models), ncol = length( x$models)) index <- 0 for(i in seq(along = x$models)) { for(j in seq(along = x$models)) { if(i < j) { index <- index + 1 temp[i, j] <- if(what == "pvalues") { x$statistics[[h]][index][[1]]$p.value } else x$statistics[[h]][index][[1]]$estimate temp[j, i] <- temp[i, j] } } } colnames(temp) <- x$models temp <- as.data.frame(temp) temp$A <- x$models temp$Metric <- x$metric[h] all[[h]] <- temp } all <- do.call("rbind", all) all <- melt(all, measure.vars = x$models) names(all)[names(all) == "variable"] <- "B" all$A <- factor(all$A, levels = x$models) all$B <- factor(as.character(all$B), levels = x$models) all <- all[complete.cases(all),] levelplot(value ~ A + B|Metric, data = all, subset = Metric %in% metric, xlab = "", ylab = "", sub = ifelse(what == "pvalues", "p-values", "difference = (x axis - y axis)"), ...) } print.summary.diff.resamples <- function(x, ...) { printCall(x$call) if(x$adjustment != "none") cat("p-value adjustment:", x$adjustment, "\n") cat("Upper diagonal: estimates of the difference\n", "Lower diagonal: p-value for H0: difference = 0\n\n", sep = "") for(i in seq(along = x$table)) { cat(names(x$table)[i], "\n") print(x$table[[i]], quote = FALSE) cat("\n") } invisible(x) } dotplot.diff.resamples <- function(x, data = NULL, metric = x$metric[1], ...) { if(length(metric) > 1) { metric <- metric[1] warning("Sorry Dave, only one value of metric is allowed right now. I'll use the first value") } h <- which(x$metric == metric) plotData <- as.data.frame(matrix(NA, ncol = 3, nrow = ncol(x$difs[[metric]]))) ## Get point and interval estimates on the differences index <- 0 for(i in seq(along = x$models)) { for(j in seq(along = x$models)) { if(i < j) { index <- index + 1 plotData[index, 1] <- x$statistics[[h]][index][[1]]$estimate plotData[index, 2:3] <- x$statistics[[h]][index][[1]]$conf.int } } } names(plotData)[1:3] <- c("Estimate", "LowerLimit", "UpperLimit") plotData$Difference <- gsub(".diff.", " - ", colnames(x$difs[[metric]]), fixed = TRUE) plotData <- melt(plotData, id.vars = "Difference") xText <- paste("Difference in", useMathSymbols(metric), "\nConfidence Level", round(x$confLevel, 3), ifelse(x$adjustment == "bonferroni", " (multiplicity adjusted)", " (no multiplicity adjustment)")) dotplot(Difference ~ value, data = plotData, xlab = xText, panel = function(x, y) { plotTheme <- trellis.par.get() middle <- aggregate(x, list(mod = y), median) upper <- aggregate(x, list(mod = as.numeric(y)), max) lower <- aggregate(x, list(mod = as.numeric(y)), min) panel.dotplot(middle$x, middle$mod, col = plotTheme$plot.symbol$col[1], pch = plotTheme$plot.symbol$pch[1], cex = plotTheme$plot.symbol$cex[1]) panel.abline(v = 0, col = plotTheme$reference.line$col[1], lty = plotTheme$reference.line$lty[1], lwd = plotTheme$reference.line$lwd[1]) for(i in seq(along = upper$mod)) { panel.segments(upper$x[i], upper$mod[i], lower$x[i], lower$mod[i], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1], lty = plotTheme$plot.line$lty[1]) len <- .03 panel.segments(lower$x[i], upper$mod[i]+len, lower$x[i], lower$mod[i]-len, lty = plotTheme$plot.line$lty[1], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1]) panel.segments(upper$x[i],upper$mod[i]+len, upper$x[i], lower$mod[i]-len, lty = plotTheme$plot.line$lty[1], col = plotTheme$plot.line$col[1], lwd = plotTheme$plot.line$lwd[1]) } }, ...) } modelCor <- function(x, metric = x$metric[1], ...) { dat <- x$values[, grep(paste("~", metric[1], sep = ""), names(x$values))] colnames(dat) <- gsub(paste("~", metric[1], sep = ""), "", colnames(dat)) cor(dat, ...) } sort.resamples <- function(x, decreasing = FALSE, metric = x$metric[1], FUN = mean, ...) { dat <- x$values[, grep(paste("~", metric[1], sep = ""), names(x$values))] colnames(dat) <- gsub(paste("~", metric[1], sep = ""), "", colnames(dat)) stats <- apply(dat, 2, FUN, ...) names(sort(stats, decreasing = decreasing)) } compare_models <- function(a, b, metric = a$metric[1]) { mods <- list(a, b) rs <- resamples(mods) diffs <- diff(rs, metric = metric[1]) diffs$statistics[[1]][[1]] } caret/R/predictors.R0000644000175000017500000000417012453550064014534 0ustar rbalintrbalint"predictors" <- function(x, ...){ UseMethod("predictors") } predictors.train <- function(x, ...) { if(is.null(x$modelInfo)) { code <- getModelInfo(x$method, regex = FALSE)[[1]] } else code <- x$modelInfo if(!is.null(code$predictors)){ checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) out <- code$predictors(x$finalModel, ...) } else { if(hasTerms(x)) { out <- predictors(x$terms, ...) } else out <- NA } out } predictors.default <- function(x, ...) { cls <- model2method(class(x)[1]) if(cls == "gam") cls <- if(any(names(x) == "optimizer")) "gam" else "gamLoess" code <- getModelInfo(cls, regex = FALSE)[[1]] if(!is.null(code)) { if(!is.null(code$predictors)){ checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) out <- code$predictors(x, ...) } else { if(hasTerms(x)) { out <- predictors(x$terms, ...) } else out <- NA } } else { out <- if(hasTerms(x)) predictors(x$terms) else NA } out } hasTerms <- function(x) { objNames <- c(names(x), slotNames(x)) "terms" %in% tolower(objNames) } ## basicVars tries to detect the actual variable that are used ## when a formula might include other terms (such as interactions) ## For example: ## > x ## [1] "medv" "crim" "zn" "age" ## > y ## [1] "crim" "I(age^2)" "zn" ## > basicVars(x, y) ## [1] "crim" "zn" "age" basicVars <- function(x, y) { hasVar <- rep(NA, length(x)) for(i in seq(along = x)) hasVar[i] <- length(grep(x[i], y, fixed = TRUE)) > 0 x[hasVar] } predictors.terms <- function(x, ...) { if(is.null(x)) return(NA) everything <- all.vars(x) yName <- as.character(x[[2]]) everything[!(everything %in% yName)] } predictors.formula <- function(x, ...) { everything <- all.vars(x) yName <- as.character(x[[2]]) everything[!(everything %in% yName)] } predictors.list <- function(x, ...) { out <- lapply(x, predictors) names(out) <- names(x) out } caret/R/resampleWrapper.R0000644000175000017500000000071212453550064015525 0ustar rbalintrbalintresampleWrapper <- function(x, ind) { out <- rep(NA, dim(x$data)[1]) trainData <- x$data x$data <- x$data[ind,] tmpModelFit <- do.call(createModel, x) outBagData <- trainData[-ind, ] outBagData$.outcome <- NULL out[-ind] <- if(is.factor(x$data$.outcome)) { as.character(predictionFunction(x$method, tmpModelFit, outBagData)) } else { predictionFunction(x$method, tmpModelFit, outBagData) } out } caret/R/confusionMatrix.R0000644000175000017500000003330512516214271015545 0ustar rbalintrbalintconfusionMatrix <- function(data, ...){ UseMethod("confusionMatrix") } confusionMatrix.default <- function(data, reference, positive = NULL, dnn = c("Prediction", "Reference"), prevalence = NULL, ...) { if(!is.factor(data)) data <- factor(data) if(!is.factor(reference)) reference <- factor(reference) if(!is.character(positive) & !is.null(positive)) stop("positive argument must be character") if(length(levels(data)) > length(levels(reference))) stop("the data cannot have more levels than the reference") if(!any(levels(data) %in% levels(reference))){ stop("The data must contain some levels that overlap the reference.") } if(!all(levels(data) %in% levels(reference))){ badLevel <- levels(data)[!levels(data) %in% levels(reference)] if(sum(table(data)[badLevel]) > 0){ stop("The data contain levels not found in the data.") } else{ warning("The data contains levels not found in the data, but they are empty and will be dropped.") data <- factor(as.character(data)) } } if(any(levels(reference) != levels(data))) { warning("Levels are not in the same order for reference and data. Refactoring data to match.") data <- as.character(data) data <- factor(data, levels = levels(reference)) } classLevels <- levels(data) numLevels <- length(classLevels) if(numLevels < 2) stop("there must be at least 2 factors levels in the data") if(numLevels == 2 & is.null(positive)) positive <- levels(reference)[1] classTable <- table(data, reference, dnn = dnn, ...) confusionMatrix(classTable, positive, prevalence = prevalence) } confusionMatrix.table <- function(data, positive = NULL, prevalence = NULL, ...) { requireNamespaceQuietStop("e1071") if(length(dim(data)) != 2) stop("the table must have two dimensions") if(!all.equal(nrow(data), ncol(data))) stop("the table must nrow = ncol") if(!all.equal(rownames(data), colnames(data))) stop("the table must the same classes in the same order") if(!is.character(positive) & !is.null(positive)) stop("positive argument must be character") classLevels <- rownames(data) numLevels <- length(classLevels) if(numLevels < 2) stop("there must be at least 2 factors levels in the data") if(numLevels == 2 & is.null(positive)) positive <- rownames(data)[1] if(numLevels == 2 & !is.null(prevalence) && length(prevalence) != 1) stop("with two levels, one prevalence probability must be specified") if(numLevels > 2 & !is.null(prevalence) && length(prevalence) != numLevels) stop("the number of prevalence probability must be the same as the number of levels") if(numLevels > 2 & !is.null(prevalence) && is.null(names(prevalence))) stop("with >2 classes, the prevalence vector must have names") propCI <- function(x) { binom.test(sum(diag(x)), sum(x))$conf.int } propTest <- function(x) { out <- binom.test(sum(diag(x)), sum(x), p = max(apply(x, 2, sum)/sum(x)), alternative = "greater") unlist(out[c("null.value", "p.value")]) } overall <- c( unlist(e1071::classAgreement(data))[c("diag", "kappa")], propCI(data), propTest(data), mcnemar.test(data)$p.value) names(overall) <- c("Accuracy", "Kappa", "AccuracyLower", "AccuracyUpper", "AccuracyNull", "AccuracyPValue", "McnemarPValue") if(numLevels == 2) { if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data) negative <- classLevels[!(classLevels %in% positive)] tableStats <- c(sensitivity.table(data, positive), specificity.table(data, negative), posPredValue.table(data, positive, prevalence = prevalence), negPredValue.table(data, negative, prevalence = prevalence), prevalence, sum(data[positive, positive])/sum(data), sum(data[positive, ])/sum(data)) names(tableStats) <- c("Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value", "Prevalence", "Detection Rate", "Detection Prevalence") tableStats["Balanced Accuracy"] <- (tableStats["Sensitivity"]+tableStats["Specificity"])/2 } else { tableStats <- matrix(NA, nrow = length(classLevels), ncol = 8) for(i in seq(along = classLevels)) { pos <- classLevels[i] neg <- classLevels[!(classLevels %in% classLevels[i])] prev <- if(is.null(prevalence)) sum(data[, pos])/sum(data) else prevalence[pos] tableStats[i,] <- c(sensitivity.table(data, pos), specificity.table(data, neg), posPredValue.table(data, pos, prevalence = prev), negPredValue.table(data, neg, prevalence = prev), prev, sum(data[pos, pos])/sum(data), sum(data[pos, ])/sum(data), NA) tableStats[i,8] <- (tableStats[i,1] + tableStats[i,2])/2 } rownames(tableStats) <- paste("Class:", classLevels) colnames(tableStats) <- c("Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value", "Prevalence", "Detection Rate", "Detection Prevalence", "Balanced Accuracy") } structure(list( positive = positive, table = data, overall = overall, byClass = tableStats, dots = list(...)), class = "confusionMatrix") } as.matrix.confusionMatrix <- function(x, what = "xtabs", ...) { if(!(what %in% c("xtabs", "overall", "classes"))) stop("what must be either xtabs, overall or classes") out <- switch(what, xtabs = matrix(as.vector(x$table), nrow = length(colnames(x$table)), dimnames = list(rownames(x$table), colnames(x$table))), overall = as.matrix(x$overall), classes = as.matrix(x$byClass)) if(what == "classes") { if(length(colnames(x$table)) > 2) { out <- t(out) colnames(out) <- gsub("Class: ", "", colnames(out), fixed = TRUE) } } out } as.table.confusionMatrix <- function(x, ...) x$table confusionMatrix.train <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) { if(data$modelType == "Regression") stop("confusion matrices are only valid for classification models") if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'") if(data$control$method %in% c("oob", "LOOCV", "none")) stop("cannot compute confusion matrices for leave-one-out, out-of-bag resampling or no resampling") if(!is.null(data$control$index)) { resampleN <- unlist(lapply(data$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(data) } else { resampText <- "" numResamp <- 0 } lev <- levels(data) ## get only best tune names(data$bestTune) <- gsub("^\\.", "", names(data$bestTune)) resampledCM <- merge(data$bestTune, data$resampledCM) counts <- as.matrix(resampledCM[,grep("^cell", colnames(resampledCM))]) ## normalize by true class? if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x))) if(norm == "average") counts <- counts/numResamp overall <- matrix(apply(counts, 2, mean), nrow = length(lev)) rownames(overall) <- colnames(overall) <- lev if(norm != "none") overall <- overall*100 names(dimnames(overall)) <- dnn out <- list(table = as.table(overall), norm = norm, B = length(data$control$index), text = paste(resampText, "Confusion Matrix")) class(out) <- "confusionMatrix.train" out } print.confusionMatrix.train <- function(x, digits = 1, ...) { cat(x$text, "\n") normText <- switch(x$norm, none = "\n(entries are un-normalized counts)\n", average = "\n(entries are cell counts per resample)\n", overall = "\n(entries are percentages of table totals)\n", byClass = "\n(entries are percentages within the reference class)\n", "") cat(normText, "\n") if(x$norm == "none" & x$B == 1) print(confusionMatrix(x$table)) else print(round(x$table, digits)) cat("\n") invisible(x) } confusionMatrix.rfe <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) { if(is.null(data$resampledCM)) stop("resampled confusion matrices are not availible") if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'") if(data$control$method %in% c("oob", "LOOCV")) stop("cannot compute confusion matrices for leave-one-out and out-of-bag resampling") if(!is.null(data$control$index)) { resampleN <- unlist(lapply(data$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(data) } else { resampText <- "" numResamp <- 0 } resampledCM <- data$resampledCM counts <- as.matrix(resampledCM[,grep("^\\.cell", colnames(resampledCM))]) ## normalize by true class? if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x))) if(norm == "average") counts <- counts/numResamp overall <- matrix(apply(counts, 2, mean), nrow = length(data$obsLevels)) rownames(overall) <- colnames(overall) <- data$obsLevels overall <- overall*100 names(dimnames(overall)) <- dnn out <- list(table = overall, norm = norm, B = numResamp, text = paste(resampText, "Confusion Matrix")) class(out) <- "confusionMatrix.rfe" out } resampName <- function(x, numbers = TRUE) { if(!("control" %in% names(x))) return("") if(numbers) { resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) out <- switch(tolower(x$control$method), none = "None", timeslice = paste("Rolling Forecasting Origin Resampling (", x$control$horizon, " held-out with", ifelse(x$control$fixedWindow, " a ", " no "), "fixed window)", sep = ""), oob = "Out of Bag Resampling", boot =, boot632 = paste("Bootstrapped (", numResamp, " reps)", sep = ""), cv = paste("Cross-Validated (", x$control$number, " fold)", sep = ""), repeatedcv = paste("Cross-Validated (", x$control$number, " fold, repeated ", x$control$repeats, " times)", sep = ""), lgocv = paste("Repeated Train/Test Splits Estimated (", numResamp, " reps, ", round(x$control$p, 2), "%)", sep = ""), adaptive_boot = paste("Adaptively Bootstrapped (", numResamp, " reps)", sep = ""), adaptive_cv = paste("Adaptively Cross-Validated (", x$control$number, " fold, repeated ", x$control$repeats, " times)", sep = ""), adaptive_lgocv = paste("Adaptive Repeated Train/Test Splits Estimated (", numResamp, " reps, ", round(x$control$p, 2), "%)", sep = "") ) } else { out <- switch(tolower(x$control$method), none = "None", timeslice = "Rolling Forecasting Origin Resampling", oob = "Out of Bag Resampling", boot = "(Bootstrap)", boot632 = "(Bootstrap 632 Rule)", cv = "(Cross-Validation)", repeatedcv = "(Repeated Cross-Validation)", lgocv = "(Repeated Train/Test Splits)") } out } confusionMatrix.sbf <- function(data, norm = "overall", dnn = c("Prediction", "Reference"), ...) { if(is.null(data$resampledCM)) stop("resampled confusion matrices are not availible") if(!norm %in% c("none", "overall", "average")) stop("values for norm should be 'none', 'overall', 'byClass' or 'average'") if(data$control$method %in% c("oob", "LOOCV")) stop("cannot compute confusion matrices for leave-one-out and out-of-bag resampling") if(!is.null(data$control$index)) { resampleN <- unlist(lapply(data$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(data) } else { resampText <- "" numResamp <- 0 } resampledCM <- data$resampledCM counts <- as.matrix(resampledCM[,grep("^\\.cell", colnames(resampledCM))]) ## normalize by true class? if(norm == "overall") counts <- t(apply(counts, 1, function(x)x/sum(x))) if(norm == "average") counts <- counts/numResamp overall <- matrix(apply(counts, 2, mean), nrow = length(data$obsLevels)) rownames(overall) <- colnames(overall) <- data$obsLevels overall <- overall*100 names(dimnames(overall)) <- dnn out <- list(table = overall, norm = norm, B = numResamp, text = paste(resampText, "Confusion Matrix")) class(out) <- "confusionMatrix.sbf" out } print.confusionMatrix.rfe <- print.confusionMatrix.train print.confusionMatrix.sbf <- print.confusionMatrix.train mcc <- function(tab, pos = colnames(tab)[1]) { if(nrow(tab) != 2 | ncol(tab) != 2) stop("A 2x2 table is needed") neg <- colnames(tab)[colnames(tab) != pos] tp <- tab[pos, pos] tn <- tab[neg, neg] fp <- tab[pos,neg] fn <- tab[neg, pos] d1 <- tp + fp d2 <- tp + fn d3 <- tn + fp d4 <- tn + fn if(d1 == 0 | d2 == 0 | d3 == 0 | d4 == 0) return(0) ((tp * tn) - (fp * fn))/sqrt(d1*d2*d3*d4) } caret/R/pcaNNet.R0000644000175000017500000001001112516214271013672 0ustar rbalintrbalint# add model averaging? # check predict method with formula interface # how to handle variable imp? pcaNNet <- function (x, ...) UseMethod("pcaNNet") # this is a near copy of nnet.formula pcaNNet.formula <- function (formula, data, weights, ..., thresh = .99, subset, na.action, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) m$... <- m$contrasts <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] w <- model.weights(m) if (length(w) == 0) w <- rep(1, nrow(x)) y <- model.response(m) res <- pcaNNet.default(x, y, weights = w, thresh = thresh, ...) res$terms <- Terms res$coefnames <- colnames(x) res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) class(res) <- c("pcaNNet.formula", "pcaNNet") res } pcaNNet.default <- function(x, y, thresh = .99, ...) { requireNamespaceQuietStop("nnet") # check for no variance data isZV <- apply(x, 2, function(u) length(unique(u)) < 2) if(any(isZV)) { x <- x[,!isZV, drop = FALSE] xNames <- colnames(x) } else xNames <- NULL # get pca pp <- preProcess(x, "pca", thresh = thresh) x <- predict(pp, x) # check for factors if(is.factor(y)) { classLev <- levels(y) y <- class2ind(y) } else classLev <- NULL # fit nnet modelFit <- nnet::nnet(x, y, ...) modelFit$lev <- classLev # return results out <- list(model = modelFit, pc = pp, names = xNames) class(out) <- "pcaNNet" out } print.pcaNNet <- function (x, ...) { cat("Neural Network Model with PCA Pre-Processing\n\n") cat("Created from", x$pc$dim[1], "samples and", x$pc$dim[2], "variables\n") cat("PCA needed", x$pc$numComp, "components to capture", round(x$pc$thresh * 100, 2), "percent of the variance\n\n") print(x$model) cat("\n") invisible(x) } predict.pcaNNet <- function(object, newdata, type = c("raw", "class"), ...) { requireNamespaceQuietStop("nnet") if (!inherits(object, "pcaNNet")) stop("object not of class \"pcaNNet\"") if (missing(newdata)) { if(is.null(object$model$lev)) { return(fitted(object$model)) } else { scores <- fitted(object$model) classes <- colnames(scores)[apply(scores, 1, which.max)] classes <- factor(as.character(classes), levels = object$model$lev) if(type[1]== "raw") return(scores) else return(classes) } } else { if (inherits(object, "pcaNNet.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.omit, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) x <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] } else { if (is.null(dim(newdata))) dim(newdata) <- c(1, length(newdata)) x <- as.matrix(newdata) if (any(is.na(x))) stop("missing values in 'x'") keep <- 1:nrow(x) rn <- rownames(x) } } if(!is.null(object$names)) { x <- x[, object$names, drop = FALSE] } x <- predict(object$pc, x) predict(object$model, x, type = type, ...) } caret/R/bag.R0000644000175000017500000003666012516214271013115 0ustar rbalintrbalint"bag" <- function(x, ...) UseMethod("bag") bagControl <- function(fit = NULL, predict = NULL, aggregate = NULL, downSample = FALSE, oob = TRUE, allowParallel = TRUE) { list(fit = fit, predict = predict, aggregate = aggregate, downSample = downSample, oob = oob, allowParallel = allowParallel) } "bag.default" <- function(x, y, B = 10, vars = ncol(x), bagControl = NULL, ...) { funcCall <- match.call(expand.dots = TRUE) if(is.null(bagControl)) stop("Please specify 'bagControl' with the appropriate functions") if(!is.null(vars) && vars < 1) stop("vars must be an integer > 0") if(bagControl$downSample & is.numeric(y)) { warning("down-sampling with regression... downSample changed to FALSE") bagControl$downSample <- FALSE } if(is.null(bagControl$fit) | is.null(bagControl$predict) | is.null(bagControl$aggregate)) { stop("The control arguments 'fit', 'predict' and 'aggregate' should have non-NULL values") } fitter <- function(index, x, y, ctrl, v, ...) { subX <- x[index,, drop = FALSE] subY <- y[index] if(!is.null(v)) { if(v > ncol(x)) v <- ncol(x) subVars <- sample(1:ncol(subX), ceiling(v)) subX <- subX[, subVars, drop = FALSE] } else subVars <- NULL if(ctrl$downSample) { freaks <- table(subY) smallFreak <- min(freaks) splitUp <- split(seq(along = subY), subY) splitUp <- lapply(splitUp, sample, size = smallFreak) keepers <- unlist(splitUp) subX <- subX[keepers,,drop = FALSE] subY <- subY[keepers] } fit <- ctrl$fit(subX, subY, ...) if(ctrl$oob) { pred <- ctrl$predict(fit, x[-unique(index), subVars, drop = FALSE]) if(is.vector(pred)) { out <- data.frame(pred = pred, obs = y[-unique(index)]) } else { out <- as.data.frame(pred) out$obs <- y[-unique(index)] if(is.factor(y) & !(any(names(out) == "pred"))) { ## Try to detect class probs and make a pred factor if(all(levels(y) %in% names(out))) { pred <- apply(out[, levels(y)], 1, which.max) pred <- factor(levels(y)[pred], levels = levels(y)) out$pred <- pred } } } out$key <- paste(sample(letters, 10, replace = TRUE), collapse = "") } else out <- NULL list(fit = fit, vars = subVars, oob = out) } btSamples <- createResample(y, times = B) `%op%` <- if(bagControl$allowParallel) `%dopar%` else `%do%` btFits <- foreach(iter = seq(along = btSamples), .verbose = FALSE, .packages = "caret", .errorhandling = "stop") %op% fitter(btSamples[[iter]], x = x, y = y, ctrl = bagControl, v = vars, ...) structure( list(fits = btFits, control = bagControl, call = funcCall, B = B, vars = vars, smallClass = min(table(y)), dims = dim(x)), class = "bag") } "bag.formula" <- function (formula, data = NULL,..., subset, weights, na.action = na.omit) { funcCall <- match.call(expand.dots = TRUE) if (!inherits(formula, "formula")) stop("method is only for formula objects") m <- match.call(expand.dots = FALSE) mIndex <- match(c("formula", "data", "subset", "weights", "na.action"), names(m), 0) m <- m[c(1, mIndex)] m$... <- m$B <- m$vars <- m$bagControl <- NULL m$na.action <- na.action m[[1]] <- as.name("model.frame") m <- eval(m, parent.frame()) Terms <- attr(m, "terms") attr(Terms, "intercept") <- 0 y <- model.response(m) w <- model.weights(m) x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] out <- bag.default(x, y, ...) out$call <- funcCall out } "predict.bag" <- function(object, newdata = NULL, ...) { if(is.null(newdata)) stop("please provide a data set for prediction") predictor <- function(obj, x, ctrl) { if(!is.null(obj$vars)) x <- x[, obj$vars, drop = FALSE] pred <- ctrl$predict(obj$fit, x) } btPred <- lapply(object$fit, predictor, x = newdata, ctrl = object$control) object$control$aggregate(btPred, ...) } print.bag <- function (x, ...) { printCall(x$call) cat("\nB:", x$B,"\n") cat("Training data:", x$dims[2], "variables and", x$dims[1], "samples\n") cat(ifelse(is.null(x$vars) || x$dims[2] == x$vars, "All variables were used in each model", paste("Each model used", x$vars, "random", ifelse(x$vars == 1, "variable", "variables"), "predictors"))) cat('\n') if(x$control$downSample) { cat("Training data was down-sampled to balance the classes to", x$smallClass, "samples per class\n\n") } invisible(x) } "summary.bag" <- function(object, ...) { hasPred <- any(names(object$fits[[1]]$oob) == "pred") if(object$control$oob & hasPred) { ## to avoid a 'no visible binding for global variable' warning key <- NULL oobData <- lapply(object$fits, function(x) x$oob) oobData <- do.call("rbind", oobData) oobResults <- ddply(oobData, .(key), defaultSummary) oobResults$key <- NULL oobStat <- apply(oobResults, 2, function(x) quantile(x, na.rm = TRUE, probs = c(0, 0.025, .25, .5, .75, .975, 1))) rownames(oobStat) <- paste(format(as.numeric(format(gsub("%", "", rownames(oobStat))))), "%", sep = "") B <- nrow(oobResults) } else { oobStat <- NULL B <- NULL } out <- list(oobStat = oobStat, call = object$call, B = B) class(out) <- "summary.bag" out } "print.summary.bag" <- function(x, digits = max(3, getOption("digits") - 3), ...) { printCall(x$call) if(!is.null(x$oobStat)) { cat("Out of bag statistics (B = ", x$B, "):\n\n", sep = "") print(x$oobStat, digits = digits) } else cat("No out of bag statistics\n") cat("\n") } ldaBag <- list(fit = function(x, y, ...) { loadNamespace("MASS") MASS::lda(x, y, ...) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) predict(object, x)$posterior }, aggregate = function(x, type = "class") { ## The class probabilities come in as a list of matrices ## For each class, we can pool them then average over them pooled <- x[[1]] * NA n <- nrow(pooled) classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } pooled <- apply(pooled, 1, function(x) x/sum(x)) if(n != nrow(pooled)) pooled <- t(pooled) if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) out }) plsBag <- list(fit = function(x, y, ...) { loadNamespace("pls") caret::plsda(x, y, ...) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) predict(object, x, type = "prob")[,,] }, aggregate = function(x, type = "class") { pooled <- x[[1]] * NA classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) out }) nbBag <- list(fit = function(x, y, ...) { loadNamespace("klaR") klaR::NaiveBayes(x, y, usekernel = TRUE, fL = 2, ...) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) as.data.frame(predict(object, x)$posterior) }, aggregate = function(x, type = "class") { pooled <- x[[1]] * NA classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) out }) ctreeBag <- list(fit = function(x, y, ...) { loadNamespace("party") data <- as.data.frame(x) data$y <- y party::ctree(y~., data = data) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) obsLevels <- levels(object@data@get("response")[,1]) if(!is.null(obsLevels)) { rawProbs <- party::treeresponse(object, x) probMatrix <- matrix(unlist(rawProbs), ncol = length(obsLevels), byrow = TRUE) out <- data.frame(probMatrix) colnames(out) <- obsLevels rownames(out) <- NULL } else out <- unlist(party::treeresponse(object, x)) out }, aggregate = function(x, type = "class") { if(is.matrix(x[[1]]) | is.data.frame(x[[1]])) { pooled <- x[[1]] & NA classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) } else { x <- matrix(unlist(x), ncol = length(x)) out <- apply(x, 1, median) } out }) svmBag <- list(fit = function(x, y, ...) { loadNamespace("kernlab") out <- kernlab::ksvm(as.matrix(x), y, prob.model = is.factor(y), ...) out }, pred = function(object, x) { if(is.character(lev(object))) { out <- predict(object, as.matrix(x), type = "probabilities") colnames(out) <- lev(object) rownames(out) <- NULL } else out <- predict(object, as.matrix(x))[,1] out }, aggregate = function(x, type = "class") { if(is.matrix(x[[1]]) | is.data.frame(x[[1]])) { pooled <- x[[1]] & NA classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) } else { x <- matrix(unlist(x), ncol = length(x)) out <- apply(x, 1, median) } out }) nnetBag <- list(fit = function(x, y, ...) { loadNamespace("nnet") factorY <- is.factor(y) if(factorY) y <- class2ind(y) out <- nnet::nnet(x, y, linout = !factorY, trace = FALSE, ...) out$classification <- factorY out }, pred = function(object, x) { out <- predict(object, x, type= "raw") if(object$classification) { colnames(out) <- colnames(object$fitted.values) rownames(out) <- NULL } else out <- predict(object, x, type= "raw")[,1] out }, aggregate = function(x, type = "class") { if(is.matrix(x[[1]]) | is.data.frame(x[[1]])) { pooled <- x[[1]] & NA classes <- colnames(pooled) for(i in 1:ncol(pooled)) { tmp <- lapply(x, function(y, col) y[,col], col = i) tmp <- do.call("rbind", tmp) pooled[,i] <- apply(tmp, 2, median) } if(type == "class") { out <- factor(classes[apply(pooled, 1, which.max)], levels = classes) } else out <- as.data.frame(pooled) } else { x <- matrix(unlist(x), ncol = length(x)) out <- apply(x, 1, median) } out }) caret/R/createTimeSlices.R0000644000175000017500000000264612516214271015606 0ustar rbalintrbalint## From Tony Cooper on 1/9/13 createTimeSlices <- function(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0) { ## initialwindowlength = initial number of consecutive values in each training set sample ## horizonlength = number of consecutive values in test set sample ## fixedwindowlength = FALSE if we use the maximum possible length for the training set ## Ensure that initialwindowlength + horizonlength <= length(y) stops <- (seq(along = y))[initialWindow:(length(y) - horizon)] if (fixedWindow) { starts <- stops - initialWindow + 1 } else { starts <- rep(1, length(stops)) # all start at 1 } train <- mapply(seq, starts, stops, SIMPLIFY = FALSE) test <- mapply(seq, stops+1, stops+horizon, SIMPLIFY = FALSE) names(train) <- paste("Training", gsub(" ", "0", format(seq(along = train))), sep = "") names(test) <- paste("Testing", gsub(" ", "0", format(seq(along = test))), sep = "") thin <- function(x, skip = 2) { n <- length(x) x[seq(1, n, by = skip)] } if(skip > 0) { train <- thin(train, skip = skip+1) test <- thin(test, skip = skip+1) } out <- list(train = train, test = test) out } ## This will reproduce the 4 examples above #createTimeSlices(1:9, 5, 1, fixedWindow = FALSE) #createTimeSlices(1:9, 5, 1, fixedWindow = TRUE) #createTimeSlices(1:9, 5, 3, fixedWindow = TRUE) #createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)caret/R/bagEarth.R0000644000175000017500000001331712453550064014076 0ustar rbalintrbalint"bagEarth" <- function(x, ...) UseMethod("bagEarth") "bagEarth.default" <- function(x, y, weights = NULL, B = 50, summary = mean, keepX = TRUE, ...) { requireNamespaceQuietStop("earth") funcCall <- match.call(expand.dots = TRUE) if(!is.matrix(x)) x <- as.matrix(x) if(!is.factor(y)) { if(!is.vector(y)) y <- as.vector(y) if(!is.vector(y)) y <- y[,1] } if(is.null(weights)) weights <- rep(1, dim(x)[1]) if(is.factor(y)) { lev <- levels(y) theDots <- list(...) if(all(names(theDots) != "glm")) stop("must declare a binomal glm using the glm argument to earth") #if(theDots$glm$family$family != "binomial") stop("must use binomial glm for factors") } else { lev <- NA } foo <- function(index, x, y, w, ...) { subX <- x[index,, drop = FALSE] subY <- y[index] subW <- weights[index] fit <- earth::earth(subX, subY, subW, ...) fit$index <- index fit } oobFoo <- function(fit, x, y, lev) { index <- fit$index subX <- x[-index,, drop = FALSE] subY <- y[-index] ## todo predY <- if(is.null(fit$levels)) predict(fit, subX) else predict(fit, subX, type = "class") postResample(predY, subY) } btSamples <- createResample(y, times = B) btFits <- lapply(btSamples, foo, x = x, y = y, w = weights, ...) oobList <- lapply(btFits, oobFoo, x = x, y = y, lev = lev) oob <- matrix(unlist(oobList), ncol = length(oobList[[1]]), byrow = TRUE) colnames(oob) <- names(oobList[[1]]) if(keepX) x <- x else x <- NULL structure( list(fit = btFits, B = B, oob = oob, summary = summary, call = funcCall, levels = lev, x = x), class = "bagEarth") } "bagEarth.formula" <- function (formula, data = NULL, B = 50, summary = mean, keepX = TRUE, ..., subset, weights, na.action = na.omit) { funcCall <- match.call(expand.dots = TRUE) if (!inherits(formula, "formula")) stop("method is only for formula objects") m <- match.call(expand.dots = FALSE) mIndex <- match(c("formula", "data", "subset", "weights", "na.action"), names(m), 0) m <- m[c(1, mIndex)] m$... <- NULL m$na.action <- na.action m[[1]] <- as.name("model.frame") m <- eval(m, parent.frame()) Terms <- attr(m, "terms") attr(Terms, "intercept") <- 0 y <- model.response(m) w <- model.weights(m) x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] out <- bagEarth.default(x, y, w, B = B, summary = summary, keepX = keepX, ...) out$call <- funcCall out } "predict.bagEarth" <- function(object, newdata = NULL, type = "response", ...) { if(!any(type %in% c("response", "class", "prob"))) stop("type must be either response, class or prob") requireNamespaceQuietStop("earth") ## get oob predictions getTrainPred <- function(x) { oobIndex <- seq(along = x$fitted.values) oobIndex <- oobIndex[!(oobIndex %in% unique(x$index))] data.frame(pred = x$fitted.values[oobIndex], sample = oobIndex) } if(is.null(newdata) & !is.null(object$x)) newdata <- object$x if(is.null(newdata)) { pred <- lapply(object$fit, getTrainPred) pred <- rbind.fill(pred) out <- ddply(pred, .(sample), function(x) object$summary(x$pred))$V1 } else { pred <- lapply(object$fit, function(x, y) { if(is.null(x$glm.list)) predict(x, newdata = y) else predict(x, newdata = y, type = "response") }, y = newdata ) out <- matrix(unlist(pred), ncol = object$B) out <- apply(out, 1, object$summary, na.rm = TRUE) } if(!all(is.na(object$levels))) { if(type == "prob") { out <- cbind(1 - out, out) colnames(out) <- object$levels } else { if(type == "class") { out <- factor(ifelse(out > .5, object$levels[2], object$levels[1]), levels = object$levels) } } } out } print.bagEarth <- function (x, ...) { cat("\nCall:\n", deparse(x$call), "\n\n", sep = "") if(!is.null(x$x))cat("Data:\n # variables:\t", dim(x$x)[2], "\n # samples:\t", dim(x$x)[1], "\n") cat("\nB:", x$B,"\n") invisible(x) } "summary.bagEarth" <- function(object, ...) { requireNamespaceQuietStop("earth") oobStat <- apply(object$oob, 2, function(x) quantile(x, probs = c(0, 0.025, .25, .5, .75, .975, 1))) numTerms <- unlist(lapply(object$fit, function(x) length(x$selected.terms))) numVar <- unlist(lapply( object$fit, function(x) { imp <- rownames(earth::evimp(x, trim = FALSE)) imp <- imp[!grepl("-unused", imp)] imp })) modelInfo <- cbind(numTerms, numVar) colnames(modelInfo) <- c("Num Terms", "Num Variables") out <- list(modelInfo = modelInfo, oobStat = oobStat, bagEarthCall = object$call) class(out) <- "summary.bagEarth" out } "print.summary.bagEarth" <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall:\n", deparse(x$bagEarthCall), "\n\n", sep = "") oobStat <- apply(x$oob, 2, function(x) quantile(x, probs = c(0, 0.025, .5, .975, 1))) cat("Out of bag statistics:\n\n") print(x$oobStat, digits = digits) cat("\nModel Selection Statistics:\n\n") print(summary(x$modelInfo)) cat("\n") } caret/R/selection.R0000644000175000017500000000220512453550064014340 0ustar rbalintrbalint ## In these functions, x is the data fram of performance values and tuning parameters. oneSE <- function(x, metric, num, maximize) { index <- 1:nrow(x) if(!maximize) { bestIndex <- which.min(x[,metric]) perf <- x[bestIndex,metric] + (x[bestIndex,paste(metric, "SD", sep = "")])/sqrt(num) candidates <- index[x[, metric] <= perf] bestIter <- min(candidates) } else { bestIndex <- which.max(x[,metric]) perf <- x[bestIndex,metric] - (x[bestIndex,paste(metric, "SD", sep = "")])/sqrt(num) candidates <- index[x[, metric] >= perf] bestIter <- min(candidates) } bestIter } tolerance <- function(x, metric, tol = 1.5, maximize) { index <- 1:nrow(x) if(!maximize) { best <- min(x[,metric]) perf <- (x[,metric] - best)/best * 100 candidates <- index[perf < tol] bestIter <- min(candidates) } else { best <- max(x[,metric]) perf <- (x[,metric] - best)/best * -100 candidates <- index[perf < tol] bestIter <- min(candidates) } bestIter } caret/R/findLinearCombos.R0000644000175000017500000000456212453550064015601 0ustar rbalintrbalint# enumerate linear combinations enumLC <- function(object, ...) UseMethod("enumLC") enumLC.default <- function(object, ...) { # there doesn't seem to be a reasonable default, so # we'll throw an error stop(paste('enumLC does not support ', class(object), 'objects')) } enumLC.matrix <- function(object, ...) { # factor the matrix using QR decomposition and then process it internalEnumLC(qr(object)) } enumLC.lm <- function(object, ...) { # extract the QR decomposition and the process it internalEnumLC(object$qr) } enumLC.formula <- function(object, ...) { # create an lm fit object from the formula, and then call # appropriate enumLC method enumLC(lm(object)) } # this function does the actual work for all of the enumLC methods internalEnumLC <- function(qrObj, ...) { R <- qr.R(qrObj) # extract R matrix numColumns <- dim(R)[2] # number of columns in R rank <- qrObj$rank # number of independent columns pivot <- qrObj$pivot # get the pivot vector if (is.null(numColumns) || rank == numColumns) { list() # there are no linear combinations } else { p1 <- 1:rank X <- R[p1, p1] # extract the independent columns Y <- R[p1, -p1, drop = FALSE] # extract the dependent columns b <- qr(X) # factor the independent columns b <- qr.coef(b, Y) # get regression coefficients of # the dependent columns b[abs(b) < 1e-6] <- 0 # zap small values # generate a list with one element for each dependent column lapply(1:dim(Y)[2], function(i) c(pivot[rank + i], pivot[which(b[,i] != 0)])) } } findLinearCombos <- function(x) { if(!is.matrix(x)) x <- as.matrix(x) lcList <- enumLC(x) initialList <- lcList badList <- NULL if(length(lcList) > 0) { continue <- TRUE while(continue) { # keep removing linear dependencies until it resolves tmp <- unlist(lapply(lcList, function(x) x[1])) tmp <- unique(tmp[!is.na(tmp)]) badList <- unique(c(tmp, badList)) lcList <- enumLC(x[,-badList]) continue <- (length(lcList) > 0) } } else badList <- NULL list(linearCombos = initialList, remove = badList) } caret/R/rfe.R0000644000175000017500000007703612516214271013142 0ustar rbalintrbalint rfeIter <- function(x, y, testX, testY, sizes, rfeControl = rfeControl(), label = "", seeds = NA, ...) { if(is.null(colnames(x))) stop("x must have column names") if(is.null(testX) | is.null(testY)) stop("a test set must be specified") if(is.null(sizes)) stop("please specify the number of features") predictionMatrix <- matrix(NA, nrow = length(testY), ncol = length(sizes)) p <- ncol(x) retained <- colnames(x) sizeValues <- sort(unique(c(sizes, ncol(x))), decreasing = TRUE) sizeText <- format(sizeValues) finalVariables <- vector(length(sizeValues), mode = "list") for(k in seq(along = sizeValues)) { if(!any(is.na(seeds))) set.seed(seeds[k]) if(rfeControl$verbose) { cat("+(rfe) fit", ifelse(label != "", label, ""), "size:", sizeText[k], "\n") } flush.console() fitObject <- rfeControl$functions$fit(x[,retained,drop = FALSE], y, first = p == ncol(x[,retained,drop = FALSE]), last = FALSE, ...) if(rfeControl$verbose) { cat("-(rfe) fit", ifelse(label != "", label, ""), "size:", sizeText[k], "\n") } modelPred <- rfeControl$functions$pred(fitObject, testX[,retained,drop = FALSE]) if(is.data.frame(modelPred) | is.matrix(modelPred)) { if(is.matrix(modelPred)) modelPred <- as.data.frame(modelPred) modelPred$obs <- testY modelPred$Variables <- sizeValues[k] } else modelPred <- data.frame(pred = modelPred, obs = testY, Variables = sizeValues[k]) ## save as a vector and rbind at end rfePred <- if(k == 1) modelPred else rbind(rfePred, modelPred) if(!exists("modImp")) ##todo: get away from this since it finds object in other spaces { if(rfeControl$verbose) { cat("+(rfe) imp", ifelse(label != "", label, ""), "\n") } modImp <- rfeControl$functions$rank(fitObject, x[,retained,drop = FALSE], y) if(rfeControl$verbose) { cat("-(rfe) imp", ifelse(label != "", label, ""), "\n") } } else { if(rfeControl$rerank) { if(rfeControl$verbose) { cat("+(rfe) imp", ifelse(label != "", label, ""), "size:", sizeText[k], "\n") } modImp <- rfeControl$functions$rank(fitObject, x[,retained,drop = FALSE], y) if(rfeControl$verbose) { cat("-(rfe) imp", ifelse(label != "", label, ""), "size:", sizeText[k], "\n") } } } if(nrow(modImp) < sizeValues[k]) stop(paste("rfe is expecting", sizeValues[k], "importance values but only has", nrow(modImp))) if(any(!complete.cases(modImp))) stop("There were missing importance values") finalVariables[[k]] <- subset(modImp, var %in% retained) finalVariables[[k]]$Variables <- sizeValues[[k]] if(k < length(sizeValues)) retained <- as.character(modImp$var)[1:sizeValues[k+1]] } list(finalVariables = finalVariables, pred = rfePred) } ###################################################################### ###################################################################### rfe <- function (x, ...) UseMethod("rfe") "rfe.default" <- function(x, y, sizes = 2^(2:4), metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), rfeControl = rfeControl(), ...) { startTime <- proc.time() funcCall <- match.call(expand.dots = TRUE) if(!("caret" %in% loadedNamespaces())) loadNamespace("caret") if(nrow(x) != length(y)) stop("there should be the same number of samples in x and y") numFeat <- ncol(x) classLevels <- levels(y) if(is.null(rfeControl$index)) rfeControl$index <- switch(tolower(rfeControl$method), cv = createFolds(y, rfeControl$number, returnTrain = TRUE), repeatedcv = createMultiFolds(y, rfeControl$number, rfeControl$repeats), loocv = createFolds(y, length(y), returnTrain = TRUE), boot =, boot632 = createResample(y, rfeControl$number), test = createDataPartition(y, 1, rfeControl$p), lgocv = createDataPartition(y, rfeControl$number, rfeControl$p)) if(is.null(names(rfeControl$index))) names(rfeControl$index) <- prettySeq(rfeControl$index) if(is.null(rfeControl$indexOut)){ rfeControl$indexOut <- lapply(rfeControl$index, function(training, allSamples) allSamples[-unique(training)], allSamples = seq(along = y)) names(rfeControl$indexOut) <- prettySeq(rfeControl$indexOut) } sizes <- sort(unique(sizes)) sizes <- sizes[sizes <= ncol(x)] ## check summary function and metric testOutput <- data.frame(pred = sample(y, min(10, length(y))), obs = sample(y, min(10, length(y)))) if(is.factor(y)) { for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput)) } test <- rfeControl$functions$summary(testOutput, lev = classLevels) perfNames <- names(test) if(!(metric %in% perfNames)) { warning(paste("Metric '", metric, "' is not created by the summary function; '", perfNames[1], "' will be used instead", sep = "")) metric <- perfNames[1] } ## Set or check the seeds when needed totalSize <- if(any(sizes == ncol(x))) length(sizes) else length(sizes) + 1 if(is.null(rfeControl$seeds)) { seeds <- vector(mode = "list", length = length(rfeControl$index)) seeds <- lapply(seeds, function(x) sample.int(n = 1000000, size = totalSize)) seeds[[length(rfeControl$index) + 1]] <- sample.int(n = 1000000, size = 1) rfeControl$seeds <- seeds } else { if(!(length(rfeControl$seeds) == 1 && is.na(rfeControl$seeds))) { ## check versus number of tasks numSeeds <- unlist(lapply(rfeControl$seeds, length)) badSeed <- (length(rfeControl$seeds) < length(rfeControl$index) + 1) || (any(numSeeds[-length(numSeeds)] < totalSize)) if(badSeed) stop(paste("Bad seeds: the seed object should be a list of length", length(rfeControl$index) + 1, "with", length(rfeControl$index), "integer vectors of size", totalSize, "and the last list element having a", "single integer")) } } if(rfeControl$method == "LOOCV") { tmp <- looRfeWorkflow(x, y, sizes, ppOpts = NULL, ctrl = rfeControl, lev = classLevels, ...) selectedVars <- do.call("c", tmp$everything[names(tmp$everything) == "finalVariables"]) selectedVars <- do.call("rbind", selectedVars) externPerf <- tmp$performance } else { tmp <- nominalRfeWorkflow(x, y, sizes, ppOpts = NULL, ctrl = rfeControl, lev = classLevels, ...) selectedVars <- do.call("rbind", tmp$everything[names(tmp$everything) == "selectedVars"]) resamples <- do.call("rbind", tmp$everything[names(tmp$everything) == "resamples"]) rownames(resamples) <- NULL externPerf <- tmp$performance } rownames(selectedVars) <- NULL bestSubset <- rfeControl$functions$selectSize(x = externPerf, metric = metric, maximize = maximize) bestVar <- rfeControl$functions$selectVar(selectedVars, bestSubset) finalTime <- system.time( fit <- rfeControl$functions$fit(x[, bestVar, drop = FALSE], y, first = FALSE, last = TRUE, ...)) if(is.factor(y) & any(names(tmp$performance) == ".cell1")) { keepers <- c("Resample", "Variables", grep("\\.cell", names(tmp$performance), value = TRUE)) resampledCM <- subset(tmp$performance, Variables == bestSubset) tmp$performance <- tmp$performance[, -grep("\\.cell", names(tmp$performance))] } else resampledCM <- NULL if(!(rfeControl$method %in% c("LOOCV"))) { resamples <- switch(rfeControl$returnResamp, none = NULL, all = resamples, final = subset(resamples, Variables == bestSubset)) } else resamples <- NULL endTime <- proc.time() times <- list(everything = endTime - startTime, final = finalTime) ######################################################################### ## Now, based on probability or static ranking, figure out the best vars ## and the best subset size and fit final model out <- structure( list( pred = if(rfeControl$saveDetails) do.call("rbind", tmp$everything[names(tmp$everything) == "predictions"]) else NULL, variables = selectedVars, results = as.data.frame(externPerf), bestSubset = bestSubset, fit = fit, optVariables = bestVar, optsize = bestSubset, call = funcCall, control = rfeControl, resample = resamples, metric = metric, maximize = maximize, perfNames = perfNames, times = times, resampledCM = resampledCM, obsLevels = classLevels, dots = list(...)), class = "rfe") if(rfeControl$timingSamps > 0) { out$times$prediction <- system.time(predict(out, x[1:min(nrow(x), rfeControl$timingSamps),,drop = FALSE])) } else out$times$prediction <- rep(NA, 3) out } rfe.formula <- function (form, data, ..., subset, na.action, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) m$... <- m$contrasts <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] y <- model.response(m) res <- rfe(as.data.frame(x), y, ...) res$terms <- Terms res$coefnames <- colnames(x) res$call <- match.call() res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) class(res) <- c("rfe", "rfe.formula") res } ###################################################################### ###################################################################### print.rfe <- function(x, top = 5, digits = max(3, getOption("digits") - 3), ...) { cat("\nRecursive feature selection\n\n") resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(x) cat("Outer resampling method:", resampText, "\n") cat("\nResampling performance over subset size:\n\n") x$results$Selected <- "" x$results$Selected[x$results$Variables == x$bestSubset] <- "*" print(format(x$results, digits = digits), row.names = FALSE) cat("\n") cat("The top ", min(top, x$bestSubset), " variables (out of ", x$bestSubset, "):\n ", paste(x$optVariables[1:min(top, x$bestSubset)], collapse = ", "), "\n\n", sep = "") invisible(x) } ###################################################################### ###################################################################### plot.rfe <- function (x, metric = x$metric, ...) { x$results$Selected <- "" x$results$Selected[x$results$Variables == x$bestSubset] <- "*" results <- x$results[, colnames(x$results) %in% c("Variables", "Selected", metric)] metric <- metric[which(metric %in% colnames(results))] plotForm <- as.formula(paste(metric, "~ Variables")) panel.profile <- function(x, y, groups, ...) { panel.xyplot(x, y, ...) panel.xyplot(x[groups == "*"], y[groups == "*"], pch = 16) } resampText <- resampName(x, FALSE) resampText <- paste(metric, resampText) out <- xyplot(plotForm, data = results, groups = Selected, panel = panel.profile, ylab = resampText, ...) out } ###################################################################### ###################################################################### rfeControl <- function(functions = NULL, rerank = FALSE, method = "boot", saveDetails = FALSE, number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25), repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number), verbose = FALSE, returnResamp = "final", p = .75, index = NULL, indexOut = NULL, timingSamps = 0, seeds = NA, allowParallel = TRUE) { list( functions = if(is.null(functions)) caretFuncs else functions, rerank = rerank, method = method, saveDetails = saveDetails, number = number, repeats = repeats, returnResamp = returnResamp, verbose = verbose, p = p, index = index, indexOut = indexOut, timingSamps = timingSamps, seeds = seeds, allowParallel = allowParallel) } ###################################################################### ###################################################################### ## some built-in functions for certain models pickSizeBest <- function(x, metric, maximize) { best <- if(maximize) which.max(x[,metric]) else which.min(x[,metric]) min(x[best, "Variables"]) } pickSizeTolerance <- function(x, metric, tol = 1.5, maximize) { if(!maximize) { best <- min(x[,metric]) perf <- (x[,metric] - best)/best * 100 flag <- perf <= tol } else { best <- max(x[,metric]) perf <- (best - x[,metric])/best * 100 flag <- perf <= tol } min(x[flag, "Variables"]) } pickVars <- function(y, size) { finalImp <- ddply(y[, c("Overall", "var")], .(var), function(x) mean(x$Overall, na.rm = TRUE)) names(finalImp)[2] <- "Overall" finalImp <- finalImp[order(finalImp$Overall, decreasing = TRUE),] as.character(finalImp$var[1:size]) } caretFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) train(x, y, ...), pred = function(object, x) { tmp <- predict(object, x) if(object$modelType == "Classification" & !is.null(object$modelInfo$prob)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, rank = function(object, x, y) { vimp <- varImp(object, scale = FALSE)$importance if(object$modelType == "Regression") { vimp <- vimp[ order(vimp[,1], decreasing = TRUE) ,,drop = FALSE] } else { if(all(levels(y) %in% colnames(vimp))) { avImp <- apply(vimp[, levels(y), drop = TRUE], 1, mean) vimp$Overall <- avImp } } vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars ) ## write a better imp sort function ldaFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { loadNamespace("MASS") MASS::lda(x, y, ...) }, pred = function(object, x) { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp$class), as.data.frame(tmp$posterior)) out }, rank = function(object, x, y) { vimp <- filterVarImp(x, y, TRUE) vimp$Overall <- apply(vimp, 1, mean) vimp <- vimp[ order(vimp$Overall, decreasing = TRUE) ,] vimp <- as.data.frame(vimp)[, "Overall",drop = FALSE] vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars ) treebagFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { loadNamespace("ipred") ipred::ipredbagg(y, x, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, rank = function(object, x, y) { vimp <- varImp(object) vimp <- vimp[ order(vimp$Overall, decreasing = TRUE) ,,drop = FALSE] vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars) gamFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { loaded <- search() gamLoaded <- any(loaded == "package:gam") if(gamLoaded) detach(package:gam) loadNamespace("mgcv") dat <- if(is.data.frame(x)) x else as.data.frame(x) dat$y <- y args <- list(formula = gamFormula(x, smoother = "s", y = "y"), data = dat, family = if(!is.factor(y)) gaussian else binomial) do.call("gam", args) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) loaded <- search() gamLoaded <- any(loaded == "package:gam") if(gamLoaded) detach(package:gam) loadNamespace("mgcv") rsp <- predict(object, newdata = x, type = "response") if(object$family$family == "binomial") { lvl <- levels(object$model$y) out <- data.frame(p1 = rsp, p2 = 1-rsp, pred = factor(ifelse(rsp > .5, lvl[2], lvl[1]), levels = lvl)) colnames(out)[1:2] <- make.names(lvl) out } else out <- data.frame(pred = rsp) out }, rank = function(object, x, y) { loaded <- search() gamLoaded <- any(loaded == "package:gam") if(gamLoaded) detach(package:gam) loadNamespace("mgcv") vimp <- varImp(object) vimp$var <- rownames(vimp) if(any(!(colnames(x) %in% rownames(vimp)))) { missing <- colnames(x)[!(colnames(x) %in% rownames(vimp))] tmpdf <- data.frame(var = missing, Overall = rep(0, length(missing))) vimp <- rbind(vimp, tmpdf) } vimp }, selectSize = pickSizeBest, selectVar = pickVars) rfFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { loadNamespace("randomForest") randomForest::randomForest(x, y, importance = first, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, rank = function(object, x, y) { vimp <- varImp(object) if(is.factor(y)) { if(all(levels(y) %in% colnames(vimp))) { avImp <- apply(vimp[, levels(y), drop = TRUE], 1, mean) vimp$Overall <- avImp } } vimp <- vimp[ order( vimp$Overall, decreasing = TRUE) ,, drop = FALSE] vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars) lmFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { tmp <- if(is.data.frame(x)) x else as.data.frame(x) tmp$y <- y lm(y~., data = tmp) }, pred = function(object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) predict(object, x) }, rank = function(object, x, y) { vimp <- varImp(object, scale = FALSE) vimp <- vimp[ order( vimp$Overall, decreasing = TRUE) ,, drop = FALSE] vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars) nbFuncs <- list(summary = defaultSummary, fit = function(x, y, first, last, ...) { loadNamespace("klaR") klaR::NaiveBayes(x, y, usekernel = TRUE, fL = 2, ...) }, pred = function(object, x) { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp$class), as.data.frame(tmp$posterior)) out }, rank = function(object, x, y) { vimp <- filterVarImp(x, y) if(is.factor(y)) { avImp <- apply(vimp, 1, mean) vimp$Overall <- avImp } vimp <- vimp[ order( vimp$Overall, decreasing = TRUE) ,, drop = FALSE] vimp$var <- rownames(vimp) vimp }, selectSize = pickSizeBest, selectVar = pickVars) lrFuncs <- ldaFuncs lrFuncs$fit <- function (x, y, first, last, ...) { tmp <- if(is.data.frame(x)) x else as.data.frame(x) tmp$Class <- y glm(Class ~ ., data = tmp, family = "binomial") } lrFuncs$pred <- function (object, x) { if(!is.data.frame(x)) x <- as.data.frame(x) lvl <- levels(object$data$Class) tmp <- predict(object, x, type = "response") out <- data.frame(1-tmp, tmp) colnames(out) <- lvl out$pred <- factor(ifelse(tmp > .5, lvl[2], lvl[1]), levels = lvl) out } lrFuncs$rank <- function (object, x, y) { vimp <- varImp(object, scale = FALSE) vimp <- vimp[order(vimp$Overall, decreasing = TRUE),, drop = FALSE] vimp$var <- rownames(vimp) vimp } ###################################################################### ###################################################################### ## lattice functions densityplot.rfe <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) data$Variable <- factor(data$Variable, levels = paste(sort(unique(data$Variable)))) form <- as.formula(paste("~", metric, "|Variable")) densityplot(form, data = data, ...) } histogram.rfe <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) data$Variable <- factor(data$Variable, levels = paste(sort(unique(data$Variable)))) form <- as.formula(paste("~", metric, "|Variable")) histogram(form, data = data, ...) } stripplot.rfe <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) data$Variable <- factor(data$Variable, levels = paste(sort(unique(data$Variable)))) theDots <- list(...) if(any(names(theDots) == "horizontal")) { formText <- if(theDots$horizontal) paste("Variable ~", metric) else paste(metric, "~ Variable") } else formText <- paste("Variable ~", metric) form <- as.formula(formText) stripplot(form, data = data, ...) } xyplot.rfe <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) form <- as.formula(paste(metric, " ~ Variables")) xyplot(form, data = data, ...) } ###################################################################### ###################################################################### ## other functions predictors.rfe <- function(x, ...) x$optVariables varImp.rfe <- function(object, drop = FALSE, ...) { imp <- subset(object$variables, Variables == object$optsize) imp <- ddply(imp[, c("Overall", "var")], .(var), function(x) mean(x$Overall, rm.na = TRUE)) names(imp)[2] <- "Overall" if(drop) imp <- subset(imp, var %in% object$optVar) rownames(imp) <- imp$var imp$var <- NULL imp[order(-imp$Overall),,drop = FALSE] } predict.rfe <- function(object, newdata, ...) { if(length(list(...)) > 0) warning("... are ignored for predict.rfe") if(inherits(object, "rfe.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.omit, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) newdata <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(newdata), nomatch = 0) if (xint > 0) newdata <- newdata[, -xint, drop = FALSE] } checkCols <- object$optVar %in% colnames(newdata) if(!all(checkCols)) stop(paste("missing columns from newdata:", paste(object$optVar[!checkCols], collapse = ", "))) newdata <- newdata[, object$optVar, drop = FALSE] object$control$functions$pred(object$fit, newdata) } update.rfe <- function(object, x, y, size, ...) { size <- size[1] selectedVars <- object$variables bestVar <- object$control$functions$selectVar(selectedVars, size) object$fit <- object$control$functions$fit(x[, bestVar, drop = FALSE], y, first = FALSE, last = TRUE, ...) object$bestSubset <- size object$bestVar <- bestVar if(object$control$returnResamp == "final") { warning("The saved resamples are no longer appropriate and were removed") object$resampledCM <- object$resample <- NULL } object } caret/R/nearZeroVar.R0000644000175000017500000000621112453550064014612 0ustar rbalintrbalintnearZeroVar <- function (x, freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE, foreach = FALSE, allowParallel = TRUE) { if(!foreach) return(nzv(x, freqCut = freqCut, uniqueCut = uniqueCut, saveMetrics = saveMetrics)) `%op%` <- getOper(foreach && allowParallel && getDoParWorkers() > 1) if(saveMetrics) { res <- foreach(name = colnames(x), .combine=rbind) %op% { r <- nzv(x[[name]], freqCut = freqCut, uniqueCut = uniqueCut, saveMetrics = TRUE) r[,"column" ] <- name r } res <- res[, c(5, 1, 2, 3, 4)] rownames(res) <- as.character(res$column) res$column <- NULL } else { res <- foreach(name = colnames(x), .combine=c) %op% { nzv(x[[name]], freqCut = freqCut, uniqueCut = uniqueCut, saveMetrics = FALSE) } } res } nzv <- function (x, freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE) { if (is.vector(x)) x <- matrix(x, ncol = 1) freqRatio <- apply(x, 2, function(data) { t <- table(data[!is.na(data)]) if (length(t) <= 1) { return(0); } w <- which.max(t); return(max(t, na.rm=TRUE)/max(t[-w], na.rm=TRUE)) }) lunique <- apply(x, 2, function(data) length(unique(data[!is.na(data)]))) percentUnique <- 100 * lunique / apply(x, 2, length) zeroVar <- (lunique == 1) | apply(x, 2, function(data) all(is.na(data))) if (saveMetrics) { out <- data.frame(freqRatio = freqRatio, percentUnique = percentUnique, zeroVar = zeroVar, nzv = (freqRatio > freqCut & percentUnique <= uniqueCut) | zeroVar) } else { out <- which((freqRatio > freqCut & percentUnique <= uniqueCut) | zeroVar) names(out) <- NULL } out } zeroVar <- function(x) { x <- x[,colnames(x) != ".outcome", drop = FALSE] which(apply(x, 2, function(x) length(unique(x)) < 2)) } checkConditionalX <- function(x, y) { x$.outcome <- y unique(unlist(dlply(x, .(.outcome), zeroVar))) } checkResamples <- function(index, x, y) { if(!is.factor(y)) stop("y must be a factor") if(length(levels(y)) < 2) stop("y must have at least 2 levels") wrap <- function(index, x, y) checkConditionalX(x[index,,drop=FALSE], y[index]) unique(unlist(lapply(index, wrap, x = x, y = y))) } nearZeroVarOld <- function(x, freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE) { if(is.vector(x)) x <- matrix(x, ncol = 1) freqRatio <- apply( x, 2, function(data) { dataTable <- sort(table(data[!is.na(data)]), decreasing = TRUE) if(length(dataTable ) >= 2) { dataTable [1]/dataTable[2] } else 0 }) percentUnique <- apply( x, 2, function(data) 100*length(unique(data[!is.na(data)]))/length(data)) zeroVar <- apply(x, 2, function(data) length(unique(data[!is.na(data)])) == 1 | all(is.na(data))) if(saveMetrics) { out <- data.frame(freqRatio = freqRatio, percentUnique = percentUnique, zeroVar = zeroVar, nzv = (freqRatio > freqCut & percentUnique <= uniqueCut) | zeroVar) } else { out <- which((freqRatio > freqCut & percentUnique <= uniqueCut) | zeroVar) names(out) <- NULL } out } caret/R/heldout.R0000644000175000017500000001623012453550064014022 0ustar rbalintrbalint################################################################### ## ## Todo: ## X. Make an argument for what to save ("prediction", "probabilities", "both") ## X. Adapt code to average predictions/probabilities ## 1. Use train/rfe/sbf saved prediction function to make predictions ## for the ensemble. ## X. Change `nrow` to an average of `complete.cases` ## X. Detect class prob column names and drop off the last one ## X. Breakout the 'n' code so that it isn't converted to character ## X. Call it "oob" or "oob_pred" ## 2. Make `oob_pred.list` smaller and create a new function to ## do the real work ## 3. TEST TEST TEST ################################################################### ## oob_pred <- function (x, ...) UseMethod("oob_pred") oob_pred.train <- function(x, best = TRUE, average = TRUE) { if(is.null(x$pred)) stop("re-fit the model using 'trainControl(savePredictions=TRUE)'") prd <- x$pred pname <- as.character(x$modelInfo$parameters$parameter) if(best) { prd <- merge(prd, x$bestTune) prd <- prd[, !(colnames(prd) %in% pname)] } bycol <- "rowIndex" if(!best) bycol <- c(bycol, pname) if(average) prd <- get_averages(x, prd, bycol) if(x$modelType == "Classification") { lev <- train_lev(x) if(!is.null(lev)) { if(is.character(prd$obs)) prd$obs <- factor(prd$obs, levels = lev) if(is.character(prd$pred)) prd$pred <- factor(prd$pred, levels = lev) } } prd } oob_pred.rfe <- function(x, best = TRUE, average = TRUE) { if(is.null(x$pred)) stop("re-fit the model using 'rfeControl(saveDetails=TRUE)'") prd <- x$pred if(best) { prd <- subset(prd, Variables == x$bestSubset) prd <- prd[, colnames(prd) != "Variables"] bycol <- "rowIndex" } else bycol <- c("rowIndex", "Variables") ## Temp kludge since I think there is an issue with ## how `rfe` saves the data prd <- prd[!duplicated(prd),] if(average) prd <- get_averages(x, prd, bycol) if(is.character(prd$obs) && !is.null(x$obsLevels)) prd$obs <- factor(prd$obs, levels = x$obsLevels) if(is.character(prd$pred) && !is.null(x$obsLevels)) prd$pred <- factor(prd$pred, levels = x$obsLevels) prd } oob_pred.sbf <- function(x, average = TRUE) { if(is.null(x$pred)) stop("re-fit the model using 'rfeControl(saveDetails=TRUE)'") prd <- x$pred[names(x$pred) == "predictions"] prd <- rbind.fill(prd) prd <- prd[!duplicated(prd),] if(average) prd <- get_averages(x, prd, bycol = "rowIndex") if(is.character(prd$obs) && !is.null(x$obsLevels)) prd$obs <- factor(prd$obs, levels = x$obsLevels) if(is.character(prd$pred) && !is.null(x$obsLevels)) prd$pred <- factor(prd$pred, levels = x$obsLevels) prd } oob_pred.list <- function(x, direction = "wide", what = "both", ...) { num <- length(x) oob <- lapply(x, oob_pred, ...) ## check column names and get those common to everything cnames <- lapply(oob, colnames) cnames <- table(unlist(cnames)) if(any(cnames < num)) { cnames <- names(cnames[cnames == num]) for(i in 1:num) oob[[i]] <- oob[[i]][, cnames] } nms <- names(oob) if(is.null(nms)) nms <- well_numbered("Model", length(oob)) for(i in seq(along = nms)) oob[[i]]$.label <- nms[i] oob <- rbind.fill(oob) if(length(table(table(oob$n))) > 1) stop("Some averages have different sample sizes than others") if(direction == "wide") { vert_names <- colnames(oob) vert_names <- vert_names[!(vert_names %in% c("rowIndex", "n", "obs", ".label"))] oob <- reshape(oob, direction = "wide", v.names = vert_names, idvar = c("rowIndex", "obs", "n"), timevar = ".label") vert_names <- vert_names[vert_names != "pred"] exclude <- vert_names[length(vert_names)] oob <- oob[, !grepl(paste0("^", exclude, "\\."), names(oob))] if(!is.null(what) && !("both" %in% what)) { if(!is.null(what) && !("pred" %in% what)) oob <- oob[, !grepl("^pred\\.", names(oob))] if(!is.null(what) && !("prob" %in% what)) { for(i in vert_names) oob <- oob[, !grepl(paste0("^", i, "\\."), names(oob))] } } } oob } ################################################################### ## get_averages <- function (x, ...) UseMethod("get_averages") get_averages.train <- function(x, prd, bycol = "rowIndex") { if("Regression" %in% x$modelType) { out <- ddply(prd, bycol, function(x) c(colMeans(x[, c("pred", "obs")]))) } else { out <- ddply(prd, bycol, function(x) c(pred = char_mode(x$pred), obs = as.character(x$obs)[1])) if(x$control$classProbs) { lev <- train_lev(x) cprobs <- ddply(prd, bycol, function(x, lev) c(colMeans(x[, lev])), lev = lev) out <- merge(out, cprobs) } } n <- ddply(prd, bycol, function(x) c(n = sum(complete.cases(x)))) out <- merge(out, n) out } get_averages.rfe <- function(x, prd, bycol = "rowIndex") { if(is.null(x$obsLevels)) { out <- ddply(prd, bycol, function(x) c(colMeans(x[, c("pred", "obs")]))) } else { out <- ddply(prd, bycol, function(x) c(pred = char_mode(x$pred), obs = as.character(x$obs)[1])) if(all(x$obsLevels %in% colnames(prd))) { lev <- x$obsLevels cprobs <- ddply(prd, bycol, function(x, lev) c(colMeans(x[, lev])), lev = lev) out <- merge(out, cprobs) } } n <- ddply(prd, bycol, function(x) c(n = sum(complete.cases(x)))) out <- merge(out, n) out } get_averages.sbf <- function(x, prd, bycol = "rowIndex") { if(is.null(x$obsLevels)) { out <- ddply(prd, bycol, function(x) c(colMeans(x[, c("pred", "obs")]))) } else { out <- ddply(prd, bycol, function(x) c(pred = char_mode(x$pred), obs = as.character(x$obs)[1])) if(all(x$obsLevels %in% colnames(prd))) { lev <- x$obsLevels cprobs <- ddply(prd, bycol, function(x, lev) c(colMeans(x[, lev])), lev = lev) out <- merge(out, cprobs) } } n <- ddply(prd, bycol, function(x) c(n = sum(complete.cases(x)))) out <- merge(out, n) out } ################################################################### ## char_mode <- function(x, random = TRUE, na.rm = FALSE) { if(na.rm) x <- x[complete.cases(x)] tab <- table(x) tab <- tab[tab == max(tab)] tab <- if(length(tab) > 1 & random) sample(tab, 1) else tab[1] as.vector(names(tab)) } train_lev <- function(x) { if(x$modelType == "Classification") { if(!is.null(x$modelInfo$levels)) { lev <- x$modelInfo$levels(x$finalModel) } else { lev <- if(!isS4(x)) x$finalModel$obsLevel else unique(x$pred$obs) } } else lev <- NULL lev } corr_mat <- function (object, metric = object$metrics, ...) { dat <- object$values[, grepl(paste0("~", metric[1]), colnames(object$values))] colnames(dat) <- gsub(paste0("~", metric[1]), "", colnames(dat)) dat <- cor(dat, ...) dat } caret/R/filterVarImp.R0000644000175000017500000001317512516214271014764 0ustar rbalintrbalint## todo start using foreach here oldfilterVarImp <- function(x, y, nonpara = FALSE, ...) { { notNumber <- unlist(lapply(x, function(x) !is.numeric(x))) if(any(notNumber)) { for(i in which(notNumber)) x[,i] <- as.numeric(x[,i]) } } if(is.factor(y)) { classLevels <- levels(y) outStat <- matrix(NA, nrow = dim(x)[2], ncol = length(classLevels)) for(i in seq(along = classLevels)) { otherLevels <- classLevels[classLevels != classLevels[i]] for(k in seq(along = otherLevels)) { tmpSubset <- as.character(y) %in% c(classLevels[i], otherLevels[k]) tmpY <- factor(as.character(y)[tmpSubset]) tmpX <- x[tmpSubset,] rocAuc <- apply( tmpX, 2, function(x, class, pos) { isMissing <- is.na(x) | is.na(class) if(any(isMissing)) { x <- x[!isMissing] class <- class[!isMissing] } outResults <- if(length(unique(x)) > 200) roc(x, class = class, positive = pos) else roc(x, class = class, dataGrid = FALSE, positive = pos) aucRoc(outResults) }, class = tmpY, pos = classLevels[i]) outStat[, i] <- pmax(outStat[, i], rocAuc, na.rm = TRUE) } if(i ==1 & length(classLevels) == 2) { outStat[, 2] <- outStat[, 1] break() } } colnames(outStat) <- classLevels rownames(outStat) <- dimnames(x)[[2]] outStat <- data.frame(outStat) } else { paraFoo <- function(data, y) abs(coef(summary(lm(y ~ data, na.action = na.omit)))[2, "t value"]) nonparaFoo <- function(x, y, ...) { meanMod <- sum((y - mean(y, rm.na = TRUE))^2) nzv <- nearZeroVar(x, saveMetrics = TRUE) if(nzv$zeroVar) return(NA) if(nzv$percentUnique < 20) { regMod <- lm(y~x, na.action = na.omit, ...) } else { regMod <- try(loess(y~x, na.action = na.omit, ...), silent = TRUE) if(class(regMod) == "try-error" | any(is.nan(regMod$residuals))) try(regMod <- lm(y~x, ...)) if(class(regMod) == "try-error") return(NA) } pR2 <- 1 - (sum(resid(regMod)^2)/meanMod) if(pR2 < 0) pR2 <- 0 pR2 } testFunc <- if(nonpara) nonparaFoo else paraFoo outStat <- apply(x, 2, testFunc, y = y) outStat <- data.frame(Overall = outStat) } outStat } rocPerCol <- function(dat, cls) { loadNamespace("pROC") pROC::roc(cls, dat)$auc } filterVarImp <- function(x, y, nonpara = FALSE, ...) { { notNumber <- unlist(lapply(x, function(x) !is.numeric(x))) if(any(notNumber)) { for(i in which(notNumber)) x[,i] <- as.numeric(x[,i]) } } if(is.factor(y)) { classLevels <- levels(y) k <- length(classLevels) if(k > 2) { counter <- 1 classIndex <- vector(mode = "list", length = k) tmpStat <- matrix(NA, nrow = ncol(x), ncol = choose(k, 2)) for(i in 1:k) { for(j in i:k) { if(i != j) { classIndex[[i]] <- c(classIndex[[i]], counter) classIndex[[j]] <- c(classIndex[[j]], counter) index <- which(y %in% c(classLevels[i], classLevels[j])) tmpX <- x[index,,drop = FALSE] tmpY <- factor(as.character(y[index]), levels = c(classLevels[i], classLevels[j])) tmpStat[,counter] <- apply(tmpX, 2, rocPerCol, cls = tmpY) counter <- counter + 1 } } } outStat <- matrix(NA, ncol(x), k) for(i in 1:k) outStat[,i] <- apply(tmpStat[,classIndex[[i]]], 1, max) } else { tmp <- apply(x, 2, rocPerCol, cls = y) outStat <- cbind(tmp, tmp) } outStat <- as.data.frame(outStat) colnames(outStat) <- classLevels rownames(outStat) <- dimnames(x)[[2]] outStat <- data.frame(outStat) } else { paraFoo <- function(data, y) abs(coef(summary(lm(y ~ data, na.action = na.omit)))[2, "t value"]) nonparaFoo <- function(x, y, ...) { meanMod <- sum((y - mean(y, rm.na = TRUE))^2) nzv <- nearZeroVar(x, saveMetrics = TRUE) if(nzv$zeroVar) return(NA) if(nzv$percentUnique < 20) { regMod <- lm(y~x, na.action = na.omit, ...) } else { regMod <- try(loess(y~x, na.action = na.omit, ...), silent = TRUE) if(class(regMod) == "try-error" | any(is.nan(regMod$residuals))) try(regMod <- lm(y~x, ...)) if(class(regMod) == "try-error") return(NA) } pR2 <- 1 - (sum(resid(regMod)^2)/meanMod) if(pR2 < 0) pR2 <- 0 pR2 } testFunc <- if(nonpara) nonparaFoo else paraFoo outStat <- apply(x, 2, testFunc, y = y) outStat <- data.frame(Overall = outStat) } outStat } caret/R/createResample.R0000644000175000017500000000106512453550064015312 0ustar rbalintrbalintcreateResample <- function(y, times = 10, list = TRUE) { trainIndex <- matrix(0, ncol = times, nrow = length(y)) out <- apply(trainIndex, 2, function(data) { index <- seq(along = data) out <- sort(sample(index, size = length(index), replace = TRUE)) out }) if (list) { out <- as.data.frame(out) attributes(out) <- NULL names(out) <- prettySeq(out) } else { colnames(out) <- prettySeq(1:ncol(out)) } out } caret/R/dotplot.varImp.train.R0000644000175000017500000000173612453550064016421 0ustar rbalintrbalintdotPlot <- function (x, top = min(20, dim(x$importance)[1]), ...) { varSubset <- sortImp(x, top) plotObj <- stack(varSubset) if(dim(varSubset)[2] == 1) { plotObj <- varSubset names(plotObj) <- "values" plotObj$ind <- "Overall" } else plotObj <- stack(varSubset) plotObj$Var <- rep(rownames(varSubset), dim(varSubset)[2]) plotObj$Var <- factor(plotObj$Var, levels = rev(rownames(varSubset))) if(dim(varSubset)[2] < 3) { if(dim(varSubset)[2] > 1) plotObj <- plotObj[plotObj$ind == levels(plotObj$ind)[1],] out <- dotplot( Var ~ values, data = plotObj, as.table = TRUE, xlab = "Importance", ...) } else { out <- dotplot( Var ~ values, data = plotObj, groups = plotObj$ind, auto.key = list(columns = min(3, length(levels(plotObj$ind)))), as.table = TRUE, xlab = "Importance", ...) } out } caret/R/rocPoint.R0000644000175000017500000000125212453550064014151 0ustar rbalintrbalintrocPoint <- function(cutoff, x, y, positive) { warning("This function is deprecated a of 1/3/12. The computations now utilize the pROC package. This function will be removed in a few releases.") classLevels <- levels(y) negative <- classLevels[positive != classLevels] newClass <- factor( ifelse( x <= cutoff, negative, positive), levels = classLevels) out <- c( cutoff, sensitivity(newClass, y, positive), specificity(newClass, y, negative)) names(out) <- c("cutoff", "sensitivity", "specificity") out } caret/R/safs.R0000644000175000017500000007432312516214271013316 0ustar rbalintrbalintsa_internal_names <- c('Best','Note','Random','Prob','Iter','Cycle', 'SinceRestart','Size','Similarity','Similarity_B','Resample') sa_external_names <- c('Iter','Resample') sa_func_check <- function(x) { fnames <- names(x) required <- c('fit', 'fitness_intern', 'pred', 'fitness_extern', 'initial', 'perturb', 'prob', 'selectIter') missing <- !(required %in% fnames) if(any(missing)) stop(paste("The following functions are missing from the 'func' argument:", paste(required[missing], sep = "", collapse = ","))) invisible(x) args <- lapply(x, function(x) names(formals(x))) expected <- list(fit = c('x', 'y', 'lev', 'last', '...'), fitness_intern = c('object', 'x', 'y', 'maximize', 'p'), pred = c('object', 'x'), fitness_extern = c('data', 'lev', 'model'), initial = c('vars', 'prob', '...'), perturb = c('x', 'vars', 'number'), prob = c('old', 'new', 'iteration'), selectIter = c('x', 'metric', 'maximize')) if(is.vector(x$initial)) { x <- x[!(names(x) == "initial")] expected <- expected[!(names(expected) == "initial")] } for(i in names(x)) { .args <- names(formals(x[[i]])) .check <- same_args(.args, expected[[i]]) if(!.check) { stop(paste("Arguments to function", i, "should be {", paste(expected[[i]], sep = "", collapse = ", "), "} and these were given {", paste(.args, sep = "", collapse = ", "), "}\n")) } } } sa_bl_correct0 <- function(x) { extras <- c('Best', 'Note', 'Random', 'Prob', 'Iter', 'Cycle', 'SinceRestart', 'Size', 'Resample') bl <- x[which.min(x$Iter),] bl <- bl[, !(names(bl) %in% extras)] perf_names <- names(bl) for(i in perf_names) x[, i] <- x[,i] - bl[,i] x } sa_bl_correct <- function(x) ddply(x, .(Resample), sa_bl_correct0 ) print.safs <- function (x, top = 5, digits = max(3, getOption("digits") - 3), ...) { cat("\nSimulated Annealing Feature Selection\n\n") cat(x$dims[1], " samples\n", x$dims[2], " predictor", ifelse(x$dims[2] > 1, "s\n", "\n"), sep = "") if(!is.null(x$levels)){ cat(length(x$levels), "classes:", paste("'", x$levels, "'", sep = "", collapse = ", "), "\n") } cat("\n") cat("Maximum search iterations:", max(x$iters), "\n") if(x$control$improve < Inf) { num_re <- ddply(x$internal, "Resample", function(x) sum(x$Note == "Restart")) cat("Restart after ", x$control$improve, " iterations without improvement (", round(mean(num_re$V1), 1), " restarts on average)\n", sep = "") } cat("\n") inames <- names(x$internal) inames <- inames[!(inames %in% sa_internal_names)] enames <- names(x$external) enames <- enames[!(enames %in% sa_external_names)] cat("Internal performance value", ifelse(length(inames) > 1, "s: ", ": "), paste(inames, sep = "", collapse = ", "), "\n", sep = "") cat("Subset selection driven to", if(x$control$maximize["internal"]) "maximize internal" else "minimize internal", x$control$metric["internal"], "\n") cat("\n") cat("External performance value", ifelse(length(enames) > 1, "s: ", ": "), paste(enames, sep = "", collapse = ", "), "\n", sep = "") if(x$auto) { cat("Best iteration chose by", if(x$control$maximize["external"]) "maximizing external" else "minimizing external", x$control$metric["external"], "\n") } else { cat("Best iteration chosen manually\n") } resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) resampText <- getFromNamespace("resampName", "caret")(x) cat("External resampling method:", resampText, "\n") if(x$control$holdout > 0) cat("Subsampling for internal fitness calculation: ", round(x$control$holdout*100, digits), "%\n", sep = "") cat("\n") vars <- sort(table(unlist(x$resampled_vars)), decreasing = TRUE) top <- min(top, length(vars)) smallVars <- vars[1:top] smallVars <- round(smallVars/length(x$control$index)*100, 1) varText <- paste0(names(smallVars), " (", smallVars, "%)") varText <- paste(varText, collapse = ", ") if(!all(is.na(smallVars))) { cat("During resampling:\n * the top ", top, " selected variables (out of a possible ", x$dims[2], "):\n ", varText, "\n", sep = "") cat(" * on average, ", round(mean(unlist(lapply(x$resampled_vars, length))), 1), " variables were selected (min = ", round(min(unlist(lapply(x$resampled_vars, length))), 1), ", max = ", round(max(unlist(lapply(x$resampled_vars, length))), 1), ")\n\n", sep = "") } else { cat("During resampling, no variables were selected.\n\n") } cat("In the final search using the entire training set:\n", " *", length(x$optVariables), "features selected at iteration", x$optIter, "including:\n ", paste(x$optVariables[1:min(length(x$optVariables), top)], sep = "", collapse = ", "), if(length(x$optVariables) > top) "..." else "", "\n") perf_dat <- subset(x$external, Iter == x$optIter) perf_dat <- perf_dat[!(names(perf_dat) %in% c("Iter", "Resample"))] perf <- colMeans(perf_dat) cat(" * external performance at this iteration is\n\n") ch_perf <- format(perf, digits = digits, row.names = FALSE) ch_perf[1] <- paste(" ", ch_perf[1]) print(ch_perf, quote = FALSE) cat("\n") invisible(x) } predict.safs <- function (object, newdata, ...) { newdata <- newdata[, object$optVariables, drop = FALSE] object$control$functions$pred(object$fit, newdata) } safsControl <- function(functions = NULL, method = "repeatedcv", metric = NULL, maximize = NULL, number = ifelse(grepl("cv", method), 10, 25), repeats = ifelse(grepl("cv", method), 1, 5), verbose = FALSE, returnResamp = "final", p = .75, index = NULL, indexOut = NULL, seeds = NULL, holdout = 0, improve = Inf, allowParallel = TRUE) { if(!(method %in% c("cv", "boot", "repeatedcv", "LGOCV", "LOOCV"))) stop('method should be one of: "cv", "boot", "repeatedcv", "LGOCV" or "LOOCV"') if(holdout < 0 | holdout >= 1) stop("'holdout' should be in [0, 1)") if(improve < 2 ) stop("'improve' should be >= 2") if(!is.null(metric)) { if(length(metric) != 2) stop("'metric' should be a two-element named vector. See ?safsControl") if(is.null(names(metric)) || any(sort(names(metric)) != c("external", "internal"))) stop("'metric' should have names 'internal' and 'external' See ?safsControl") } if(!is.null(maximize)) { if(length(maximize) != 2) stop("'maximize' should be a two-element named vector. See ?safsControl") if(is.null(names(maximize)) || any(sort(names(maximize)) != c("external", "internal"))) stop("'maximize' should have names 'internal' and 'external' See ?safsControl") } list(functions = if(is.null(functions)) caretFuncs else functions, method = method, metric = metric, maximize = maximize, number = number, repeats = repeats, returnResamp = returnResamp, verbose = verbose, p = p, index = index, indexOut = indexOut, seeds = seeds, holdout = holdout, improve = improve, allowParallel = allowParallel) } safs <- function (x, ...) UseMethod("safs") "safs.default" <- function(x, y, iters = 10, differences = TRUE, safsControl = safsControl(), ...) { startTime <- proc.time() funcCall <- match.call(expand.dots = TRUE) if(is.null(safsControl$metric)) safsControl$metric <- rep(ifelse(is.factor(y), "Accuracy", "RMSE"), 2) if(is.null(safsControl$maximize)) safsControl$maximize <- rep(ifelse(safsControl$metric == "RMSE", FALSE, TRUE), 2) if(is.null(names(safsControl$metric))) names(safsControl$metric) <- c("internal", "external") if(is.null(names(safsControl$maximize))) names(safsControl$maximize) <- c("internal", "external") if(nrow(x) != length(y)) stop("there should be the same number of samples in x and y") numFeat <- ncol(x) classLevels <- levels(y) if(is.null(safsControl$index)) safsControl$index <- switch(tolower(safsControl$method), cv = createFolds(y, safsControl$number, returnTrain = TRUE), repeatedcv = createMultiFolds(y, safsControl$number, safsControl$repeats), loocv = createFolds(y, length(y), returnTrain = TRUE), boot =, boot632 = createResample(y, safsControl$number), test = createDataPartition(y, 1, safsControl$p), lgocv = createDataPartition(y, safsControl$number, safsControl$p)) if(is.null(names(safsControl$index))) names(safsControl$index) <- getFromNamespace("prettySeq", "caret")(safsControl$index) ## Create hold--out indicies if(is.null(safsControl$indexOut)){ safsControl$indexOut <- lapply(safsControl$index, function(training, allSamples) allSamples[-unique(training)], allSamples = seq(along = y)) names(safsControl$indexOut) <- getFromNamespace("prettySeq", "caret")(safsControl$indexOut) } if(!is.null(safsControl$seeds)) { if(length(safsControl$seeds) < length(safsControl$index) + 1) stop(paste("There must be at least", length(safsControl$index) + 1, "random number seeds passed to safsControl")) } else { safsControl$seeds <- sample.int(100000, length(safsControl$index) + 1) } ## check summary function and metric testOutput <- data.frame(pred = sample(y, min(10, length(y))), obs = sample(y, min(10, length(y)))) if(is.factor(y)) for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput)) test <- safsControl$functions$fitness_extern(testOutput, lev = classLevels) perfNames <- names(test) if(is.null(perfNames)) { warning(paste("The external fitness results should be a *named* vector;", "new name(s) are", paste(paste0("external", 1:length(test)), sep = "", collapse = ", ")), immediate. = TRUE) perfNames <- paste0("external", 1:length(test)) } if(!(safsControl$metric["external"] %in% perfNames)) { warning(paste("The metric '", safsControl$metric["external"], "' is not created by the external summary function; '", perfNames[1], "' will be used instead", sep = "")) safsControl$metric["external"] <- perfNames[1] } `%op%` <- getOper(safsControl$allowParallel && getDoParWorkers() > 1) # sa_resampled <- external <- vector(mode = "list", length = length(safsControl$index)) result <- foreach(i = seq(along = safsControl$index), .combine = "c", .verbose = FALSE, .errorhandling = "stop") %op% { sa_select(x[safsControl$index[[i]],,drop=FALSE], y[safsControl$index[[i]]], funcs = safsControl$functions, sa_metric = safsControl$metric, sa_maximize = safsControl$maximize, iters = iters, sa_verbose = safsControl$verbose, testX = x[safsControl$indexOut[[i]],,drop=FALSE], testY = y[safsControl$indexOut[[i]]], sa_seed = safsControl$seeds[i], improve = safsControl$improve, Resample = names(safsControl$index)[i], holdout = safsControl$holdout, lvl = classLevels, ...) } ## TODO save only the parts you need inside of loop external <- result[names(result) == "external"] external <- do.call("rbind", external) rownames(external) <- NULL internal <- result[names(result) == "table"] internal <- do.call("rbind", internal) rownames(internal) <- NULL selected_vars <- result[names(result) == "final"] names(selected_vars) <- names(safsControl$index) if(differences) { diffs <- try(process_diffs(result[names(result) == "diffs"], colnames(x)), silent = TRUE) if(class(diffs)[1] == "try-error") { diffs <- NULL warning("An error occured when computing the variable differences") } } else diffs <- NULL rm(result) if(safsControl$verbose) cat("+ final SA\n") if(safsControl$holdout > 0) { in_holdout <- createDataPartition(y, p = safsControl$holdout, list = FALSE) in_model <- seq(along = y)[-unique(in_holdout)] } else { in_model <- seq(along = y) in_holdout <- NULL } final_sa <- sa_select(x[in_model,,drop=FALSE], y[in_model], funcs = safsControl$functions, sa_metric = safsControl$metric, sa_maximize = safsControl$maximize, iters = iters, sa_verbose = safsControl$verbose, testX = if(!is.null(in_holdout)) x[in_holdout,,drop=FALSE] else NULL, testY = if(!is.null(in_holdout)) y[in_holdout] else NULL, sa_seed = safsControl$seeds[length(safsControl$seeds)], improve = safsControl$improve, lvl = classLevels, ...) averages <- ddply(external, .(Iter), function(x, nms) { apply(x[, perfNames, drop = FALSE], 2, mean) }, nms = perfNames) if(!is.null(safsControl$functions$selectIter)) { best_index <- safsControl$functions$selectIter(averages, metric = safsControl$metric["external"], maximize = safsControl$maximize["external"]) best_iter <- averages$Iter[best_index] best_vars <- colnames(x)[final_sa$subsets[[best_index]]] } else { best_index <- if(safsControl$maximize["external"]) which.max(averages[,safsControl$metric["external"]]) else which.min(averages[,safsControl$metric["external"]]) best_iter <- averages$Iter[best_index] best_vars <- colnames(x)[final_sa$subsets[[best_index]]] } if(safsControl$verbose) cat("+ final model\n") fit <- safsControl$functions$fit(x[, best_vars, drop=FALSE], y, lev = lvls, last = TRUE, ...) endTime <- proc.time() res <- list(fit = fit, sa = final_sa, external = external, internal = internal, all_vars = colnames(x), resampled_vars = selected_vars, averages = averages, iters = iters, optVariables = best_vars, optIter = best_iter, control = safsControl, dims = dim(x), differences = diffs, perfNames = perfNames, auto = TRUE, the_dots = list(...), times = list(everything = endTime - startTime), levels = if(is.factor(y)) classLevels else NULL) class(res) <- "safs" res } safs_initial <- function (vars, prob = .20, ...) { sort(sample.int(vars, size = floor(vars*prob)+1)) } safs_perturb <- function(x, vars, number = floor(vars*.01) + 1) { bin <- index2vec(x, vars) change <- sample(seq(along = bin), size = number) bin[change] <- ifelse(bin[change] == 1, 0, 1) sort(which(bin == 1)) } safs_prob <- function(old, new, iteration = 1) { if(new < old) return(1) ediff <- as.vector(old - new) ediff <- ediff/abs(old) exp(ediff*iteration) } sa_wrapper <- function(ind, x, y, funcs, holdoutX, holdoutY, testX, testY, sa_metric, sa_maximize, lvl = lvl, last = FALSE, ...) { mod <- funcs$fit(x[, ind, drop=FALSE], y, lev = lvl, last = last,...) internal <- funcs$fitness_intern(mod, x = if(!is.null(holdoutX)) holdoutX[, ind, drop=FALSE] else x[, ind, drop=FALSE], y = if(!is.null(holdoutY)) holdoutY else y, p = ncol(x)) if(!is.null(testX)) { modelPred <- funcs$pred(mod, testX[, ind, drop=FALSE]) if(is.data.frame(modelPred) | is.matrix(modelPred)) { if(is.matrix(modelPred)) modelPred <- as.data.frame(modelPred) modelPred$obs <- testY modelPred$Size <- length(ind) } else modelPred <- data.frame(pred = modelPred, obs = testY, Size = sum(ind == 1)) external <- funcs$fitness_extern(modelPred, lev = levels(testY)) if(is.null(names(external))) { names(external) <- paste0("external", 1:length(external)) } } else external <- NULL if(sa_maximize["internal"]) internal[sa_metric["internal"]] <- -internal[sa_metric["internal"]] list(internal = internal, external = external) } ################################################################### ## sa_select <- function(x, y, ## testX, testY: optional holdout data for computing ## the fitness function testX = NULL, testY = NULL, iters = 20, funcs = NULL, sa_metric = NULL, sa_maximize = TRUE, sa_verbose = TRUE, holdout = 0, sa_seed = NULL, improve = 10, lvl = NULL, Resample = "", ...) { sa_func_check(funcs) if(!is.null(sa_seed)) set.seed(sa_seed[1]) dig <- options()$digits if(holdout > 0) { in_holdout <- createDataPartition(y, p = holdout, list = FALSE) holdout_x <- x[in_holdout,,drop = FALSE] holdout_y <- y[in_holdout] x <- x[-in_holdout,,drop = FALSE] y <- y[-in_holdout] } else { holdout_x <- NULL holdout_y <- NULL } p <- ncol(x) cycle <- 1 since_restart <- 0 since_improve <- 0 last_improve <- 0 restarts <- 1 subsets <- vector(mode = "list", length = iters) internal <- data.frame(Best = rep(0*NA, iters), Note = "", Random = runif(iters), Prob = rep(1, iters), Iter = 1:(iters), Cycle = rep(0*NA, iters), SinceRestart = rep(0*NA, iters), Size = rep(0*NA, iters), Similarity = rep(0*NA, iters), Similarity_B = rep(0*NA, iters), stringsAsFactors = FALSE) external <- if(!is.null(testX)) data.frame(Iter = 1:(iters)) else NULL for(i in 1:iters){ if(i == 1) { if(is.function(funcs$initial)) { best_subset <- new_subset <- current_subset <- funcs$initial(vars = p) } else { if(max(funcs$initial) > p) stop(paste("The initial vector uses columns not in the data")) best_subset <- new_subset <- current_subset <- funcs$initial } } else { new_subset <- funcs$perturb(current_subset, vars = p) } if(length(new_subset) == 0) new_subset <- sample.int(p, 1) subsets[[i]] <- new_subset if(i > 1) { internal$Similarity[i] <- jack_sim(index2vec(subsets[[i-1]], p), index2vec(subsets[[i ]], p)) internal$Similarity_B[i] <- jack_sim(index2vec(best_subset, p), index2vec(new_subset, p)) } since_restart <- since_restart + 1 internal$SinceRestart[i] <- since_restart new_obj <- sa_wrapper(ind = new_subset, x = x, y = y, funcs, holdoutX = holdout_x, holdoutY = holdout_y, testX = testX, testY = testY, sa_metric = sa_metric, sa_maximize = sa_maximize, lvl = lvl, last = Resample == "", ...) ## Use the initial results to setup containers for ## the remaining iterations if(i == 1) { ##TODO check for name and modify if needed as with external k <- length(new_obj$internal) perf_names <- names(new_obj$internal) for(new_var in perf_names) internal[,new_var] <- NA nr <- ncol(internal) internal[1, (nr-k+1):nr] <- new_obj$internal if(!is.null(testX)) { for(new_var in names(new_obj$external)) external[,new_var] <- NA external[1, -1] <- new_obj$external } } else { internal[i, (nr-k+1):nr] <- new_obj$internal if(!is.null(testX)) external[i, -1] <- new_obj$external } if(sa_verbose){ if(i > 1) { cat(Resample, " ", format(1:iters)[i], " ", if(sa_maximize["internal"]) signif(-internal$Best[i-1], digits = dig) else signif(internal$Best[i-1], digits = dig), "->" , if(sa_maximize["internal"]) signif(-new_obj$internal[sa_metric["internal"]], digits = dig) else signif(new_obj$internal[sa_metric["internal"]], digits = dig), change_text(best_subset, new_subset, p), sep = "") } else { cat(Resample, " ", format(1:iters)[i], " ", if(sa_maximize["internal"]) signif(-new_obj$internal[sa_metric["internal"]], digits = dig) else signif(new_obj$internal[sa_metric["internal"]], digits = dig), " (" , length(best_subset), ")\n", sep = "") } } internal$Size[i] <- length(new_subset) internal$Cycle[i] <- cycle if(i == 1 || new_obj$internal[sa_metric["internal"]] < internal$Best[i-1]) { current_subset <- new_subset best_subset <- new_subset internal$Best[i] <- new_obj$internal[sa_metric["internal"]] internal$Note[i] <- "Improved" last_improve <- i since_improve <- 0 if(sa_verbose & i > 1) cat(" *\n") } else { if(i > 1) { internal$Prob[i] <- funcs$prob(old = internal$Best[i-1], new = new_obj$internal[sa_metric["internal"]], iteration = since_restart) since_improve <- since_improve + 1 if(sa_verbose) cat(" ", signif(internal$Prob[i], digits = dig), " ") } else internal$Prob[i] <- 1 if(internal$Prob[i] > internal$Random[i]) { current_subset <- new_subset internal$Best[i] <- internal$Best[i-1] internal$Note[i] <- "Accepted" if(sa_verbose & i > 1) cat("A\n") } else { internal$Obj[i] <- internal$Obj[i-1] internal$Best[i] <- internal$Best[i-1] internal$Note[i] <- "Discarded" if(sa_verbose & i > 1) cat("\n") } } if(since_improve == improve) { internal$Note[i] <- "Restart" current_subset <- subsets[[last_improve]] cycle <- cycle + 1 since_restart <- 0 since_improve <- 0 if(sa_verbose) cat(Resample, "restart, goto iter", last_improve, "\n") } } if(sa_maximize["internal"]) { internal[, sa_metric["internal"]] <- -internal[, sa_metric["internal"]] internal$Best <- -internal$Best } mod <- funcs$fit(x[, best_subset, drop=FALSE], y, lev = lvl, last = TRUE, ...) if(Resample != "") internal$Resample <- Resample if(Resample != "" && !is.null(testX)) external$Resample <- Resample diffs <- try(get_fitness_differences(colnames(x), subsets, external[, !(names(external) %in% sa_external_names), drop = FALSE]), silent = TRUE) if(class(diffs)[1] == "try-error") diffs <- NULL list(table = internal, subsets = subsets, external = external, final = names(x)[best_subset], fit = mod, diffs = diffs) } ################################################################### ## plot.safs <- function(x, metric = x$control$metric["external"], estimate = c("internal", "external"), output = "ggplot", ...) { int_names <- names(x$internal)[!(names(x$internal) %in% sa_internal_names)] ext_names <- names(x$external)[!(names(x$external) %in% sa_external_names)] common <- intersect(int_names, ext_names) both_estimates <- length(estimate) == 2 && all(sort(estimate) == c("external", "internal")) if(both_estimates){ if(!metric %in% common) stop(paste("'", metric, "' not computed in both estimates")) tmp_e <- x$external[, c("Iter", "Resample", common)] tmp_e$Estimate <- "External" tmp_i <- x$internal[, c("Iter", "Resample", common)] tmp_i$Estimate <- "Internal" plot_dat <- rbind(tmp_e, tmp_i) } else { if("internal" %in% estimate) { if(!metric %in% int_names) stop(paste("'", metric, "' not computed internally")) plot_dat <- x$internal[, c("Iter", "Resample", int_names)] } if("external" %in% estimate) { if(!metric %in% int_names) stop(paste("'", metric, "' not computed externally")) plot_dat <- x$external[, c("Iter", "Resample", ext_names)] } } if(output == "data") out <- plot_dat plot_dat <- if(both_estimates) ddply(plot_dat, c("Iter", "Estimate"), function(x) c(Mean = mean(x[, metric]))) else ddply(plot_dat, c("Iter"), function(x) c(Mean = mean(x[, metric]))) if(output == "ggplot") { out <- if(both_estimates) ggplot(plot_dat, aes(x = Iter, y = Mean, color = Estimate)) + geom_point() else ggplot(plot_dat, aes(x = Iter, y = Mean)) + geom_point() out <- out + xlab("Iteration") } if(output == "lattice") { out <- if(both_estimates) xyplot(Mean ~ Iter, data = plot_dat, groups = Estimate, ...) else xyplot(Mean ~ Iter, data = plot_dat, ...) out <- update(out, xlab = "Iteration") } out } ################################################################### ## caretSA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) train(x, y, ...), pred = function(object, x) { tmp <- predict(object, x) if(object$control$classProbs) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p){ perf_val <- getTrainPerf(object) perf_val <- perf_val[names(perf_val) != "method"] perf_val <- unlist(perf_val) names(perf_val) <- gsub("Train", "", names(perf_val)) perf_val }, fitness_extern = defaultSummary, initial = safs_initial, perturb = safs_perturb, prob = safs_prob, selectIter = best) treebagSA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) { loadNamespace("ipred") ipred::ipredbagg(y, x, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p) ipredStats(object)[1:2], fitness_extern = defaultSummary, initial = safs_initial, perturb = safs_perturb, prob = safs_prob, selectIter = best) rfSA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) { loadNamespace("randomForest") randomForest::randomForest(x, y, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p) rfStats(object), fitness_extern = defaultSummary, initial = safs_initial, perturb = safs_perturb, prob = safs_prob, selectIter = best) update.safs <- function(object, iter, x, y, ...) { iter <- iter[1] if(iter > length(object$sa$subsets)) stop(paste("iter must be less than", length(object$sa$subsets))) if(is.null(x) | is.null(y)) stop("the original training data is needed to refit the model") args <- list(x = x[, object$sa$subsets[[iter]], drop=FALSE], y = y, lev = object$levels, last = TRUE) if(length(object$the_dots) > 0) args <- c(args, object$the_dots) if(object$control$verbose) cat("Refitting model to use", length(object$sa$subsets[[iter]]), "predictors from iteration", iter, "\n") object$fit <- do.call(object$control$functions$fit, args) object$auto <- FALSE object$optVariables <- colnames(x)[object$sa$subsets[[iter]]] object$optIter <- iter object } "varImp.safs" <- function(object, metric = object$control$metric["external"], maximize = object$control$maximize["external"], ...) { if(is.null(object$differences)) stop("must have used `differences = TRUE`") out <- object$differences[,metric, drop = FALSE] rownames(out) <- as.character(object$differences$Variable) if(!maximize) out[, metric, drop = FALSE] <- -out[, metric, drop = FALSE] out <- out[order(-out[, metric]),, drop = FALSE] out } caret/R/trainControl.R0000644000175000017500000000543312516214271015034 0ustar rbalintrbalinttrainControl <- function(method = "boot", number = ifelse(grepl("cv", method), 10, 25), repeats = ifelse(grepl("cv", method), 1, number), p = .75, initialWindow = NULL, horizon = 1, fixedWindow = TRUE, verboseIter = FALSE, returnData = TRUE, returnResamp = "final", savePredictions = FALSE, classProbs = FALSE, summaryFunction = defaultSummary, selectionFunction = "best", preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5), index = NULL, indexOut = NULL, timingSamps = 0, predictionBounds = rep(FALSE, 2), seeds = NA, adaptive = list(min = 5, alpha = 0.05, method = "gls", complete = TRUE), trim = FALSE, allowParallel = TRUE) { if(is.null(selectionFunction)) stop("null selectionFunction values not allowed") if(!(returnResamp %in% c("all", "final", "none"))) stop("incorrect value of returnResamp") if(length(predictionBounds) > 0 && length(predictionBounds) != 2) stop("'predictionBounds' should be a logical or numeric vector of length 2") if(any(names(preProcOptions) == "method")) stop("'method' cannot be specified here") if(any(names(preProcOptions) == "x")) stop("'x' cannot be specified here") if(!(adaptive$method %in% c("gls", "BT"))) stop("incorrect value of adaptive$method") if(adaptive$alpha < .0000001 | adaptive$alpha > 1) stop("incorrect value of adaptive$alpha") if(grepl("adapt", method)) { num <- if(method == "adaptive_cv") number*repeats else number if(adaptive$min >= num) stop(paste("adaptive$min should be less than", num)) if(adaptive$min <= 1) stop("adaptive$min should be greater than 1") } list(method = method, number = number, repeats = repeats, p = p, initialWindow = initialWindow, horizon = horizon, fixedWindow = fixedWindow, verboseIter = verboseIter, returnData = returnData, returnResamp = returnResamp, savePredictions = savePredictions, classProbs = classProbs, summaryFunction = summaryFunction, selectionFunction = selectionFunction, preProcOptions = preProcOptions, index = index, indexOut = indexOut, timingSamps = timingSamps, predictionBounds = predictionBounds, seeds = seeds, adaptive = adaptive, trim = trim, allowParallel = allowParallel) } caret/R/print.confusionMatrix.R0000644000175000017500000000570312453550064016704 0ustar rbalintrbalintprint.confusionMatrix <- function(x, digits = max(3, getOption("digits") - 3), printStats = TRUE, ...) { cat("Confusion Matrix and Statistics\n\n") print(x$table, ...) # cat("\n(columns are reference results, rows are predictions)\n") if(printStats) { tmp <- round(x$overall, digits = digits) pIndex <- grep("PValue", names(x$overall)) tmp[pIndex] <- format.pval(x$overall[pIndex], digits = digits) overall <- tmp accCI <- paste( "(", paste( overall[ c("AccuracyLower", "AccuracyUpper")], collapse = ", "), ")", sep = "") overallText <- c( paste(overall["Accuracy"]), accCI, paste(overall[c("AccuracyNull", "AccuracyPValue")]), "", paste(overall["Kappa"]), paste(overall["McnemarPValue"])) overallNames <- c("Accuracy", "95% CI", "No Information Rate", "P-Value [Acc > NIR]", "", "Kappa", "Mcnemar's Test P-Value") if(dim(x$table)[1] > 2) { cat("\nOverall Statistics\n") overallNames <- ifelse( overallNames == "", "", paste(overallNames, ":")) out <- cbind( format(overallNames, justify = "right"), overallText) colnames(out) <- rep("", ncol(out)) rownames(out) <- rep("", nrow(out)) print(out, quote = FALSE) cat("\nStatistics by Class:\n\n") print(t(x$byClass), digits = digits) } else { overallText <- c( overallText, "", format(x$byClass, digits = digits)) overallNames <- c( overallNames, "", names(x$byClass)) overallNames <- ifelse( overallNames == "", "", paste(overallNames, ":")) overallNames <- c(overallNames, "", "'Positive' Class :") overallText <- c(overallText, "", x$positive) out <- cbind( format(overallNames, justify = "right"), overallText) colnames(out) <- rep("", ncol(out)) rownames(out) <- rep("", nrow(out)) out <- rbind(out, rep("", 2)) print(out, quote = FALSE) } } invisible(x) } caret/R/sampling.R0000644000175000017500000000356412453550064014176 0ustar rbalintrbalint downSample <- function(x, y, list = FALSE, yname = "Class") { xc <- class(x) if(!is.data.frame(x)) x <- as.data.frame(x) if(!is.factor(y)) { warning("Down-sampling requires a factor variable as the response. The original data was returned.") return(list(x = x, y = y)) } minClass <- min(table(y)) x$.outcome <- y x <- ddply(x, .(y), function(dat, n) dat[sample(seq(along = dat$.outcome), n),,drop = FALSE], n = minClass) y <- x$.outcome x <- x[, !(colnames(x) %in% c("y", ".outcome")), drop = FALSE] if(list) { if(xc[1] == "matrix") x <- as.matrix(x) out <- list(x = x, y = y) } else { out <- cbind(x, y) colnames(out)[ncol(out)] <- yname } out } upSample <- function(x, y, list = FALSE, yname = "Class") { xc <- class(x) if(!is.data.frame(x)) x <- as.data.frame(x) if(!is.factor(y)) { warning("Up-sampling requires a factor variable as the response. The original data was returned.") return(list(x = x, y = y)) } maxClass <- max(table(y)) x$.outcome <- y x <- ddply(x, .(y), function(x, top = maxClass) { if(nrow(x) < top) { ind <- sample(1:nrow(x), size = top - nrow(x), replace = TRUE) ind <- c(1:nrow(x), ind) x <- x[ind,,drop = FALSE] } x }) y <- x$.outcome x <- x[, !(colnames(x) %in% c("y", ".outcome")), drop = FALSE] if(list) { if(xc[1] == "matrix") x <- as.matrix(x) out <- list(x = x, y = y) } else { out <- cbind(x, y) colnames(out)[ncol(out)] <- yname } out } caret/R/gafs.R0000644000175000017500000011212012516214271013266 0ustar rbalintrbalintga_internal_names <- c('Iter','Size','Similarity','Similarity_M','Resample') ga_external_names <- c('Iter','Resample') check_ga_pop <- function(x) { no_vars <- apply(x, 1, sum) == 0 if(any(no_vars)) { for(i in which(no_vars)) { here <- sample(1:ncol(x), 1) x[i,here] <- 1 } } x } ga_func_check <- function(x) { fnames <- names(x) required <- c('fit', 'fitness_intern', 'pred', 'fitness_extern', 'initial', 'selection', 'crossover', 'mutation', 'selectIter') missing <- !(required %in% fnames) if(any(missing)) stop(paste("The following functions are missing from the 'func' argument:", paste(required[missing], sep = "", collapse = ","))) invisible(x) args <- lapply(x, function(x) names(formals(x))) expected <- list(fit = c('x', 'y', 'lev', 'last', '...'), fitness_intern = c('object', 'x', 'y', 'maximize', 'p'), pred = c('object', 'x'), fitness_extern = c('data', 'lev', 'model'), initial = c('vars', 'popSize', '...'), selection = c('population', 'fitness', 'r', 'q', '...'), crossover = c('population', 'fitness', 'parents', '...'), mutation = c('population', 'parent', '...'), selectIter = c('x', 'metric', 'maximize')) for(i in names(x)) { .args <- names(formals(x[[i]])) .check <- same_args(.args, expected[[i]]) if(!.check) { stop(paste("Arguments to function", i, "should be {", paste(expected[[i]], sep = "", collapse = ", "), "} and these were given {", paste(.args, sep = "", collapse = ", "), "}\n")) } } } gafs_initial <- function (vars, popSize, ...) { x <- matrix(NA, nrow = popSize, ncol = vars) probs <- seq(.9, .1, length = popSize) for(i in 1:popSize){ x[i,] <- sample(0:1, replace = TRUE, size = vars, prob = c(probs[i], 1-probs[i])) } var_count <- apply(x, 1, sum) if(any(var_count == 0)) { for(i in which(var_count == 0)) { x[i, ] <- sample(0:1, replace = TRUE, size = vars) } } x } gafs_lrSelection <- function (population, fitness, r = NULL, q = NULL, ...) { popSize = nrow(population) if(is.null(r)) r <- 2/(popSize * (popSize - 1)) if(is.null(q)) q <- 2/popSize rank <- (popSize + 1) - rank(fitness, ties.method = "random") prob <- q - (rank - 1) * r sel <- sample(1:popSize, size = popSize, prob = pmin(pmax(0, prob), 1, na.rm = TRUE), replace = TRUE) out <- list(population = population[sel, , drop = FALSE], fitness = fitness[sel]) out } gafs_spCrossover <- function (population, fitness, parents, ...) { fitness <- fitness[parents] parents <- population[parents, , drop = FALSE] n <- ncol(parents) children <- matrix(as.double(NA), nrow = 2, ncol = n) fitnessChildren <- rep(NA, 2) crossOverPoint <- sample(0:n, size = 1) if (crossOverPoint == 0) { children[1:2, ] <- parents[2:1, ] fitnessChildren[1:2] <- fitness[2:1] } else if (crossOverPoint == n) { children <- parents fitnessChildren <- fitness } else { children[1, ] <- c(parents[1, 1:crossOverPoint], parents[2, (crossOverPoint + 1):n]) children[2, ] <- c(parents[2, 1:crossOverPoint], parents[1, (crossOverPoint + 1):n]) } out <- list(children = children, fitness = fitnessChildren) out } gafs_raMutation <- function (population, parent, ...) { mutate <- parent <- as.vector(population[parent, ]) n <- length(parent) j <- sample(1:n, size = 1) mutate[j] <- abs(mutate[j] - 1) mutate } gafs_nlrSelection <- function (population, fitness, q = 0.25, ...) { popSize <- nrow(population) rank <- (popSize + 1) - rank(fitness, ties.method = "random") prob <- q * (1 - q)^(rank - 1) sel <- sample(1:popSize, size = popSize, prob = pmin(pmax(0, prob), 1, na.rm = TRUE), replace = TRUE) out <- list(population = population[sel, , drop = FALSE], fitness = fitness[sel]) return(out) } gafs_rwSelection <- function (population, fitness, ...) { popSize <- nrow(population) prob <- abs(fitness)/sum(abs(fitness)) sel <- sample(1:popSize, size = popSize, prob = pmin(pmax(0, prob), 1, na.rm = TRUE), replace = TRUE) out <- list(population = population[sel, , drop = FALSE], fitness = fitness[sel]) return(out) } gafs_tourSelection <- function (population, fitness, k = 3, ...) { popSize <- nrow(population) sel <- rep(NA, popSize) for (i in 1:popSize) { s <- sample(1:popSize, size = k) sel[i] <- s[which.max(fitness[s])] } out <- list(population = population[sel, , drop = FALSE], fitness = fitness[sel]) return(out) } gafs_uCrossover <- function (population, parents, ...) { parents <- population[parents, , drop = FALSE] n <- ncol(parents) u <- runif(n) children <- parents children[1:2, u > 0.5] <- children[2:1, u > 0.5] out <- list(children = children, fitness = rep(NA, 2)) return(out) } ################################################################### ## gafsControl <- function(functions = NULL, method = "repeatedcv", metric = NULL, maximize = NULL, number = ifelse(grepl("cv", method), 10, 25), repeats = ifelse(grepl("cv", method), 1, 5), verbose = FALSE, returnResamp = "final", p = .75, index = NULL, indexOut = NULL, seeds = NULL, holdout = 0, genParallel = FALSE, allowParallel = TRUE) { if(!(method %in% c("cv", "boot", "repeatedcv", "LGOCV", "LOOCV"))) stop('method should be one of: "cv", "boot", "repeatedcv", "LGOCV" or "LOOCV"') if(holdout < 0 | holdout >= 1) stop("'holdout' should be in [0, 1)") if(!is.null(metric)) { if(length(metric) != 2) stop("'metric' should be a two-element named vector. See ?gafsControl") if(is.null(names(metric)) || any(sort(names(metric)) != c("external", "internal"))) stop("'metric' should have names 'internal' and 'external' See ?gafsControl") } if(!is.null(maximize)) { if(length(maximize) != 2) stop("'maximize' should be a two-element named vector. See ?gafsControl") if(is.null(names(maximize)) || any(sort(names(maximize)) != c("external", "internal"))) stop("'maximize' should have names 'internal' and 'external' See ?gafsControl") } list(functions = if(is.null(functions)) caretFuncs else functions, method = method, metric = metric, maximize = maximize, number = number, repeats = repeats, returnResamp = returnResamp, verbose = verbose, p = p, index = index, indexOut = indexOut, seeds = seeds, holdout = holdout, genParallel = genParallel, allowParallel = allowParallel) } ################################################################### ## ga_wrapper <- function(ind, x, y, funcs, holdoutX, holdoutY, testX, testY, ga_metric, ga_maximize, lvl = lvl, last = FALSE, indiv = 0, ...) { mod <- funcs$fit(x[, ind, drop=FALSE], y, lev = lvl, last = last,...) internal <- funcs$fitness_intern(mod, x = if(!is.null(holdoutX)) holdoutX[, ind, drop=FALSE] else x[, ind, drop=FALSE], y = if(!is.null(holdoutY)) holdoutY else y, p = ncol(x)) if(!is.null(testX)) { modelPred <- funcs$pred(mod, testX[, ind, drop=FALSE]) if(is.data.frame(modelPred) | is.matrix(modelPred)) { if(is.matrix(modelPred)) modelPred <- as.data.frame(modelPred) modelPred$obs <- testY modelPred$Size <- length(ind) } else modelPred <- data.frame(pred = modelPred, obs = testY, Size = sum(ind == 1)) external <- funcs$fitness_extern(modelPred, lev = levels(testY)) if(is.null(names(external))) { names(external) <- paste0("external", 1:length(external)) } } else external <- NULL if(!ga_maximize["internal"]) internal[ga_metric["internal"]] <- -internal[ga_metric["internal"]] list(internal = c(internal, .indiv = indiv), external = c(external, .indiv = indiv)) } ################################################################### ## ga_select <- function(x, y, testX = NULL, testY = NULL, iters = 20, funcs = NULL, ga_metric = NULL, ga_maximize = TRUE, ga_verbose = TRUE, holdout = 0, ga_seed = NULL, lvl = NULL, popSize = 50, pcrossover = 0.8, pmutation = 0.1, elite = base::max(1, round(popSize*0.05)), maxfitness = Inf, suggestions = NULL, genParallel = FALSE, Resample = "", ...) { ga_func_check(funcs) nvars <- ncol(x) if(!is.null(ga_seed)) set.seed(ga_seed[1]) dig <- options()$digits if(holdout > 0) { in_holdout <- createDataPartition(y, p = holdout, list = FALSE) holdout_x <- x[in_holdout,,drop = FALSE] holdout_y <- y[in_holdout] x <- x[-in_holdout,,drop = FALSE] y <- y[-in_holdout] } else { holdout_x <- NULL holdout_y <- NULL } ################################################################### ## subsets <- vector(mode = "list", length = iters) internal <- data.frame(Iter = 1:(iters), Size = rep(0*NA, iters), Similarity = rep(0*NA, iters), Similarity_M= rep(0*NA, iters), stringsAsFactors = FALSE) external <- if(!is.null(testX)) data.frame(Iter = 1:(iters)) else NULL ## add GA package warnings ################################################################### ## From GA package: ## TODO make input a vector of indicies if(is.null(suggestions)) { suggestions <- matrix(nrow = 0, ncol = nvars) } else { if(is.vector(suggestions)) { if(nvars > 1) suggestions <- matrix(suggestions, nrow = 1) else suggestions <- matrix(suggestions, ncol = 1) } else suggestions <- as.matrix(suggestions) if(nvars != ncol(suggestions)) stop("Provided suggestions (ncol) matrix do not match number of variables of the problem!") } ################################################################### ## From GA package: Pop <- matrix(as.double(NA), nrow = popSize, ncol = nvars) ng <- min(nrow(suggestions), popSize) if(ng > 0) { # use suggestion if provided Pop[1:ng,] <- suggestions } # fill the rest with a random population if(popSize > ng) { Pop[(ng+1):popSize,] <- funcs$initial(vars = nvars, popSize = popSize)[1:(popSize-ng),] } colnames(Pop) <- colnames(x) .Pop <- Pop .Fit <- rep(NA, nrow(Pop)) ################################################################### ## `%op%` <- getOper(genParallel && getDoParWorkers() > 1) for(generation in 1:iters) { Pop <- check_ga_pop(Pop) currennt_results <- foreach(i = seq_len(popSize), .combine = "c", .verbose = FALSE, .errorhandling = "stop") %op% { ga_wrapper(ind = which(Pop[i,] == 1), x = x, y = y, funcs, holdoutX = holdout_x, holdoutY = holdout_y, testX = testX, testY = testY, ga_metric = ga_metric, ga_maximize = ga_maximize, lvl = lvl, last = Resample == "", indiv = i, ...) } ## loop over chromosomes ## TODO save only the parts you need inside of loop if(!is.null(testX)) { current_ext <- currennt_results[names(currennt_results) == "external"] current_ext <- do.call("rbind", current_ext) current_ext <- current_ext[order(current_ext[,".indiv"]),] current_ext <- current_ext[, -ncol(current_ext), drop = FALSE] rownames(current_ext) <- NULL } else current_ext <- NULL current_int <- currennt_results[names(currennt_results) == "internal"] current_int <- do.call("rbind", current_int) current_int <- current_int[order(current_int[,".indiv"]),] current_int <- current_int[, -ncol(current_int), drop = FALSE] rownames(current_int) <- NULL rm(currennt_results) Fitness <- if(is.matrix(current_int)) current_int[,ga_metric["internal"]] best_index <- which.max(Fitness) best_internal <- current_int[best_index,] if(!is.null(testX)) best_external <- current_ext[best_index,] subsets[[generation]] <- which(Pop[best_index,] == 1) internal$Size[generation] <- sum(Pop[best_index,] == 1) if(generation > 1) { hist_best <- which.max(internal[1:(generation-1), ga_metric["internal"]]) internal$Similarity[generation] <- jack_sim(index2vec(subsets[[hist_best]], ncol(Pop)), index2vec(subsets[[generation]], ncol(Pop))) tmp_sim <- apply(Pop, 1, function(x, y) jack_sim(x, y), y = index2vec(subsets[[hist_best]], ncol(Pop))) internal$Similarity_M[generation] <- mean(tmp_sim, na.rm = TRUE) } .Pop <- Pop .Fit <- Fitness if(generation == 1) { k <- length(best_internal) perf_names <- names(best_internal) for(new_var in perf_names) internal[,new_var] <- NA nr <- ncol(internal) internal[1, (nr-k+1):nr] <- best_internal if(!is.null(testX)) { for(new_var in names(best_external)) external[,new_var] <- NA external[1, -1] <- best_external } } else { internal[generation, (nr-k+1):nr] <- best_internal if(!is.null(testX)) external[generation, -1] <- best_external } if(ga_verbose){ if(generation > 1) { imp <- internal[hist_best, ga_metric["internal"]] < max(Fitness) cat(Resample, " ", format(1:iters)[generation], " ", if(ga_maximize["internal"]) signif( internal[hist_best, ga_metric["internal"]], digits = dig) else signif(-internal[hist_best, ga_metric["internal"]], digits = dig), "->" , if(ga_maximize["internal"]) signif(max(Fitness), digits = dig) else signif( min(-Fitness), digits = dig), change_text(subsets[[hist_best]], subsets[[generation]], nvars, show_diff = FALSE), if(imp) " *" else "", "\n", sep = "") } else { cat(Resample, " ", format(1:iters)[generation], " ", if(ga_maximize["internal"]) signif(internal[1, ga_metric["internal"]], digits = dig) else signif(-internal[1, ga_metric["internal"]], digits = dig), " (", length(subsets[[1]]), ")\n", sep = "") } } ################################################################### ## From GA package ord <- order(Fitness, decreasing = TRUE) PopSorted <- Pop[ord,,drop = FALSE] FitnessSorted <- Fitness[ord] # selection if(is.function(funcs$selection)) { sel <- funcs$selection(population = .Pop, fitness = .Fit) Pop <- sel$population Fitness <- sel$fitness } else { sel <- sample(1:popSize, size = popSize, replace = TRUE) Pop <- .Pop[sel,] Fitness <- .Fit[sel] } .Pop <- Pop .Fit <- Fitness # crossover if(is.function(funcs$crossover) & pcrossover > 0) { nmating <- floor(popSize/2) mating <- matrix(sample(1:(2*nmating), size = (2*nmating)), ncol = 2) for(i in seq_len(nmating)) { if(pcrossover > runif(1)){ parents <- mating[i,] Crossover <- funcs$crossover(population = .Pop, fitness = .Fit, parents = parents) Pop[parents,] <- Crossover$children Fitness[parents] <- Crossover$fitness } } .Pop <- Pop .Fit <- Fitness } # mutation pm <- if(is.function(pmutation)) pmutation(object) else pmutation if(is.function(funcs$mutation) & pm > 0) { for(i in seq_len(popSize)) { if(pm > runif(1)) { Mutation <- funcs$mutation(population = .Pop, parent = i) Pop[i,] <- Mutation Fitness[i] <- NA } } .Pop <- Pop .Fit <- Fitness } # elite if(elite > 0) { ord <- order(.Fit, na.last = TRUE) u <- which(!duplicated(PopSorted, margin = 1)) Pop[ord[1:elite],] <- PopSorted[u[1:elite],] Fitness[ord[1:elite]] <- FitnessSorted[u[1:elite]] .Pop <- Pop .Fit <- Fitness } } ## search iterations best_index <- which.max(internal[, ga_metric["internal"]]) best_subset <- colnames(x)[subsets[[best_index]]] if(!ga_maximize["internal"]) { internal[, ga_metric["internal"]] <- -internal[, ga_metric["internal"]] } mod <- funcs$fit(x[, best_subset, drop=FALSE], y, lev = lvl, last = TRUE, ...) if(Resample != "") internal$Resample <- Resample if(Resample != "" && !is.null(testX)) external$Resample <- Resample diffs <- try(get_fitness_differences(colnames(x), subsets, external[, !(names(external) %in% ga_external_names), drop = FALSE]), silent = TRUE) if(class(diffs)[1] == "try-error") diffs <- NULL list(internal = internal, subsets = subsets, external = external, final = best_subset, fit = mod, diffs = diffs) } ################################################################### ## print.gafs <- function (x, top = 5, digits = max(3, getOption("digits") - 3), ...) { cat("\nGenetic Algorithm Feature Selection\n\n") cat(x$dims[1], " samples\n", x$dims[2], " predictor", ifelse(x$dims[2] > 1, "s\n", "\n"), sep = "") if(!is.null(x$levels)){ cat(length(x$levels), "classes:", paste("'", x$levels, "'", sep = "", collapse = ", "), "\n") } cat("\n") cat("Maximum generations:", max(x$iters), "\n") cat("Population per generation:", x$ga_param$popSize, "\n") cat("Crossover probability:", x$ga_param$pcrossover, "\n") if(is.function(x$ga_param$pmutation)) { cat("Mutation probability: variable\n") } else cat("Mutation probability:", x$ga_param$pmutation, "\n") cat("Elitism:", x$ga_param$elite, "\n\n") inames <- names(x$internal) inames <- inames[!(inames %in% ga_internal_names)] enames <- names(x$external) enames <- enames[!(enames %in% ga_external_names)] cat("Internal performance value", ifelse(length(inames) > 1, "s: ", ": "), paste(inames, sep = "", collapse = ", "), "\n", sep = "") cat("Subset selection driven to", if(x$control$maximize["internal"]) "maximize internal" else "minimize internal", x$control$metric["internal"], "\n") cat("\n") cat("External performance value", ifelse(length(enames) > 1, "s: ", ": "), paste(enames, sep = "", collapse = ", "), "\n", sep = "") if(x$auto) { cat("Best iteration chose by", if(x$control$maximize["external"]) "maximizing external" else "minimizing external", x$control$metric["external"], "\n") } else { cat("Best iteration chosen manually\n") } resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) resampText <- getFromNamespace("resampName", "caret")(x) cat("External resampling method:", resampText, "\n") if(x$control$holdout > 0) cat("Subsampling for internal fitness calculation: ", round(x$control$holdout*100, digits), "%\n", sep = "") cat("\n") vars <- sort(table(unlist(x$resampled_vars)), decreasing = TRUE) top <- min(top, length(vars)) smallVars <- vars[1:top] smallVars <- round(smallVars/length(x$control$index)*100, 1) varText <- paste0(names(smallVars), " (", smallVars, "%)") varText <- paste(varText, collapse = ", ") if(!all(is.na(smallVars))) { cat("During resampling:\n * the top ", top, " selected variables (out of a possible ", x$dims[2], "):\n ", varText, "\n", sep = "") cat(" * on average, ", round(mean(unlist(lapply(x$resampled_vars, length))), 1), " variables were selected (min = ", round(min(unlist(lapply(x$resampled_vars, length))), 1), ", max = ", round(max(unlist(lapply(x$resampled_vars, length))), 1), ")\n\n", sep = "") } else { cat("During resampling, no variables were selected.\n\n") } cat("In the final search using the entire training set:\n", " *", length(x$optVariables), "features selected at iteration", x$optIter, "including:\n ", paste(x$optVariables[1:min(length(x$optVariables), top)], sep = "", collapse = ", "), if(length(x$optVariables) > top) "..." else "", "\n") perf_dat <- subset(x$external, Iter == x$optIter) perf_dat <- perf_dat[!(names(perf_dat) %in% c("Iter", "Resample"))] perf <- colMeans(perf_dat) cat(" * external performance at this iteration is\n\n") ch_perf <- format(perf, digits = digits, row.names = FALSE) ch_perf[1] <- paste(" ", ch_perf[1]) print(ch_perf, quote = FALSE) cat("\n") invisible(x) } predict.gafs <- function (object, newdata, ...) { newdata <- newdata[, object$optVariables, drop = FALSE] object$control$functions$pred(object$fit, newdata) } ################################################################### ## gafs <- function (x, ...) UseMethod("gafs") "gafs.default" <- function(x, y, iters = 10, popSize = 50, pcrossover = 0.8, pmutation = 0.1, elite = 0, suggestions = NULL, differences = TRUE, gafsControl = gafsControl(), ...) { startTime <- proc.time() funcCall <- match.call(expand.dots = TRUE) if(is.null(gafsControl$metric)) gafsControl$metric <- rep(ifelse(is.factor(y), "Accuracy", "RMSE"), 2) if(is.null(gafsControl$maximize)) gafsControl$maximize <- rep(ifelse(gafsControl$metric == "RMSE", FALSE, TRUE), 2) if(is.null(names(gafsControl$metric))) names(gafsControl$metric) <- c("internal", "external") if(is.null(names(gafsControl$maximize))) names(gafsControl$maximize) <- c("internal", "external") if(nrow(x) != length(y)) stop("there should be the same number of samples in x and y") numFeat <- ncol(x) classLevels <- levels(y) if(is.null(gafsControl$index)) gafsControl$index <- switch(tolower(gafsControl$method), cv = createFolds(y, gafsControl$number, returnTrain = TRUE), repeatedcv = createMultiFolds(y, gafsControl$number, gafsControl$repeats), loocv = createFolds(y, length(y), returnTrain = TRUE), boot =, boot632 = createResample(y, gafsControl$number), test = createDataPartition(y, 1, gafsControl$p), lgocv = createDataPartition(y, gafsControl$number, gafsControl$p)) if(is.null(names(gafsControl$index))) names(gafsControl$index) <- getFromNamespace("prettySeq", "caret")(gafsControl$index) ## Create hold--out indicies if(is.null(gafsControl$indexOut)){ gafsControl$indexOut <- lapply(gafsControl$index, function(training, allSamples) allSamples[-unique(training)], allSamples = seq(along = y)) names(gafsControl$indexOut) <- getFromNamespace("prettySeq", "caret")(gafsControl$indexOut) } if(!is.null(gafsControl$seeds)) { if(length(gafsControl$seeds) < length(gafsControl$index) + 1) stop(paste("There must be at least", length(gafsControl$index) + 1, "random number seeds passed to gafsControl")) } else { gafsControl$seeds <- sample.int(100000, length(gafsControl$index) + 1) } ## check summary function and metric testOutput <- data.frame(pred = sample(y, min(10, length(y))), obs = sample(y, min(10, length(y)))) if(is.factor(y)) for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput)) test <- gafsControl$functions$fitness_extern(testOutput, lev = classLevels) perfNames <- names(test) if(is.null(perfNames)) { warning(paste("The external fitness results should be a *named* vector;", "new name(s) are", paste(paste0("external", 1:length(test)), sep = "", collapse = ", ")), immediate. = TRUE) perfNames <- paste0("external", 1:length(test)) } if(!(gafsControl$metric["external"] %in% perfNames)) { warning(paste("The metric '", gafsControl$metric["external"], "' is not created by the summary function; '", perfNames[1], "' will be used instead", sep = "")) gafsControl$metric["external"] <- perfNames[1] } `%op%` <- getOper(gafsControl$allowParallel && getDoParWorkers() > 1) result <- foreach(i = seq(along = gafsControl$index), .combine = "c", .verbose = FALSE, .errorhandling = "stop") %op% { ga_select(x[gafsControl$index[[i]],,drop=FALSE], y[gafsControl$index[[i]]], funcs = gafsControl$functions, ga_maximize = gafsControl$maximize, ga_metric = gafsControl$metric, iters = iters, popSize = popSize, pcrossover = pcrossover, pmutation = pmutation, elite = elite, suggestions = suggestions, ga_verbose = gafsControl$verbose, testX = x[gafsControl$indexOut[[i]],,drop=FALSE], testY = y[gafsControl$indexOut[[i]]], ga_seed = gafsControl$seeds[i], Resample = names(gafsControl$index)[i], holdout = gafsControl$holdout, lvl = classLevels, genParallel = gafsControl$genParallel, ...) } ## TODO save only the parts you need inside of loop external <- result[names(result) == "external"] external <- do.call("rbind", external) rownames(external) <- NULL internal <- result[names(result) == "internal"] internal <- do.call("rbind", internal) rownames(internal) <- NULL selected_vars <- result[names(result) == "final"] names(selected_vars) <- names(gafsControl$index) if(differences) { diffs <- try(process_diffs(result[names(result) == "diffs"], colnames(x)), silent = TRUE) if(class(diffs)[1] == "try-error") { diffs <- NULL # warning("An error occured when computing the variable differences") } } else diffs <- NULL rm(result) if(gafsControl$verbose) cat("+ final GA\n") if(gafsControl$holdout > 0) { in_holdout <- createDataPartition(y, p = gafsControl$holdout, list = FALSE) in_model <- seq(along = y)[-unique(in_holdout)] } else { in_model <- seq(along = y) in_holdout <- NULL } final_ga <- ga_select(x[in_model,,drop=FALSE], y[in_model], funcs = gafsControl$functions, ga_maximize = gafsControl$maximize, ga_metric = gafsControl$metric, iters = iters, popSize = popSize, pcrossover = pcrossover, pmutation = pmutation, elite = elite, suggestions = suggestions, ga_verbose = gafsControl$verbose, testX = if(!is.null(in_holdout)) x[in_holdout,,drop=FALSE] else NULL, testY = if(!is.null(in_holdout)) y[in_holdout] else NULL, ga_seed = gafsControl$seeds[length(gafsControl$seeds)], lvl = classLevels, genParallel = gafsControl$genParallel, ...) averages <- ddply(external, .(Iter), function(x, nms) { apply(x[, perfNames, drop = FALSE], 2, mean) }, nms = perfNames) if(!is.null(gafsControl$functions$selectIter)) { best_index <- gafsControl$functions$selectIter(averages, metric = gafsControl$metric["external"], maximize = gafsControl$maximize["external"]) best_iter <- averages$Iter[best_index] best_vars <- colnames(x)[final_ga$subsets[[best_index]]] } else { best_index <- if(gafsControl$maximize["external"]) which.max(averages[,gafsControl$metric["external"]]) else which.min(averages[,gafsControl$metric["external"]]) best_iter <- averages$Iter[best_index] best_vars <- colnames(x)[final_ga$subsets[[best_index]]] } if(gafsControl$verbose) cat("+ final model\n") fit <- gafsControl$functions$fit(x[, best_vars, drop=FALSE], y, lev = lvls, last = TRUE, ...) endTime <- proc.time() res <- list(fit = fit, ga = final_ga, ga_param = list(popSize = popSize, pcrossover = pcrossover, pmutation = pmutation, elite = elite), external = external, internal = internal, resampled_vars = selected_vars, averages = averages, iters = iters, optVariables = best_vars, optIter = best_iter, control = gafsControl, dims = dim(x), differences = diffs, perfNames = perfNames, auto = TRUE, the_dots = list(...), call = funcCall, times = list(everything = endTime - startTime), levels = if(is.factor(y)) classLevels else NULL) ## now do analysis for whole dataset, plus make update method class(res) <- "gafs" res } ################################################################### ## plot.gafs <- function(x, metric = x$control$metric["external"], estimate = c("internal", "external"), output = "ggplot", ...) { int_names <- names(x$internal)[!(names(x$internal) %in% ga_internal_names)] ext_names <- names(x$external)[!(names(x$external) %in% ga_external_names)] common <- intersect(int_names, ext_names) both_estimates <- length(estimate) == 2 && all(sort(estimate) == c("external", "internal")) if(both_estimates){ if(!metric %in% common) stop(paste("'", metric, "' not computed in both estimates")) tmp_e <- x$external[, c("Iter", "Resample", common)] tmp_e$Estimate <- "External" tmp_i <- x$internal[, c("Iter", "Resample", common)] tmp_i$Estimate <- "Internal" plot_dat <- rbind(tmp_e, tmp_i) } else { if("internal" %in% estimate) { if(!metric %in% int_names) stop(paste("'", metric, "' not computed internally")) plot_dat <- x$internal[, c("Iter", "Resample", int_names)] } if("external" %in% estimate) { if(!metric %in% int_names) stop(paste("'", metric, "' not computed externally")) plot_dat <- x$external[, c("Iter", "Resample", ext_names)] } } if(output == "data") out <- plot_dat plot_dat <- if(both_estimates) ddply(plot_dat, c("Iter", "Estimate"), function(x) c(Mean = mean(x[, metric]))) else ddply(plot_dat, c("Iter"), function(x) c(Mean = mean(x[, metric]))) if(output == "ggplot") { out <- if(both_estimates) ggplot(plot_dat, aes(x = Iter, y = Mean, color = Estimate)) + geom_point() else ggplot(plot_dat, aes(x = Iter, y = Mean)) + geom_point() out <- out + xlab("Generation") } if(output == "lattice") { out <- if(both_estimates) xyplot(Mean ~ Iter, data = plot_dat, groups = Estimate, ...) else xyplot(Mean ~ Iter, data = plot_dat, ...) out <- update(out, xlab = "Generation") } out } ################################################################### ## caretGA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) train(x, y, ...), pred = function(object, x) { tmp <- predict(object, x) if(object$control$classProbs) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p){ perf_val <- getTrainPerf(object) perf_val <- perf_val[names(perf_val) != "method"] perf_val <- unlist(perf_val) names(perf_val) <- gsub("Train", "", names(perf_val)) perf_val }, fitness_extern = defaultSummary, initial = gafs_initial, selection = gafs_lrSelection, crossover = gafs_spCrossover, mutation = gafs_raMutation, selectIter = best) treebagGA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) { loadNamespace("ipred") ipred::ipredbagg(y, x, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p) ipredStats(object)[1:2], fitness_extern = defaultSummary, initial = gafs_initial, selection = gafs_lrSelection, crossover = gafs_spCrossover, mutation = gafs_raMutation, selectIter = best) rfGA <- list(fit = function(x, y, lev = NULL, last = FALSE, ...) { loadNamespace("randomForest") randomForest::randomForest(x, y, ...) }, pred = function(object, x) { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp out }, fitness_intern = function(object, x, y, maximize, p) rfStats(object), fitness_extern = defaultSummary, initial = gafs_initial, selection = gafs_lrSelection, crossover = gafs_spCrossover, mutation = gafs_raMutation, selectIter = best) update.gafs <- function(object, iter, x, y, ...) { iter <- iter[1] if(iter > length(object$ga$subsets)) stop(paste("iter must be less than", length(object$ga$subsets))) if(is.null(x) | is.null(y)) stop("the original training data is needed to refit the model") args <- list(x = x[, object$ga$subsets[[iter]], drop=FALSE], y = y, lev = object$levels, last = TRUE) if(length(object$the_dots) > 0) args <- c(args, object$the_dots) if(object$control$verbose) cat("Refitting model to use", length(object$ga$subsets[[iter]]), "predictors from generation", iter, "\n") object$fit <- do.call(object$control$functions$fit, args) object$auto <- FALSE object$optVariables <- colnames(x)[object$ga$subsets[[iter]]] object$optIter <- iter object } "varImp.gafs" <- function(object, metric = object$control$metric["external"], maximize = object$control$maximize["external"], ...) { if(is.null(object$differences)) stop("must have used `differences = TRUE`") out <- object$differences[,metric, drop = FALSE] rownames(out) <- as.character(object$differences$Variable) if(!maximize) out[, metric, drop = FALSE] <- -out[, metric, drop = FALSE] out <- out[order(-out[, metric]),, drop = FALSE] out } caret/R/specificity.R0000644000175000017500000000356612453550064014701 0ustar rbalintrbalintspecificity <- function(data, ...){ UseMethod("specificity") } "specificity.default" <- function(data, reference, negative = levels(reference)[-1], na.rm = TRUE, ...) { if(!is.factor(reference) | !is.factor(data)) stop("input data must be a factor") ## todo: relax the =2 constraint and let ngative length be > 2 if(length(unique(c(levels(reference), levels(data)))) != 2) stop("input data must have the same two levels") if(na.rm) { cc <- complete.cases(data) & complete.cases(reference) if(any(!cc)) { data <- data[cc] reference <- reference[cc] } } numer <- sum(data %in% negative & reference %in% negative) denom <- sum(reference %in% negative) spec <- ifelse(denom > 0, numer / denom, NA) spec } "specificity.table" <- function(data, negative = rownames(data)[-1], ...) { ## "truth" in columns, predictions in rows if(!all.equal(nrow(data), ncol(data))) stop("the table must have nrow = ncol") if(!all.equal(rownames(data), colnames(data))) stop("the table must the same groups in the same order") if(nrow(data) > 2) { tmp <- data data <- matrix(NA, 2, 2) colnames(data) <- rownames(data) <- c("pos", "neg") negCol <- which(colnames(tmp) %in% negative) posCol <- which(!(colnames(tmp) %in% negative)) data[1, 1] <- sum(tmp[posCol, posCol]) data[1, 2] <- sum(tmp[posCol, negCol]) data[2, 1] <- sum(tmp[negCol, posCol]) data[2, 2] <- sum(tmp[negCol, negCol]) data <- as.table(data) negative <- "neg" rm(tmp) } numer <- sum(data[negative, negative]) denom <- sum(data[, negative]) spec <- ifelse(denom > 0, numer / denom, NA) spec } "specificity.matrix" <- function(data, negative = rownames(data)[-1], ...) { data <- as.table(data) specificity.table(data) } caret/R/icr.R0000644000175000017500000000656012516214271013135 0ustar rbalintrbalint icr <- function (x, ...) UseMethod("icr") icr.formula <- function (formula, data, weights, ..., subset, na.action, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) m$... <- m$contrasts <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] w <- model.weights(m) if (length(w) == 0) w <- rep(1, nrow(x)) y <- model.response(m) res <- icr.default(x, y, weights = w, thresh = thresh, ...) res$terms <- Terms res$coefnames <- colnames(x) res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) class(res) <- c("icr.formula", "icr") res } icr.default <- function(x, y, ...) { xNames <- colnames(x) pp <- preProcess(x, "ica", ...) x <- predict(pp, x) if(is.factor(y)) stop("y must be numeric") data <- if(is.data.frame(x)) x else as.data.frame(x) data$y <- y modelFit <- lm(y ~ ., data = data) out <- list(model = modelFit, ica = pp, dim = dim(x), n.comp = list(...)$n.comp, names = xNames) class(out) <- "icr" out } print.icr <- function (x, digits = max(3, getOption("digits") - 3), ...) { cat("Independent Component Regression\n\n") cat("Created from", x$dim[1], "samples and", x$dim[2], "variables\n\n") if (length(coef(x$model))) { cat("Coefficients:\n") print.default( format( coef(x$model), digits = digits), print.gap = 2, quote = FALSE) } else cat("No coefficients\n") cat("\n") invisible(x) } predict.icr <- function(object, newdata, ...) { loadNamespace("fastICA") if (!inherits(object, "icr")) stop("object not of class \"icr\"") if (missing(newdata)) { return(fitted(object$model)) } else { if (inherits(object, "icr.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.omit, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) x <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] } else { if (is.null(dim(newdata))) dim(newdata) <- c(1, length(newdata)) x <- as.matrix(newdata) if (any(is.na(x))) stop("missing values in 'x'") keep <- 1:nrow(x) rn <- rownames(x) } } if(!is.null(object$names)) { x <- x[, object$names, drop = FALSE] } if(!is.data.frame(x)) x <- as.data.frame(x) x <- predict(object$ica, x) predict(object$model, x, ...) } caret/R/calibration.R0000644000175000017500000001011612516214271014637 0ustar rbalintrbalintcalibration <- function(x, ...) UseMethod("calibration") calibration.default <- function(x, ...) stop("'x' should be a formula") calibration.formula <- function(x, data = NULL, class = NULL, cuts = 11, subset = TRUE, lattice.options = NULL, ...) { if (!is.null(lattice.options)) { oopt <- lattice.options(lattice.options) on.exit(lattice.options(oopt), add = TRUE) } formula <- x groups <- NULL subset <- eval(substitute(subset), data, environment(x)) form <- latticeParseFormula(formula, data, subset = subset, groups = groups, multiple = TRUE, outer = TRUE, subscripts = TRUE, drop = TRUE) calibData <- data.frame(prob = form$y) probNames <- strsplit(form$right.name, " + ", fixed = TRUE)[[1]] calibData <- data.frame(calibClassVar = rep(form$left, length(probNames)), calibProbVar = form$right) calibData$calibModelVar <- if(length(probNames) > 1) form$condition[[length(form$condition)]] else probNames if(length(form$condition) > 0 && any(names(form$condition) != "")) { ind <- sum(names(form$condition) != "") tmp <- as.data.frame(form$condition[1:ind]) calibData <- cbind(calibData, tmp) } if(!is.factor(calibData$calibClassVar)) stop("the left-hand side of the formula must be a factor of classes") splitVars <- names(calibData)[!(names(calibData) %in% c("calibClassVar", "calibProbVar"))] if(is.null(class)) class <- levels(calibData$calibClassVar)[1] plotData <- ddply(calibData, splitVars, calibCalc, class = class, cuts = cuts) out <- list(data = plotData, cuts = cuts, class = class, probNames = probNames, call = match.call()) class(out) <- "calibration" out } print.calibration <- function(x, ...) { printCall(x$call) cat("Models:", paste(unique(x$data$calibModelVar), collapse = ", "), "\n") cat("Event: ", x$class, "\n") cat("Cuts:", x$cuts, "\n") invisible(x) } calibCalc <- function(x, class = levels(obs)[1], cuts = 11) { if(length(cuts) == 1) { num_cuts <- cuts cuts <- (0:num_cuts)/num_cuts } else { cuts <- unique(c(0, cuts, 1)) num_cuts <- length(cuts) } binData <- data.frame(prob = x$calibProbVar, bin = cut(x$calibProbVar, cuts, include.lowest = TRUE), class = x$calibClassVar) dataPoints <- ddply(binData, .(bin), function(x, cls) c(Percent = mean(x$class == cls)*100, Count = sum(x$class == cls)), cls = class, .drop = FALSE) dataPoints$midpoint <- NA for(i in 2:length(cuts)) dataPoints$midpoint[i-1] <- .5*(cuts[i] + cuts[i-1]) * 100 dataPoints$Percent <- ifelse(dataPoints$Count == 0, 0, dataPoints$Percent) dataPoints } plot.calibration <- function(x, y = NULL, ...) xyplot(x = x, data = NULL, ...) xyplot.calibration <- function(x, data = NULL, ...) { lFormula <- "Percent ~ midpoint" defaults <- c("calibModelVar", "bin", "Percent", "Count", "midpoint") extras <- names(x$data)[!(names(x$data) %in% defaults)] if(length(extras) > 0) lFormula <- paste(lFormula, paste(extras, collapse = "*"), sep = "|") rng <- extendrange(c(0, 100)) opts <- list(...) if(!any(names(opts) == "xlab")) opts$xlab <- "Bin Midpoint" if(!any(names(opts) == "ylab")) opts$ylab <- "Observed Event Percentage" if(!any(names(opts) == "type")) opts$type <- "o" if(!any(names(opts) == "ylim")) opts$ylim <- rng if(!any(names(opts) == "xlim")) opts$xlim <- rng if(!any(names(opts) == "panel")) opts$panel <- panel.calibration args <- list(x = as.formula(lFormula), data = x$data) if(length(x$probNames) > 1) args$groups <- x$data$calibModelVar args <- c(args, opts) do.call("xyplot", args) } panel.calibration <- function(...) { panel.abline(0, 1, col = trellis.par.get("reference.line")$col, lwd = trellis.par.get("reference.line")$lwd, lty = trellis.par.get("reference.line")$lty) panel.xyplot(...) } caret/R/print.mars.R0000644000175000017500000000076012453550064014454 0ustar rbalintrbalintprint.bmars <- function (x, ...) { cat("\nCall:\n", deparse(x$call), "\n\n", sep = "") if(!is.null(x$x))cat("Data:\n # variables:\t", dim(x$x)[2], "\n # samples:\t", dim(x$x)[1], "\n") cat( "\nModel:", "\n B: \t", x$B, "\n degree: \t", x$fit[[1]]$degree, "\n nk: \t", x$fit[[1]]$nk, "\n penalty: \t", x$fit[[1]]$penalty, "\n threshold:\t", x$fit[[1]]$thresh, "\n") cat("\n") invisible(x) } caret/R/knn3.R0000644000175000017500000000666212516214271013234 0ustar rbalintrbalint"knn3" <- function(x, ...) UseMethod("knn3") knn3.default <- function(x, ...) { if(!any(class(x) %in% "formula")) stop("knn3 only implemented for formula objects") } knn3.formula <- function (formula, data, subset, na.action, k = 5, ...) { if (missing(formula) || (length(formula) != 3) || (length(attr(terms(formula[-2], data = data), "term.labels")) < 1) || (length(attr(terms(formula[-3], data = data), "term.labels")) != 1)) stop("formula missing or incorrect") m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data, parent.frame()))) m$data <- as.data.frame(data) m[[1]] <- as.name("model.frame") m$... <- NULL m$k <- NULL m <- eval(m, parent.frame()) Terms <- attr(m, "terms") y <- model.extract(m, "response") x <- model.matrix(Terms, m) xvars <- as.character(attr(Terms, "variables"))[-1] if ((yvar <- attr(Terms, "response")) > 0) xvars <- xvars[-yvar] xlev <- if (length(xvars) > 0) { xlev <- lapply(m[xvars], levels) xlev[!sapply(xlev, is.null)] } xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] RET <- list(learn = list(y = y, X = x)) RET$k <- k RET$terms <- Terms RET$contrasts <- attr(x, "contrasts") RET$xlevels <- xlev RET$theDots <- list(...) attr(RET, "na.message") <- attr(m, "na.message") if (!is.null(attr(m, "na.action"))) RET$na.action <- attr(m, "na.action") class(RET) <- "knn3" RET } knn3.data.frame <- function(x, y, k = 5, ...) { x <- as.matrix(x) out <- knn3(x, y = y, k = k, ...) out } knn3.matrix <- function(x, y, k = 5, ...) { if(!is.matrix(x)) x <- as.matrix(x) if(!is.factor(y)) stop("y must be a factor") RET <- list(learn = list(y = y, X = x)) RET$k <- k RET$terms <- NULL RET$contrasts <- NULL RET$xlevels <- NULL RET$theDots <- list(...) class(RET) <- "knn3" RET } print.knn3 <- function (x, ...) { cat(x$k, "-nearest neighbor classification model\n", sep = "") cat("Training set class distribution:\n") print(table(x$learn$y)) cat("\n") invisible(x) } predict.knn3 <- function (object, newdata, type = c("prob", "class"), ...) { type <- match.arg(type) if (!inherits(object, "knn3")) stop("object not of class knn3") if (!is.null(Terms <- object$terms)) { if (missing(newdata)) newdata <- model.frame(object) else { newdata <- model.frame(as.formula(delete.response(Terms)), newdata, na.action = function(x) x, xlev = object$xlevels) } x <- model.matrix(delete.response(Terms), newdata, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] } else { x <- as.matrix(newdata) } argList <- list( train = object$learn$X, test = x, cl = object$learn$y, k = object$k) if(length(object$theDots) == 0) object$theDots <- list(prob = TRUE) if(any(names(object$theDots) == "prob")) object$theDots$prob <- TRUE argList <- c(argList, object$theDots) RET <- do.call( "knn3Train", argList) if (type == "prob") { return(attr(RET, "prob")) } else { RET <- factor(RET, levels = levels(object$learn$y)) return(RET) } } caret/R/createDataPartition.R0000644000175000017500000000267312516214271016310 0ustar rbalintrbalintcreateDataPartition <- function (y, times = 1, p = 0.5, list = TRUE, groups = min(5, length(y))){ out <- vector(mode = "list", times) if(length(y) < 2) stop("y must have at least 2 data points") if(groups < 2) groups <- 2 if(is.numeric(y)) { y <- cut(y, unique(quantile(y, probs = seq(0, 1, length = groups))), include.lowest = TRUE) } else { xtab <- table(y) if(any(xtab == 0)) { warning(paste("Some classes have no records (", paste(names(xtab)[xtab == 0], sep = "", collapse = ", "), ") and these will be ignored")) y <- factor(as.character(y)) } if(any(xtab == 1)) { warning(paste("Some classes have a single record (", paste(names(xtab)[xtab == 1], sep = "", collapse = ", "), ") and these will be selected for the sample")) } } subsample <- function(dat, p) { if(nrow(dat) == 1) { out <- dat$index } else { num <- ceiling(nrow(dat) * p) out <- sample(dat$index, size = num) } out } for (j in 1:times) { tmp <- dlply(data.frame(y = y, index = seq(along = y)), .(y), subsample, p = p) tmp <- sort(as.vector(unlist(tmp))) out[[j]] <- tmp } if (!list) { out <- matrix(unlist(out), ncol = times) colnames(out) <- prettySeq(1:ncol(out)) } else { names(out) <- prettySeq(out) } out } caret/R/twoClassSim.R0000644000175000017500000000537512516214271014633 0ustar rbalintrbalinttwoClassSim <- function(n = 100, intercept = -5, linearVars = 10, noiseVars = 0, ## Number of uncorrelated x's corrVars = 0, ## Number of correlated x's corrType = "AR1", ## Corr structure ('AR1' or 'exch') corrValue = 0, ## Corr parameter mislabel = 0) { requireNamespaceQuietStop("MASS") sigma <- matrix(c(2,1.3,1.3,2),2,2) tmpData <- data.frame(MASS::mvrnorm(n=n, c(0,0), sigma)) names(tmpData) <- paste("TwoFactor", 1:2, sep = "") if(linearVars > 0) { tmpData <- cbind(tmpData, matrix(rnorm(n*linearVars), ncol = linearVars)) colnames(tmpData)[(1:linearVars)+2] <- paste("Linear", gsub(" ", "0", format(1:linearVars)), sep = "") } tmpData$Nonlinear1 <- runif(n, min = -1) tmpData <- cbind(tmpData, matrix(runif(n*2), ncol = 2)) colnames(tmpData)[(ncol(tmpData)-1):ncol(tmpData)] <- paste("Nonlinear", 2:3, sep = "") tmpData <- as.data.frame(tmpData) p <- ncol(tmpData) if(noiseVars > 0) { tmpData <- cbind(tmpData, matrix(rnorm(n * noiseVars), ncol = noiseVars)) colnames(tmpData)[(p+1):ncol(tmpData)] <- paste("Noise", gsub(" ", "0", format(1:noiseVars)), sep = "") } if(corrVars > 0) { p <- ncol(tmpData) loadNamespace("MASS") if(corrType == "exch") { vc <- matrix(corrValue, ncol = corrVars, nrow = corrVars) diag(vc) <- 1 } if(corrType == "AR1") { vcValues <- corrValue^(seq(0, corrVars - 1, by = 1)) vc <- toeplitz(vcValues) } tmpData <- cbind(tmpData, MASS::mvrnorm(n, mu = rep(0, corrVars), Sigma = vc)) colnames(tmpData)[(p+1):ncol(tmpData)] <- paste("Corr", gsub(" ", "0", format(1:corrVars)), sep = "") } lp <- intercept - 4 * tmpData$TwoFactor1 + 4*tmpData$TwoFactor2 + 2*tmpData$TwoFactor1*tmpData$TwoFactor2 + (tmpData$Nonlinear1^3) + 2 * exp(-6*(tmpData$Nonlinear1 - 0.3)^2) + 2*sin(pi*tmpData$Nonlinear2* tmpData$Nonlinear3) if(linearVars > 0) { lin <- seq(10, 1, length = linearVars)/4 lin <- lin * rep(c(-1, 1), floor(linearVars)+1)[1:linearVars] for(i in seq(along = lin)) lp <- lp + tmpData[, i+3]*lin[i] } prob <- binomial()$linkinv(lp) if(mislabel > 0 & mislabel < 1) { shuffle <- sample(1:nrow(tmpData), floor(nrow(tmpData)*j)) prob[shuffle] <- 1 - prob[shuffle] } tmpData$Class <- ifelse(prob <= runif(n), "Class1", "Class2") tmpData$Class <- factor(tmpData$Class, levels = c("Class1", "Class2")) tmpData } caret/R/diag.R0000644000175000017500000000220212453550064013254 0ustar rbalintrbalint# File src/library/stats/R/contrast.R # Part of the R package, http://www.R-project.org # # Copyright (C) 1995-2012 The R Core Team # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ ## a fast version of diag(n, .) / sparse-Diagonal() + dimnames ## Originally .Diag in stats:contrast.R .RDiag <- function (nms, sparse) { n <- as.integer(length(nms)) d <- c(n, n) dn <- list(nms, nms) # if (sparse) { # requireNamespace("Matrix", quietly = TRUE) # new("ddiMatrix", diag = "U", Dim = d, Dimnames = dn) # } # else array(c(rep.int(c(1, numeric(n)), n - 1L), 1), d, dn) }caret/R/bagFDA.R0000644000175000017500000001206112516214271013415 0ustar rbalintrbalint"bagFDA" <- function(x, ...) UseMethod("bagFDA") "bagFDA.default" <- function(x, y, weights = NULL, B = 50, keepX = TRUE, ...) { requireNamespaceQuietStop("mda") requireNamespaceQuietStop("earth") if(!is.matrix(x)) x <- as.matrix(x) if(!is.vector(y) & !is.factor(y)) y <- as.vector(y) if(!is.vector(y) & !is.factor(y)) y <- factor(y[,1]) if(is.null(weights)) weights <- rep(1, dim(x)[1]) foo <- function(index, x, y, w, ...) { subX <- x[index,, drop = FALSE] subY <- y[index] #'weights' are not yet supported by 'earth' # subW <- weights[index] tmp <- as.data.frame(subX) tmp$.outcome <- subY fit <- mda::fda(.outcome ~., data = tmp, method = earth::earth, ...) fit$index <- index fit } oobFoo <- function(fit, x, y) { index <- fit$index subX <- x[-index,, drop = FALSE] subY <- y[-index] predY <- predict(fit, subX) postResample(predY, subY) } btSamples <- createResample(y, times = B) btFits <- lapply(btSamples, foo, x = x, y = y, w = weights, ...) oobList <- lapply(btFits, oobFoo, x = x, y = y) oob <- matrix(unlist(oobList), ncol = length(oobList[[1]]), byrow = TRUE) colnames(oob) <- names(oobList[[1]]) if(keepX) x <- x else x <- NULL structure(list(fit = btFits, B = B, oob = oob, x = x, levels = levels(y), dots = list(...)), class = "bagFDA") } "bagFDA.formula" <- function (formula, data = NULL, B = 50, keepX = TRUE, ..., subset, weights, na.action = na.omit) { if (!inherits(formula, "formula")) stop("method is only for formula objects") m <- match.call(expand.dots = FALSE) mIndex <- match(c("formula", "data", "subset", "weights", "na.action"), names(m), 0) m <- m[c(1, mIndex)] m$... <- NULL m$na.action <- na.action m[[1]] <- as.name("model.frame") m <- eval(m, parent.frame()) Terms <- attr(m, "terms") attr(Terms, "intercept") <- 0 y <- model.response(m) w <- model.weights(m) x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] out <- bagFDA.default(x, y, w, B = B, keepX = keepX, ...) out } "print.bagFDA" <- function (x, ...) { if(!is.null(x$x))cat("Data:\n # variables:\t", dim(x$x)[2], "\n # samples:\t", dim(x$x)[1], "\n") cat( "\nModel:", "\n B: \t", x$B, "\n dimension:\t", x$fit[[1]]$dimension, "\n") cat("\n") invisible(x) } "predict.bagFDA" <- function(object, newdata = NULL, type = "class", ...) { requireNamespaceQuietStop("mda") requireNamespaceQuietStop("earth") getTrainPred <- function(x) { oobIndex <- 1:nrow(x$fit$fitted.values) oobIndex <- oobIndex[!(oobIndex %in% unique(x$index))] tmp <- predict(x, type = "posterior")[oobIndex,,drop = FALSE] rownames(tmp) <- 1:nrow(tmp) out <- data.frame(pred = tmp, sample = oobIndex, check.rows = FALSE) colnames(out)[1:ncol(tmp)] <- names(x$prior) out } if(is.null(newdata) & !is.null(object$x)) newdata <- object$x if(is.null(newdata)) { pred <- lapply(object$fit, getTrainPred) } else { pred <- lapply(object$fit, function(x, y) { tmp <- predict(x, newdata = y, type = "posterior") nms <- colnames(tmp) tmp <- as.data.frame(tmp) names(tmp) <- nms tmp$sample <- 1:nrow(tmp) tmp }, y = newdata) } pred <- rbind.fill(pred) out <- ddply(pred, .(sample), function(x) colMeans(x[,seq(along = object$levels)], na.rm = TRUE)) out <- out[,-1,drop = FALSE] rownames(out) <- rownames(newdata) predClass <- object$levels[apply(out, 1, which.max)] predClass <- factor(predClass, levels = object$levels) switch(type, class = predClass, probs = out, posterior = out) } "summary.bagFDA" <- function(object, ...) { oobStat <- apply(object$oob, 2, function(x) quantile(x, probs = c(0, 0.025, .5, .975, 1))) numTerms <- unlist(lapply(object$fit, function(x) length(x$fit$selected.terms))) numVar <- unlist(lapply( object$fit, function(x) { sum( apply( x$fit$dirs, 2, function(u) any(u != 0))) })) modelInfo <- cbind(numTerms, numVar) colnames(modelInfo) <- c("Num Terms", "Num Variables") out <- list(modelInfo = modelInfo, oobStat = oobStat) class(out) <- "summary.bagFDA" out } "print.summary.bagFDA" <- function(x, digits = max(3, getOption("digits") - 3), ...) { oobStat <- apply(x$oob, 2, function(x) quantile(x, probs = c(0, 0.025, .25, .5, .75, .975, 1))) cat("Out of bag statistics:\n\n") print(x$oobStat, digits = digits) cat("\nModel Selection Statistics:\n\n") print(summary(x$modelInfo)) cat("\n") } caret/R/predict.train.R0000644000175000017500000000373112453550064015126 0ustar rbalintrbalintpredict.list <- function(object, ...) { out <- lapply(object, predict, ... = ...) if(!is.null(names(object))) names(out) <- names(object) out } predict.train <- function(object, newdata = NULL, type = "raw", na.action = na.omit, ...) { if(all(names(object) != "modelInfo")) { object <- update(object, param = NULL) } if(!is.null(object$modelInfo$library)) for(i in object$modelInfo$library) do.call("require", list(package = i)) if(!(type %in% c("raw", "prob"))) stop("type must be either \"raw\" or \"prob\"") if(type == "prob") { if (is.null(object$modelInfo$prob)) stop("only classification models that produce probabilities are allowed") } if(!is.null(newdata)) { if (inherits(object, "train.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) newdata <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(newdata), nomatch = 0) if (xint > 0) newdata <- newdata[, -xint, drop = FALSE] } } else { if(!is.null(object$trainingData)) { newdata <- if(object$method == "pam") object$finalModel$xData else object$trainingData ##newdata$.outcome <-NULL } else stop("please specify data via newdata") } if(type == "prob") { out <- extractProb(list(object), unkX = newdata, unkOnly = TRUE, ...) obsLevels <- levels(object) out <- out[, obsLevels, drop = FALSE] } else { out <- extractPrediction(list(object), unkX = newdata, unkOnly = TRUE, ...)$pred } out } caret/R/panel.needle.R0000644000175000017500000000302612453550064014707 0ustar rbalintrbalint"panel.needle" <- function (x, y, horizontal = TRUE, pch = if (is.null(groups)) dot.symbol$pch else sup.symbol$pch, col = if (is.null(groups)) dot.symbol$col else sup.symbol$col, lty = dot.line$lty, lwd = dot.line$lwd, col.line = dot.line$col, levels.fos = NULL, groups = NULL, ...) { #adapted from panel.dotplot in lattice x <- as.numeric(x) y <- as.numeric(y) dot.line <- trellis.par.get("dot.line") dot.symbol <- trellis.par.get("dot.symbol") sup.symbol <- trellis.par.get("superpose.symbol") if (horizontal) { yscale <- extendrange(y, f = .2) if (is.null(levels.fos)) levels.fos <- floor(yscale[2]) - ceiling(yscale[1]) + 1 panel.abline(v = 0, col =1, lty = 1, lwd = 1) pch <- rep(pch, length(x)) pch <- ifelse(x == 0, NA, pch) for(i in seq(along=x)) lsegments(x[i], y[i], 0, y[i]) if (is.null(groups)) panel.xyplot(x = x, y = y, col = col, pch = pch, ...) else panel.superpose(x = x, y = y, groups = groups, col = col, pch = pch, ...) } else { xscale <- extendrange(x, f = .2) if (is.null(levels.fos)) levels.fos <- floor(xscale[2]) - ceiling(xscale[1]) + 1 panel.abline(h = 0, col = col.line, lty = lty, lwd = lwd) pch <- rep(pch, length(x)) pch <- ifelse(x == 0, NA, pch) if (is.null(groups))panel.xyplot(x = x, y = y, col = col, pch = pch, ...) else panel.superpose(x = x, y = y, groups = groups, col = col, pch = pch, ...) } } caret/R/misc.R0000644000175000017500000003137312516214271013313 0ustar rbalintrbalint subsemble_index <- function(y, J = 2, V = 10){ dat <- data.frame(y = y, index = seq(along = y)) outer_index <- sample(1:J, size = nrow(dat), replace = TRUE) outer_splits <- vector(mode = "list", length = J) for(i in 1:J) { outer_splits[[i]] <- dat[outer_index == i,] outer_splits[[i]]$label <- well_numbered("Outer", J)[i] } foo <- function(x, V = 10) { inner_index_0 <- createFolds(x$y, k = V, returnTrain = TRUE) modeling_index <- lapply(inner_index_0, function(x, y) y[x], y = x$index) holdout_index <- lapply(inner_index_0, function(x, y) y[-unique(x)], y = x$index) names(modeling_index) <- names(holdout_index) <- paste(x$label[1], names(modeling_index), sep = ".") list(model = modeling_index, holdout = holdout_index) } all_index <- lapply(outer_splits, foo, V = V) model_index <- holdout_index <- NULL for(i in seq(along = all_index)) { model_index <- c(model_index, all_index[[i]]$model) holdout_index <- c(holdout_index, all_index[[i]]$holdout) } list(model = model_index, holdout = holdout_index) } well_numbered <- function(prefix, items) { paste0(prefix, gsub(" ", "0", format(1:items))) } evalSummaryFunction <- function(y, wts, ctrl, lev, metric, method) { ## get phoney performance to obtain the names of the outputs testOutput <- data.frame(pred = sample(y, min(10, length(y))), obs = sample(y, min(10, length(y)))) if(ctrl$classProbs) { for(i in seq(along = lev)) testOutput[, lev[i]] <- runif(nrow(testOutput)) testOutput[, lev] <- t(apply(testOutput[, lev], 1, function(x) x/sum(x))) } else { if(metric == "ROC" & !ctrl$classProbs) stop("train()'s use of ROC codes requires class probabilities. See the classProbs option of trainControl()") } if(!is.null(wts)) testOutput$weights <- sample(wts, min(10, length(wts))) testOutput$rowIndex <- sample(seq(along = y), size = nrow(testOutput)) ctrl$summaryFunction(testOutput, lev, method) } hasDots <- function(grid, info) { mnames <- sort(as.character(info$parameters$parameter)) mnames2 <- paste(".", mnames, sep = "") gnames <- sort(colnames(grid)) out <- all.equal(mnames2, gnames) if(class(out)[1] != "logical") out <- FALSE out } model2method <- function(x) { ## There are some disconnecs between the object class and the ## method used by train. switch(x, randomForest = "rf", rvm = "rvmRadial", ksvm = "svmRadial", lssvm = "lssvmRadial", gausspr = "gaussprRadial", NaiveBayes = "nb", classbagg =, regbagg = "treebag", plsda = "pls", pamrtrained = "pam", x) } Kim2009 <- function(n) { grid <- matrix(runif(n*10), ncol = 10) grid <- as.data.frame(grid) names(grid) = paste("x", 1:10, sep = "") grid$x5 <- floor((grid$x5*3)+1) pred <- -10 + 10 * sin(pi * grid$x1* grid$x2) + 5*(grid$x3 - .5)^2 + 5*grid$x4 + 2*grid$x5 prob <- binomial()$linkinv(pred) grid$Class <- ifelse(prob <= runif(n), "Class1", "Class2") grid$Class <- factor(grid$Class, levels = c("Class1","Class2")) grid } gamFormula <- function(data, smoother = "s", cut = 8, y = "y") { nzv <- nearZeroVar(data) if(length(nzv) > 0) data <- data[, -nzv, drop = FALSE] numValues <- apply(data, 2, function(x) length(unique(x))) prefix <- rep("", ncol(data)) prefix[numValues > cut] <- paste(smoother, "(", sep = "") suffix <- rep("", ncol(data)) suffix[numValues > cut] <- ")" rhs <- paste(prefix, names(numValues), suffix, sep = "") rhs <- paste(rhs, collapse = "+") form <- as.formula(paste(y, "~", rhs, sep = "")) form } printCall <- function(x) { call <- paste(deparse(x), collapse = "\n") # cat("\nCall:\n", call, "\n\n", sep = "") ## or cat("\nCall:\n", truncateText(deparse(x, width.cutoff = 500)), "\n\n", sep = "") invisible(call) } flatTable <- function(pred, obs) { cells <- as.vector(table(pred, obs)) if(length(cells) == 0) cells <- rep(NA, length(levels(obs))^2) names(cells) <- paste(".cell", seq(along= cells), sep = "") cells } prettySeq <- function(x) paste("Resample", gsub(" ", "0", format(seq(along = x))), sep = "") ipredStats <- function(x) { requireNamespaceQuietStop("e1071") ## error check if(is.null(x$X)) stop("to get OOB stats, keepX must be TRUE when calling the bagging function") foo <- function(object, y, x) { holdY <- y[-object$bindx] if(is.factor(y)) { requireNamespaceQuietStop("e1071") tmp <- predict(object$btree, x[-object$bindx,,drop = FALSE], type = "class") tmp <- factor(as.character(tmp), levels = levels(y)) out <- c( mean(holdY == tmp), e1071::classAgreement(table(holdY, tmp))$kappa) } else { tmp <- predict(object$btree, x[-object$bindx,,drop = FALSE]) out <- c( sqrt(mean((tmp - holdY)^2, na.rm = TRUE)), cor(holdY, tmp, use = "pairwise.complete.obs")^2) } out } eachStat <- lapply(x$mtrees, foo, y = x$y, x = x$X) eachStat <- matrix(unlist(eachStat), nrow = length(eachStat[[1]])) out <- c( apply(eachStat, 1, mean, na.rm = TRUE), apply(eachStat, 1, sd, na.rm = TRUE)) names(out) <- if(is.factor(x$y)) c("Accuracy", "Kappa", "AccuracySD", "KappaSD") else c("RMSE", "Rsquared", "RMSESD", "RsquaredSD") out } rfStats <- function(x) { out <- switch( x$type, regression = c(sqrt(max(x$mse[length(x$mse)], 0)), x$rsq[length(x$rsq)]), classification = { requireNamespaceQuietStop("e1071") c( 1 - x$err.rate[x$ntree, "OOB"], e1071::classAgreement(x$confusion[,-dim(x$confusion)[2]])[["kappa"]]) }) names(out) <- if(x$type == "regression") c("RMSE", "Rsquared") else c("Accuracy", "Kappa") out } cforestStats <- function(x) { loadNamespace("party") obs <- x@data@get("response")[,1] pred <- predict(x, x@data@get("input"), OOB = TRUE) postResample(pred, obs) } bagEarthStats <- function(x) apply(x$oob, 2, function(x) quantile(x, probs = .5)) R2 <- function(pred, obs, formula = "corr", na.rm = FALSE) { n <- sum(complete.cases(pred)) switch(formula, corr = cor(obs, pred, use = ifelse(na.rm, "complete.obs", "everything"))^2, traditional = 1 - (sum((obs-pred)^2, na.rm = na.rm)/((n-1)*var(obs, na.rm = na.rm)))) } RMSE <- function(pred, obs, na.rm = FALSE) sqrt(mean((pred - obs)^2, na.rm = na.rm)) partRuleSummary <- function(x) { predictors <- all.vars(x$terms) predictors <- predictors[predictors != as.character(x$terms[[2]])] classes <- levels(x$predictions) rules <- capture.output(print(x)) conditions <- grep("(<=|>=|<|>|=)", rules, value = TRUE) classPred <- grep("\\)$", conditions, value = TRUE) varUsage <- data.frame(Var = predictors, Overall = 0) for(i in seq(along = predictors)) varUsage$Overall[i] <- sum(grepl(paste("^", predictors[i], sep = ""), conditions)) numClass <- rep(NA, length(classes)) names(numClass) <- classes for(i in seq(along = classes)) numClass[i] <- sum(grepl(paste(":", classes[i], sep = " "), classPred)) list(varUsage = varUsage, numCond = length(conditions), classes = numClass) } ripperRuleSummary <- function(x) { predictors <- all.vars(x$terms) predictors <- predictors[predictors != as.character(x$terms[[2]])] classes <- levels(x$predictions) rules <- capture.output(print(x)) ## remove header rules <- rules[-(1:min(which(rules == "")))] conditions <- grep("(<=|>=|<|>|=)", rules, value = TRUE) varUsage <- data.frame(Var = predictors, Overall = 0) for(i in seq(along = predictors)) varUsage$Overall[i] <- sum(grepl(paste("\\(", predictors[i], sep = ""), conditions)) numClass <- rep(NA, length(classes)) names(numClass) <- classes for(i in seq(along = classes)) numClass[i] <- sum(grepl(paste(x$terms[[2]], "=", classes[i], sep = ""), conditions)) list(varUsage = varUsage, numCond = length(conditions), classes = numClass) } ########################################################################################################## ## splitIndicies takes a number of tasks (n) and divides it into k groups ## of roughly equal size. The result is an integer vector of task groups splitIndicies <- function(n, k) { out <- rep(1:k, n%/%k) if(n %% k > 0) out <- c(out, sample(1:k, n %% k)) sort(out) } ## This makes a list of copies of another list repList <- function(x, times = 3, addIndex = FALSE) { out <- vector(mode = "list", length = times) out <- lapply(out, function(a, b) b, b = x) if(addIndex) for(i in seq(along = out)) out[[i]]$.index <- i out } useMathSymbols <- function(x) { if(x == "Rsquared") x <- expression(R^2) x } depth2cp <- function(x, depth) { out <- approx(x[,"nsplit"], x[,"CP"], depth)$y out[depth > max(x[,"nsplit"])] <- min(x[,"CP"]) * .99 out } smootherFormula <- function(data, smoother = "s", cut = 10, df = 0, span = .5, degree = 1, y = ".outcome") { nzv <- nearZeroVar(data) if(length(nzv) > 0) data <- data[, -nzv, drop = FALSE] numValues <- sort(apply(data, 2, function(x) length(unique(x)))) prefix <- rep("", ncol(data)) suffix <- rep("", ncol(data)) prefix[numValues > cut] <- paste(smoother, "(", sep = "") if(smoother == "s") { suffix[numValues > cut] <- if(df == 0) ")" else paste(", df=", df, ")", sep = "") } if(smoother == "lo") { suffix[numValues > cut] <- paste(", span=", span, ",degree=", degree, ")", sep = "") } if(smoother == "rcs") { suffix[numValues > cut] <- ")" } rhs <- paste(prefix, names(numValues), suffix, sep = "") rhs <- paste(rhs, collapse = "+") form <- as.formula(paste(y, rhs, sep = "~")) form } varSeq <- function(x) { vars <- apply(summary(x)$which, 1, function(x) names(which(x))) vars <- lapply(vars, function(x) x[x != "(Intercept)"]) vars } cranRef <- function(x) paste("{\\tt \\href{http://cran.r-project.org/web/packages/", x, "/index.html}{", x, "}}", sep = "") makeTable <- function(x) { params <- paste("\\code{", as.character(x$parameter), "}", sep = "", collapse = ", ") params <- ifelse(params == "\\code{parameter}", "None", params) data.frame(method = as.character(x$model)[1], Package = cranRef(as.character(x$Package)[1]), Parameters = params) } scrubCall <- function(x) { items <- c("x", "y", "data") for(i in items) if(nchar(as.character(x[i])) > 100) x[i] <- "scrubbed" x } class2ind <- function(x, drop2nd = FALSE) { if(!is.factor(x)) stop("'x' should be a factor") y <- model.matrix(~ x - 1) colnames(y) <- gsub("^x", "", colnames(y)) attributes(y)$assign <- NULL attributes(y)$contrasts <- NULL if(length(levels(x)) == 2 & drop2nd) { y <- y[,1] } y } requireNamespaceQuietStop <- function(package) { if (!requireNamespace(package, quietly = TRUE)) stop(paste('package',package,'is required')) } get_resample_perf <- function (x, ...) UseMethod("get_resample_perf") get_resample_perf.train <- function(x) { if(x$control$returnResamp == "none") stop("use returnResamp == 'none' in trainControl()") out <- merge(x$resample, x$bestTune) out[, c(x$perfNames, "Resample")] } get_resample_perf.rfe <- function(x) { if(x$control$returnResamp == "none") stop("use returnResamp == 'none' in trainControl()") out <- subset(x$resample, Variables == x$bestSubset) out[, c(x$perfNames, "Resample")] } get_resample_perf.sbf <- function(x) { if(x$control$returnResamp == "none") stop("use returnResamp == 'none' in trainControl()") x$resample } get_resample_perf.safs <- function(x) { out <- subset(x$external, Iter == x$optIter) out[, !(names(out) %in% "Iter")] } get_resample_perf.gafs <- function(x) { out <- subset(x$external, Iter == x$optIter) out[, !(names(out) %in% "Iter")] } var_seq <- function(p, classification = FALSE, len = 3) { if(len == 1) { tuneSeq <- if(classification) max(floor(p/3), 1) else floor(sqrt(p)) } else { if(p <= len) { tuneSeq <- floor(seq(2, to = p, length = p)) } else { if(p < 500 ) tuneSeq <- floor(seq(2, to = p, length = len)) else tuneSeq <- floor(2^seq(1, to = log(p, base = 2), length = len)) } } if(any(table(tuneSeq) > 1)) { tuneSeq <- unique(tuneSeq) cat( "note: only", length(tuneSeq), "unique complexity parameters in default grid.", "Truncating the grid to", length(tuneSeq), ".\n\n") } tuneSeq } caret/R/featurePlot.R0000644000175000017500000000421112453550064014644 0ustar rbalintrbalint"featurePlot" <- function(x, y, plot = if(is.factor(y)) "strip" else "scatter", labels = c("Feature", ""), ...) { if(!is.data.frame(x)) x <- as.data.frame(x) numFeat <- dim(x)[2] if(plot != "pairs") { stackX <- stack(x) stackX$.y <- rep(y, numFeat) } else { if(!is.factor(y)) { x <- data.frame(cbind(x, y)) } } if(is.factor(y)) { featPlot <- switch(tolower(plot), strip = stripplot(values ~ .y|ind, stackX, xlab = labels[1], ylab = labels[2], ...), box =, boxplot = bwplot(values ~ .y|ind, stackX, xlab = labels[1], ylab = labels[2], ...), density = densityplot(~values |ind, stackX, groups = stackX$.y, xlab = labels[1], ylab = labels[2], ...), pairs = splom(~x, groups = y, ...), ellipse = splom(~x, groups = y, panel = function(x, y, groups, subscripts, ...) { requireNamespaceQuietStop("ellipse") lineInfo <- trellis.par.get("superpose.line") pointInfo <- trellis.par.get("superpose.symbol") uniqueGroups <- sort(unique(groups)) for (i in seq(along=uniqueGroups)) { id <- which(groups[subscripts] == uniqueGroups[i]) panel.xyplot(x[id], y[id], pch = pointInfo$pch[i], col = pointInfo$col[i], cex = pointInfo$cex[i], ...) groupVar<-var(cbind(x[id],y[id])) groupMean<-cbind(mean(x[id]),mean(y[id])) groupEllipse<-ellipse::ellipse(groupVar, centre = groupMean, level = 0.95) panel.xyplot(groupEllipse[,1], groupEllipse[,2], type="l", col = lineInfo$col[i], lty = lineInfo$lty[i], ...) } }, ...) ) } else { featPlot <- switch(tolower(plot), scatter =, xyplot = xyplot(.y ~ values|ind, stackX, scales = list( x = list(relation = "free")), xlab = labels[1], ylab = labels[2], ...), pairs = splom(~x, ...)) } featPlot } caret/R/format.bagEarth.R0000644000175000017500000000067012516214271015360 0ustar rbalintrbalintformat.bagEarth <- function(x, file = "", cat = TRUE, ...) { requireNamespaceQuietStop("earth") eachEq <- lapply( x$fit, function(u, ...) format(u, ...), ...) allEq <- paste( "(", paste(eachEq, collapse = "+"), ") /", x$B, "\n") if(cat) cat(allEq, file = file) else return(allEq) } caret/R/posPredValue.R0000644000175000017500000000367112453550064014774 0ustar rbalintrbalintposPredValue <- function(data, ...){ UseMethod("posPredValue") } "posPredValue.default" <- function(data, reference, positive = levels(reference)[1], prevalence = NULL, ...) { if(!is.factor(reference) | !is.factor(data)) stop("inputs must be factors") if(length(unique(c(levels(reference), levels(data)))) != 2) stop("input data must have the same two levels") lvls <- levels(data) if(is.null(prevalence)) prevalence <- mean(reference == positive) sens <- sensitivity(data, reference, positive) spec <- specificity(data, reference, lvls[lvls != positive]) (sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence))) } "posPredValue.table" <- function(data, positive = rownames(data)[1], prevalence = NULL, ...) { ## "truth" in columns, predictions in rows if(!all.equal(nrow(data), ncol(data))) stop("the table must have nrow = ncol") if(!all.equal(rownames(data), colnames(data))) stop("the table must the same groups in the same order") if(nrow(data) > 2) { tmp <- data data <- matrix(NA, 2, 2) colnames(data) <- rownames(data) <- c("pos", "neg") posCol <- which(colnames(tmp) %in% positive) negCol <- which(!(colnames(tmp) %in% positive)) data[1, 1] <- sum(tmp[posCol, posCol]) data[1, 2] <- sum(tmp[posCol, negCol]) data[2, 1] <- sum(tmp[negCol, posCol]) data[2, 2] <- sum(tmp[negCol, negCol]) data <- as.table(data) positive <- "pos" rm(tmp) } negative <- colnames(data)[colnames(data) != positive] if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data) sens <- sensitivity(data, positive) spec <- specificity(data, negative) (sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence))) } "posPredValue.matrix" <- function(data, positive = rownames(data)[1], prevalence = NULL, ...) { data <- as.table(data) posPredValue.table(data, prevalence = prevalence) } caret/R/updates.R0000644000175000017500000000530212453550064014021 0ustar rbalintrbalintupdate.train <- function(object, param = NULL, ...) { if(is.null(param)) { if(all(names(object) != "modelInfo")) { funcs <- try(getModelInfo(object$method)[[1]], silent = TRUE) if(class(funcs)[1] == "list" && length(funcs) > 0) { funcs$updated <- TRUE object$modelInfo <- funcs warning(paste("The model was updated to work with the current version of caret.", "Please re-create the model object since future versions will", "require objects to be created from caret versions >= 6.", "Alternatively, do not update caret beyond version 5.17-7.")) } else stop(paste("This appears to be from an old version of caret", "and the model type is unknown to the new version")) } } else { ## check for original data if(is.null(object$trainingData)) stop("original training data is needed; use returnData = TRUE in trainControl()") if(is.list(param)) param <- as.data.frame(param) dotNames <- hasDots(param, object$modelInfo) if(dotNames) colnames(param) <- gsub("^\\.", "", colnames(param)) if(!is.data.frame(param)) stop("param should be a data frame or a named list") if(nrow(param) > 1) stop("only one set of parameters should be specified") paramNames <- as.character(object$modelInfo$parameter$parameter) if(length(paramNames) != ncol(param)) stop(paste("Parameters should be", paste(paramNames, sep = "", collapse = ", "))) if(any(sort(names(param)) != sort(paste(paramNames, sep = "")))) stop(paste("Parameters should be", paste(paramNames, sep = "", collapse = ", "))) ## get pre-processing options if(!is.null(object$preProcess)) { ppOpt <- list(options = object$preProcess$method) if(length(object$control$preProcOptions) > 0) ppOpt <- c(ppOpt,object$control$preProcOptions) } else ppOpt <- NULL ## refit model with new parameters args <- list(x = object$trainingData[, colnames(object$trainingData) != ".outcome"], y = object$trainingData$.outcome, method = object$modelInfo, tuneValue = param, obsLevels = levels(object$trainingData$.outcome), pp = ppOpt, last = TRUE, classProbs = object$control$classProbs) if(length(object$dots) > 0) args <- c(args, object$dots) finalFinalModel <- do.call("createModel", args) object$finalModel <- finalFinalModel$fit object$preProcess <- finalFinalModel$preProc object$bestTune <- param ## modify objects so print method reflects update object$update <- param } ## return object object } caret/R/modelLookup.R0000644000175000017500000000557012453550064014655 0ustar rbalintrbalintcheckInstall <- function(pkg){ good <- rep(TRUE, length(pkg)) for(i in seq(along = pkg)){ tested <- try(find.package(pkg[i]), silent = TRUE) if(class(tested)[1] == "try-error") good[i] <- FALSE } if(any(!good)){ pkList <- paste(pkg[!good], collapse = ", ") msg <- paste(sum(!good), ifelse(sum(!good) > 1, " packages are", " package is"), " needed for this model and", ifelse(sum(!good) > 1, " are", " is"), " not installed. (", pkList, "). Would you like to try to install", ifelse(sum(!good) > 1, " them", " it"), " now?", sep = "") cat(msg) if(interactive()) { bioc <- c("affy", "logicFS", "gpls", "vbmp") installChoice <- menu(c("yes", "no")) if(installChoice == 1){ hasBioc <- any(pkg[!good] %in% bioc) if(!hasBioc) { install.packages(pkg[!good]) } else { inst <- pkg[!good] instC <- inst[!(inst %in% bioc)] instB <- inst[inst %in% bioc] if(length(instC) > 0) install.packages(instC) biocLite <- NULL source("http://bioconductor.org/biocLite.R") biocLite(instB) } } else stop() } else stop() } } getModelInfo <- function(model = NULL, regex = TRUE, ...) { load(system.file("models", "models.RData", package = "caret")) if(!is.null(model)){ keepers <- if(regex) grepl(model, names(models), ...) else which(model == names(models))[1] models <- models[keepers] } if(length(models) == 0) stop("That model is not in caret's built-in library") models } modelLookup <- function(model = NULL){ load(system.file("models", "models.RData", package = "caret")) if(!is.null(model)){ if(!(model %in% names(models))) stop(paste("Model '", method, "' is not in the ", "set of existing models", sep = "")) models <- models[model == names(models)] } out <- lapply(models, function(x) { out <- x$parameters[, c("parameter", "label")] out$forReg <- "Regression" %in% x$type out$forClass <- "Classification" %in% x$type out$probModel <- !is.null(x$prob) out }) for(i in seq(along = out)) out[[i]]$model <- names(models)[i] out <- do.call("rbind", out) rownames(out) <- NULL out <- out[, c('model', 'parameter', 'label', 'forReg', 'forClass', 'probModel')] out[order(out$model),] } missing_packages <- function(mods = getModelInfo()) { libs <- unique(unlist(lapply(mods, function(x) x$library))) here <- rownames(installed.packages()) libs[!(libs %in% here)] } caret/R/spatialSign.R0000644000175000017500000000174312453550064014637 0ustar rbalintrbalint "spatialSign" <- function(x) UseMethod("spatialSign") "spatialSign.default" <- function(x) { if(is.character(x) | is.factor(x)) stop("spatial sign is not defined for character or factor data") denom <- sum(x^2) out <- if(sqrt(denom) > .Machine$double.eps) x / sqrt(denom) else x * 0 out } "spatialSign.matrix" <- function(x) { # check for character data if(is.character(x)) stop("spatial sign is not defined for character data") xNames <- dimnames(x) p <- ncol(x) tmp <- t(apply(x, 1, spatialSign.default)) if(p == 1 & nrow(tmp) == 1) tmp <- t(tmp) dimnames(tmp) <- xNames tmp } "spatialSign.data.frame" <- function(x) { if(any(apply(x, 2, function(data) is.character(data) | is.factor(data)))) stop("spatial sign is not defined for character or factor data") xNames <- dimnames(x) x <- as.matrix(x) if(!is.numeric(x)) stop("a character matrix was the result of as.matrix") tmp <- spatialSign(x) dimnames(tmp) <- xNames tmp } caret/R/dummyVar.R0000644000175000017500000001072412453550064014164 0ustar rbalintrbalint contr.ltfr <- function (n, contrasts = TRUE, sparse = FALSE) { if (is.numeric(n) && length(n) == 1L) { if (n > 1L) levels <- as.character(seq_len(n)) else stop("not enough degrees of freedom to define contrasts") } else { levels <- as.character(n) n <- length(n) } contr <- .RDiag(levels, sparse = sparse) if (contrasts) { if (n < 2L) stop(gettextf("contrasts not defined for %d degrees of freedom", n - 1L), domain = NA) } contr } contr.dummy <- function(n, ...) { if (is.numeric(n) && length(n) == 1L) { if (n > 1L) levels <- as.character(seq_len(n)) else stop("not enough degrees of freedom to define contrasts") } else { levels <- as.character(n) n <- length(n) } out <- diag(n) rownames(out) <- levels colnames(out) <- levels out } "dummyVars" <- function(formula, ...){ UseMethod("dummyVars") } dummyVars.default <- function (formula, data, sep = ".", levelsOnly = FALSE, fullRank = FALSE, ...) { formula <- as.formula(formula) if(!is.data.frame(data)) data <- as.data.frame(data) vars <- all.vars(formula) if(any(vars == ".")) { vars <- vars[vars != "."] vars <- unique(c(vars, colnames(data))) } isFac <- unlist(lapply(data[,vars,drop = FALSE], is.factor)) if(sum(isFac) > 0) { facVars <- vars[isFac] lvls <- lapply(data[,facVars,drop = FALSE], levels) if(levelsOnly) { tabs <- table(unlist(lvls)) if(any(tabs > 1)) { stop(paste("You requested `levelsOnly = TRUE` but", "the following levels are not unique", "across predictors:", paste(names(tabs)[tabs > 1], collapse = ", "))) } } } else { facVars <- NULL lvls <- NULL } trms <- attr(model.frame(formula, data), "terms") out <- list(call = match.call(), form = formula, vars = vars, facVars = facVars, lvls = lvls, sep = sep, terms = trms, levelsOnly = levelsOnly, fullRank = fullRank) class(out) <- "dummyVars" out } print.dummyVars <- function(x, ...) { cat("Dummy Variable Object\n\n") cat("Formula: ") print(x$form) cat(length(x$vars), " variables, ", length(x$facVars), " factors\n", sep = "") if(!is.null(x$sep) & !x$levelsOnly) cat("Variables and levels will be separated by '", x$sep, "'\n", sep = "") if(x$levelsOnly) cat("Factor variable names will be removed\n") if(x$fullRank) cat("A full rank encoding is used") else cat("A less than full rank encoding is used") cat("\n") invisible(x) } predict.dummyVars <- function(object, newdata, na.action = na.pass, ...) { if(is.null(newdata)) stop("newdata must be supplied") if(!is.data.frame(newdata)) newdata <- as.data.frame(newdata) if(!all(object$vars %in% names(newdata))) stop( paste("Variable(s)", paste("'", object$vars[object$vars %in% names(newdata)], "'", sep = "", collapse = ", "), "are not in newdata")) Terms <- object$terms Terms <- delete.response(Terms) if(!object$fullRank) { oldContr <- options("contrasts")$contrasts newContr <- oldContr newContr["unordered"] <- "contr.ltfr" options(contrasts = newContr) } m <- model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels) x <- model.matrix(Terms, m) if(!object$fullRank) options(contrasts = oldContr) if(object$levelsOnly) { for(i in object$facVars) { colnames(x) <- gsub(paste("^", i, sep = ""), "", colnames(x)) colnames(x) <- gsub(paste(":", i, sep = ""), ":", colnames(x), fixed = TRUE) } } if(!is.null(object$sep) & !object$levelsOnly) { for(i in object$facVars) { colnames(x) <- gsub(paste("^", i, sep = ""), paste(i, object$sep, sep = ""), colnames(x)) colnames(x) <- gsub(paste(":", i, sep = ""), paste(":", i, object$sep, sep = ""), colnames(x), fixed = TRUE) } } x[, colnames(x) != "(Intercept)", drop = FALSE] } caret/R/lattice.train.R0000644000175000017500000001523312453550064015121 0ustar rbalintrbalintdensityplot.train <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") resamp <- x$resample tNames <- gsub("^\\.", "", names(x$bestTune)) # adapt formula to work with muliple metrics mName <- names(resamp)[names(resamp) %in% metric][1] # Look for constant tuning parameters and remove them numVals <- unlist( lapply(resamp, function(u) length(unique(u)))) if(any(numVals == 1)) { # make sure that these are tuning parameters resamp <- resamp[, numVals > 1, drop = FALSE] tNames <- tNames[tNames %in% names(numVals)[numVals > 1]] } # Create the formula based on the data formText <- paste("~", mName) if(any(tNames %in% colnames(resamp))) { formText <- paste(formText, "|", paste( tNames, collapse = "*")) } form <- as.formula(formText) densityplot(form, data = resamp, ...) } histogram.train <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") resamp <- x$resample tNames <- gsub("^\\.", "", names(x$bestTune)) # adapt formula to work with muliple metrics mName <- names(resamp)[names(resamp) %in% metric][1] # Look for constant tuning parameters and remove them numVals <- unlist( lapply(resamp, function(u) length(unique(u)))) if(any(numVals == 1)) { # make sure that these are tuning parameters resamp <- resamp[, numVals > 1, drop = FALSE] tNames <- tNames[tNames %in% names(numVals)[numVals > 1]] } # Create the formula based on the data formText <- paste("~", mName) if(any(tNames %in% colnames(resamp))) { formText <- paste(formText, "|", paste( tNames, collapse = "*")) } form <- as.formula(formText) histogram(form, data = resamp, ...) } stripplot.train <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") resamp <- x$resample tNames <- gsub("^\\.", "", names(x$bestTune)) # adapt formula to work with muliple metrics mName <- names(resamp)[names(resamp) %in% metric][1] # Look for constant tuning parameters and remove them numVals <- unlist( lapply(resamp, function(u) length(unique(u)))) if(any(numVals == 1)) { # make sure that these are tuning parameters resamp <- resamp[, numVals > 1, drop = FALSE] tNames <- tNames[tNames %in% names(numVals)[numVals > 1]] } # determine which tuning parameter has the most values tNames1 <- names(which.max(numVals[names(numVals) %in% tNames])) tNames2 <- tNames[!(tNames %in% tNames1)] # The variable in tNames1 will be converted to a factor, so # we will make sure that numeric data gets changed correctly resamp[,tNames1] <- factor( as.character(resamp[,tNames1]), levels = paste( sort(unique(resamp[,tNames1])))) # Create the formula based on the data if(any(tNames %in% colnames(resamp))) { theDots <- list(...) if(any(names(theDots) == "horizontal")) { formText <- if(theDots$horizontal) paste(tNames1, "~", mName) else paste(mName, "~", tNames1) } else formText <- paste(tNames1, "~", mName) if(length(tNames2) > 0) { formText <- paste(formText, "|", paste( tNames2, collapse = "*")) } } else formText <- paste("~", mName) form <- as.formula(formText) stripplot(form, data = resamp, ...) } xyplot.train <- function(x, data = NULL, metric = x$metric, ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") resamp <- x$resample tNames <- gsub("^\\.", "", names(x$bestTune)) # adapt formula to work with muliple metrics mName <- names(resamp)[names(resamp) %in% metric][1] # Look for constant tuning parameters and remove them numVals <- unlist( lapply(resamp, function(u) length(unique(u)))) if(any(numVals == 1)) { # make sure that these are tuning parameters resamp <- resamp[, numVals > 1, drop = FALSE] tNames <- tNames[tNames %in% names(numVals)[numVals > 1]] } # determine which tuning parameter has the most values tNames1 <- names(which.max(numVals[names(numVals) %in% tNames])) tNames2 <- tNames[!(tNames %in% tNames1)] # Create the formula based on the data if(any(tNames %in% colnames(resamp))) { formText <- paste(mName, "~", tNames1) if(length(tNames2) > 0) { formText <- paste(formText, "|", paste( tNames2, collapse = "*")) } } else stop("there must be at least one tuning parameter for a scatter plot") form <- as.formula(formText) xyplot(form, data = resamp, ...) } caret/R/train.default.R0000644000175000017500000006455712516214271015132 0ustar rbalintrbalint"train" <- function(x, ...){ UseMethod("train") } train.default <- function(x, y, method = "rf", preProcess = NULL, ..., weights = NULL, metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), trControl = trainControl(), tuneGrid = NULL, tuneLength = 3) { startTime <- proc.time() if(is.list(method)) { minNames <- c("library", "type", "parameters", "grid", "fit", "predict", "prob") nameCheck <- minNames %in% names(method) if(!all(nameCheck)) stop(paste("some required components are missing:", paste(minNames[!nameCheck], collapse = ", "))) models <- method method <- "custom" } else { models <- getModelInfo(method, regex = FALSE)[[1]] if (length(models) == 0) stop(paste("Model", method, "is not in caret's built-in library")) } checkInstall(models$library) for(i in seq(along = models$library)) do.call("require", list(package = models$library[i])) paramNames <- as.character(models$parameters$parameter) funcCall <- match.call(expand.dots = TRUE) modelType <- if(is.factor(y)) "Classification" else "Regression" if(!(modelType %in% models$type)) stop(paste("wrong model type for", tolower(modelType))) if(grepl("^svm", method) & grepl("String$", method)) { if(is.vector(x) && is.character(x)) { stop("'x' should be a character matrix with a single column for string kernel methods") } if(is.matrix(x) && is.numeric(x)) { stop("'x' should be a character matrix with a single column for string kernel methods") } if(is.data.frame(x)) { stop("'x' should be a character matrix with a single column for string kernel methods") } } if(any(class(x) == "data.table")) x <- as.data.frame(x) stopifnot(length(y) > 1) stopifnot(nrow(x) > 1) stopifnot(nrow(x) == length(y)) ## TODO add check method and execute here ## Some models that use RWeka start multiple threads and this conflicts with multicore: if(any(search() == "package:doMC") && getDoParRegistered() && "RWeka" %in% models$library) warning("Models using Weka will not work with parallel processing with multicore/doMC") flush.console() if(!is.null(preProcess) && !(all(preProcess %in% ppMethods))) stop(paste('pre-processing methods are limited to:', paste(ppMethods, collapse = ", "))) if(modelType == "Classification") { ## We should get and save the class labels to ensure that predictions are coerced ## to factors that have the same levels as the original data. This is especially ## important with multiclass systems where one or more classes have low sample sizes ## relative to the others classLevels <- levels(y) if(trControl$classProbs && any(classLevels != make.names(classLevels))) { warning(paste("At least one of the class levels are not valid R variables names;", "This may cause errors if class probabilities are generated because", "the variables names will be converted to:", paste(make.names(classLevels), collapse = ", "))) } if(metric %in% c("RMSE", "Rsquared")) stop(paste("Metric", metric, "not applicable for classification models")) if(trControl$classProbs) { if(!is.function(models$prob)) { warning("Class probabilities were requested for a model that does not implement them") trControl$classProbs <- FALSE } } } else { if(metric %in% c("Accuracy", "Kappa")) stop(paste("Metric", metric, "not applicable for regression models")) classLevels <- NA if(trControl$classProbs) { warning("cannnot compute class probabilities for regression") trControl$classProbs <- FALSE } } if(trControl$method == "oob" & !(method %in% oob_mods)) stop(paste("for oob error rates, model bust be one of:", paste(oob_mods, sep = "", collapse = ", "))) ## If they don't exist, make the data partitions for the resampling iterations. if(is.null(trControl$index)) { trControl$index <- switch(tolower(trControl$method), oob = NULL, none = list(seq(along = y)), alt_cv =, cv = createFolds(y, trControl$number, returnTrain = TRUE), repeatedcv =, adaptive_cv = createMultiFolds(y, trControl$number, trControl$repeats), loocv = createFolds(y, length(y), returnTrain = TRUE), boot =, boot632 =, adaptive_boot = createResample(y, trControl$number), test = createDataPartition(y, 1, trControl$p), adaptive_lgocv =, lgocv = createDataPartition(y, trControl$number, trControl$p), timeslice = createTimeSlices(seq(along = y), initialWindow = trControl$initialWindow, horizon = trControl$horizon, fixedWindow = trControl$fixedWindow)$train, subsemble = subsemble_index(y, V = trControl$number, J = trControl$repeats)) } if(trControl$method == "subsemble") { if(!trControl$savePredictions) trControl$savePredictions <- TRUE trControl$indexOut <- trControl$index$holdout trControl$index <- trControl$index$model } ## Create hold--out indicies if(is.null(trControl$indexOut) & trControl$method != "oob"){ if(tolower(trControl$method) != "timeslice") { trControl$indexOut <- lapply(trControl$index, function(training, allSamples) allSamples[-unique(training)], allSamples = seq(along = y)) names(trControl$indexOut) <- prettySeq(trControl$indexOut) } else { trControl$indexOut <- createTimeSlices(seq(along = y), initialWindow = trControl$initialWindow, horizon = trControl$horizon, fixedWindow = trControl$fixedWindow)$test } } if(trControl$method != "oob" & is.null(trControl$index)) names(trControl$index) <- prettySeq(trControl$index) if(trControl$method != "oob" & is.null(names(trControl$index))) names(trControl$index) <- prettySeq(trControl$index) if(trControl$method != "oob" & is.null(names(trControl$indexOut))) names(trControl$indexOut) <- prettySeq(trControl$indexOut) # if(!is.data.frame(x)) x <- as.data.frame(x) ## Gather all the pre-processing info. We will need it to pass into the grid creation ## code so that there is a concordance between the data used for modeling and grid creation if(!is.null(preProcess)) { ppOpt <- list(options = preProcess) if(length(trControl$preProcOptions) > 0) ppOpt <- c(ppOpt,trControl$preProcOptions) } else ppOpt <- NULL ## If no default training grid is specified, get one. We have to pass in the formula ## and data for some models (rpart, pam, etc - see manual for more details) if(is.null(tuneGrid)) { if(!is.null(ppOpt) && length(models$parameters$parameter) > 1 && as.character(models$parameters$parameter) != "parameter") { pp <- list(method = ppOpt$options) if("ica" %in% pp$method) pp$n.comp <- ppOpt$ICAcomp if("pca" %in% pp$method) pp$thresh <- ppOpt$thresh if("knnImpute" %in% pp$method) pp$k <- ppOpt$k pp$x <- x ppObj <- do.call("preProcess", pp) tuneGrid <- models$grid(predict(ppObj, x), y, tuneLength) rm(ppObj, pp) } else tuneGrid <- models$grid(x, y, tuneLength) } dotNames <- hasDots(tuneGrid, models) if(dotNames) colnames(tuneGrid) <- gsub("^\\.", "", colnames(tuneGrid)) ## Check tuning parameter names tuneNames <- as.character(models$parameters$parameter) goodNames <- all.equal(sort(tuneNames), sort(names(tuneGrid))) if(!is.logical(goodNames) || !goodNames) { stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", sep = ""))) } if(trControl$method == "none" && nrow(tuneGrid) != 1) stop("Only one model should be specified in tuneGrid with no resampling") ## In case prediction bounds are used, compute the limits. For now, ## store these in the control object since that gets passed everywhere trControl$yLimits <- if(is.numeric(y)) extendrange(y) else NULL if(trControl$method != "none") { ##------------------------------------------------------------------------------------------------------------------------------------------------------# ## For each tuning parameter combination, we will loop over them, fit models and generate predictions. ## We only save the predictions at this point, not the models (and in the case of method = "oob" we ## only save the prediction summaries at this stage. ## trainInfo will hold the information about how we should loop to train the model and what types ## of parameters are used. ## There are two types of methods to build the models: "basic" means that each tuning parameter ## combination requires it's own model fit and "seq" where a single model fit can be used to ## get predictions for multiple tuning parameters. ## The tuneScheme() function is in miscr.R and it helps define the following: ## - A data frame called "loop" with columns for parameters and a row for each model to be fit. ## For "basic" models, this is the same as the tuning grid. For "seq" models, it is only ## the subset of parameters that need to be fit ## - A list called "submodels". If "basic", it is NULL. For "seq" models, it is a list. Each list ## item is a data frame of the parameters that need to be varied for the corresponding row of ## the loop object. ## ## For example, for a gbm model, our tuning grid might be: ## .interaction.depth .n.trees .shrinkage ## 1 50 0.1 ## 1 100 0.1 ## 2 50 0.1 ## 2 100 0.1 ## 2 150 0.1 ## ## For this example: ## ## loop: ## .interaction.depth .shrinkage .n.trees ## 1 0.1 100 ## 2 0.1 150 ## ## submodels: ## [[1]] ## .n.trees ## 50 ## ## [[2]] ## .n.trees ## 50 ## 100 ## ## A simplified version of predictionFunction() would have the following gbm section: ## ## # First get the predictions with the value of n.trees as given in the current ## # row of loop ## out <- predict(modelFit, ## newdata, ## type = "response", ## n.trees = modelFit$tuneValue$.n.trees) ## ## # param is the current value of submodels. In normal prediction mode (i.e ## # when using predict.train), param = NULL. When called within train() ## # with this model, it will have the other values for n.trees. ## # In this case, the output of the function is a list of predictions ## # These values are deconvoluted in workerTasks() in misc.R ## if(!is.null(param)) ## { ## tmp <- vector(mode = "list", length = nrow(param) + 1) ## tmp[[1]] <- out ## ## for(j in seq(along = param$.n.trees)) ## { ## tmp[[j]] <- predict(modelFit, ## newdata, ## type = "response", ## n.trees = param$.n.trees[j]) ## } ## out <- tmp ## # paramCols <- paste(".", as.character(models$parameters$parameter), sep = "") if(is.function(models$loop) && nrow(tuneGrid) > 1){ trainInfo <- models$loop(tuneGrid) if(!all(c("loop", "submodels") %in% names(trainInfo))) stop("The 'loop' function should produce a list with elements 'loop' and 'submodels'") } else trainInfo <- list(loop = tuneGrid) ## Set or check the seeds when needed if(is.null(trControl$seeds)) { seeds <- vector(mode = "list", length = length(trControl$index)) seeds <- lapply(seeds, function(x) sample.int(n = 1000000, size = nrow(trainInfo$loop))) seeds[[length(trControl$index) + 1]] <- sample.int(n = 1000000, size = 1) trControl$seeds <- seeds } else { if(!(length(trControl$seeds) == 1 && is.na(trControl$seeds))) { ## check versus number of tasks numSeeds <- unlist(lapply(trControl$seeds, length)) badSeed <- (length(trControl$seeds) < length(trControl$index) + 1) || (any(numSeeds[-length(numSeeds)] < nrow(trainInfo$loop))) if(badSeed) stop(paste("Bad seeds: the seed object should be a list of length", length(trControl$index) + 1, "with", length(trControl$index), "integer vectors of size", nrow(trainInfo$loop), "and the last list element having a", "single integer")) } } if(trControl$method == "oob") { ## delay this test until later perfNames <- metric } else { ## run some data thru the summary function and see what we get testSummary <- evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels, metric = metric, method = method) perfNames <- names(testSummary) } if(!(metric %in% perfNames)){ oldMetric <- metric metric <- perfNames[1] warning(paste("The metric \"", oldMetric, "\" was not in ", "the result set. ", metric, " will be used instead.", sep = "")) } if(trControl$method == "oob"){ tmp <- oobTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, method = models, ppOpts = preProcess, ctrl = trControl, lev = classLevels, ...) performance <- tmp perfNames <- colnames(performance) perfNames <- perfNames[!(perfNames %in% as.character(models$parameters$parameter))] if(!(metric %in% perfNames)){ oldMetric <- metric metric <- perfNames[1] warning(paste("The metric \"", oldMetric, "\" was not in ", "the result set. ", metric, " will be used instead.", sep = "")) } } else { if(trControl$method == "LOOCV"){ tmp <- looTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, method = models, ppOpts = preProcess, ctrl = trControl, lev = classLevels, ...) performance <- tmp$performance } else { if(!grepl("adapt", trControl$method)){ tmp <- nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, method = models, ppOpts = preProcess, ctrl = trControl, lev = classLevels, ...) performance <- tmp$performance resampleResults <- tmp$resample } else { tmp <- adaptiveWorkflow(x = x, y = y, wts = weights, info = trainInfo, method = models, ppOpts = preProcess, ctrl = trControl, lev = classLevels, metric = metric, maximize = maximize, ...) performance <- tmp$performance resampleResults <- tmp$resample } } } ## TODO we used to give resampled results for LOO if(!(trControl$method %in% c("LOOCV", "oob"))) { if(modelType == "Classification" && length(grep("^\\cell", colnames(resampleResults))) > 0) { resampledCM <- resampleResults[, !(names(resampleResults) %in% perfNames)] resampleResults <- resampleResults[, -grep("^\\cell", colnames(resampleResults))] #colnames(resampledCM) <- gsub("^\\.", "", colnames(resampledCM)) } else resampledCM <- NULL } else resampledCM <- NULL if(trControl$verboseIter) { cat("Aggregating results\n") flush.console() } perfCols <- names(performance) perfCols <- perfCols[!(perfCols %in% paramNames)] if(all(is.na(performance[, metric]))) { cat(paste("Something is wrong; all the", metric, "metric values are missing:\n")) print(summary(performance[, perfCols[!grepl("SD$", perfCols)], drop = FALSE])) stop("Stopping") } ## Sort the tuning parameters from least complex to most complex if(!is.null(models$sort)) performance <- models$sort(performance) if(any(is.na(performance[, metric]))) warning("missing values found in aggregated results") if(trControl$verboseIter && nrow(performance) > 1) { cat("Selecting tuning parameters\n") flush.console() } ## select the optimal set selectClass <- class(trControl$selectionFunction)[1] ## Select the "optimal" tuning parameter. if(grepl("adapt", trControl$method)) { perf_check <- subset(performance, .B == max(performance$.B)) } else perf_check <- performance ## Make adaptive only look at parameters with B = max(B) if(selectClass == "function") { bestIter <- trControl$selectionFunction(x = perf_check, metric = metric, maximize = maximize) } else { if(trControl$selectionFunction == "oneSE") { bestIter <- oneSE(perf_check, metric, length(trControl$index), maximize) } else { bestIter <- do.call(trControl$selectionFunction, list(x = perf_check, metric = metric, maximize = maximize)) } } if(is.na(bestIter) || length(bestIter) != 1) stop("final tuning parameters could not be determined") if(grepl("adapt", trControl$method)) { best_perf <- perf_check[bestIter,as.character(models$parameters$parameter),drop = FALSE] performance$order <- 1:nrow(performance) bestIter <- merge(performance, best_perf)$order performance$order <- NULL } ## Based on the optimality criterion, select the tuning parameter(s) bestTune <- performance[bestIter, paramNames, drop = FALSE] } else { bestTune <- tuneGrid performance <- evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels, metric = metric, method = method) perfNames <- names(performance) performance <- as.data.frame(t(performance)) performance <- cbind(performance, tuneGrid) performance <- performance[-1,,drop = FALSE] tmp <- resampledCM <- NULL } ## Save some or all of the resampling summary metrics if(!(trControl$method %in% c("LOOCV", "oob", "none"))) { byResample <- switch(trControl$returnResamp, none = NULL, all = { out <- resampleResults colnames(out) <- gsub("^\\.", "", colnames(out)) out }, final = { out <- merge(bestTune, resampleResults) out <- out[,!(names(out) %in% names(tuneGrid)), drop = FALSE] out }) } else { byResample <- NULL } # names(bestTune) <- paste(".", names(bestTune), sep = "") ## Reorder rows of performance orderList <- list() for(i in seq(along = paramNames)) orderList[[i]] <- performance[,paramNames[i]] names(orderList) <- paramNames performance <- performance[do.call("order", orderList),] if(trControl$verboseIter) { bestText <- paste(paste(names(bestTune), "=", format(bestTune, digits = 3)), collapse = ", ") if(nrow(performance) == 1) bestText <- "final model" cat("Fitting", bestText, "on full training set\n") flush.console() } ## Make the final model based on the tuning results if(!(length(trControl$seeds) == 1 && is.na(trControl$seeds))) set.seed(trControl$seeds[[length(trControl$seeds)]][1]) finalTime <- system.time( finalModel <- createModel(x = x, y = y, wts = weights, method = models, tuneValue = bestTune, obsLevels = classLevels, pp = ppOpt, last = TRUE, classProbs = trControl$classProbs, ...)) if(trControl$trim && !is.null(models$trim)) { if(trControl$verboseIter) old_size <- object.size(finalModel$fit) finalModel$fit <- models$trim(finalModel$fit) if(trControl$verboseIter) { new_size <- object.size(finalModel$fit) reduction <- format(old_size - new_size, units = "Mb") if(reduction == "0 Mb") reduction <- "< 0 Mb" p_reduction <- (unclass(old_size) - unclass(new_size))/unclass(old_size)*100 p_reduction <- if(p_reduction < 1) "< 1%" else paste0(round(p_reduction, 0), "%") cat("Final model footprint reduced by", reduction, "or", p_reduction, "\n") } } ## get pp info pp <- finalModel$preProc finalModel <- finalModel$fit ## Remove this and check for other places it is reference ## replaced by tuneValue if(method == "pls") finalModel$bestIter <- bestTune ## To use predict.train and automatically use the optimal lambda, ## we need to save it if(method == "glmnet") finalModel$lambdaOpt <- bestTune$lambda if(trControl$returnData) { outData <- if(!is.data.frame(x)) try(as.data.frame(x), silent = TRUE) else x if(class(outData)[1] == "try-error") { warning("The training data could not be converted to a data frame for saving") outData <- NULL } else outData$.outcome <- y } else outData <- NULL ## In the case of pam, the data will need to be saved differently if(trControl$returnData & method == "pam") { finalModel$xData <- x finalModel$yData <- y } endTime <- proc.time() times <- list(everything = endTime - startTime, final = finalTime) out <- structure(list(method = method, modelInfo = models, modelType = modelType, results = performance, pred = tmp$predictions, bestTune = bestTune, call = funcCall, dots = list(...), metric = metric, control = trControl, finalModel = finalModel, preProcess = pp, trainingData = outData, resample = byResample, resampledCM = resampledCM, perfNames = perfNames, maximize = maximize, yLimits = trControl$yLimits, times = times), class = "train") trControl$yLimits <- NULL if(trControl$timingSamps > 0) { pData <- lapply(x, function(x, n) sample(x, n, replace = TRUE), n = trControl$timingSamps) pData <- as.data.frame(pData) out$times$prediction <- system.time(predict(out, pData)) } else out$times$prediction <- rep(NA, 3) out } train.formula <- function (form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) m$... <- m$contrasts <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) if(nrow(m) < 1) stop("Every row has at least one missing value were found") Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts, na.action = na.action) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] y <- model.response(m) w <- as.vector(model.weights(m)) res <- train(x, y, weights = w, ...) res$terms <- Terms res$coefnames <- colnames(x) res$call <- match.call() res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) if(!is.null(res$trainingData)) { res$trainingData <- data isY <- names(res$trainingData) %in% as.character(form[[2]]) if(any(isY)) colnames(res$trainingData)[isY] <- ".outcome" } class(res) <- c("train", "train.formula") res } summary.train <- function(object, ...) summary(object$finalModel, ...) residuals.train <- function(object, ...) { if(object$modelType != "Regression") stop("train() only produces residuals on numeric outcomes") resid <- residuals(object$finalModel, ...) if(is.null(resid)) { if(!is.null(object$trainingData)) { resid <- object$trainingData$.outcome - predict(object, object$trainingData[, names(object$trainingData) != ".outcome",drop = FALSE]) } else stop("The training data must be saved to produce residuals") } resid } fitted.train <- function(object, ...) { prd <- fitted(object$finalModel) if(is.null(prd)) { if(!is.null(object$trainingData)) { prd <- predict(object, object$trainingData[, names(object$trainingData) != ".outcome",drop = FALSE]) } else stop("The training data must be saved to produce fitted values") } prd } caret/R/varImp.train.R0000644000175000017500000000667712453550064014746 0ustar rbalintrbalint"varImp.train" <- function(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...) { if(is.null(object$finalModel)) stop("must have retained the final model") modelName <- object$method if(!(class(object$finalModel)[1] %in% gsub("varImp.", "", as.character(methods("varImp"), fixed = TRUE)))) useModel <- FALSE availMethods <- methods("varImp") availMethods <- rownames(attributes(availMethods)$info) availMethods <- gsub("varImp.", "", availMethods, fixed = TRUE) hasMethod <- any(class(object$finalModel) %in% availMethods) if(!hasMethod & useModel) useModel <- FALSE if(useModel) { imp <- switch( class(object$finalModel)[1], pamrtrained = { if(is.null(object$finalModel$xData)) stop("the train$finalModel object needs an xData object") varImp(object$finalModel, threshold = ifelse(is.null(object$bestTune), 0, object$bestTune[,1]), data = object$finalModel$xData) }, mars =, earth = { if(is.null(object$trainingData)) stop("the train object needs an trainingData object") tmp <- object$trainingData[,names(object$trainingData) != ".outcome"] varImp(object$finalModel, data = tmp, ...) }, varImp(object$finalModel, ...)) } else { isX <- which(!(colnames(object$trainingData) %in% ".outcome")) imp <- filterVarImp(object$trainingData[, isX,drop = FALSE], object$trainingData[, -isX], nonpara = nonpara, ...) modelName <- ifelse(is.factor(object$trainingData[, -isX]), "ROC curve", ifelse(nonpara, "loess r-squared", "Linear model")) } if(scale) { if(class(object$finalModel)[1] == "pamrtrained") imp <- abs(imp) imp <- imp - min(imp, na.rm = TRUE) imp <- imp/max(imp, na.rm = TRUE)*100 } out <- list(importance = imp, model = modelName, calledFrom = "varImp") structure(out, class = "varImp.train") } "varImp.train" <- function(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...) { code <- object$modelInfo if(is.null(code$varImp)) useModel <- FALSE if(useModel) { checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) imp <- code$varImp(object$finalModel, ...) modelName <- object$method } else { isX <- which(!(colnames(object$trainingData) %in% ".outcome")) imp <- filterVarImp(object$trainingData[, isX,drop = FALSE], object$trainingData[, -isX], nonpara = nonpara, ...) modelName <- ifelse(is.factor(object$trainingData[, -isX]), "ROC curve", ifelse(nonpara, "loess r-squared", "Linear model")) } if(scale) { if(class(object$finalModel)[1] == "pamrtrained") imp <- abs(imp) imp <- imp - min(imp, na.rm = TRUE) imp <- imp/max(imp, na.rm = TRUE)*100 } out <- list(importance = imp, model = modelName, calledFrom = "varImp") structure(out, class = "varImp.train") } caret/R/plot.varImp.train.R0000644000175000017500000000303112453550064015700 0ustar rbalintrbalint"plot.varImp.train" <- function(x, top = dim(x$importance)[1], ...) { plotObj <- sortImp(x, top) if(ncol(plotObj) == 2) { plotObj <- plotObj[,1,drop = FALSE] names(plotObj) <- "Importance" } featureNames <- rownames(plotObj) outcomeNames <- colnames(plotObj) if(ncol(plotObj) > 1) { stackedData <- stack(plotObj) stackedData$Feature <- factor(rep(featureNames, length(outcomeNames)), levels = rev(featureNames)) names(stackedData) <- c("Importance", "Class", "Feature") } else { stackedData <- plotObj stackedData$Feature <- factor(rep(featureNames, length(outcomeNames)), levels = rev(featureNames)) names(stackedData) <- c("Importance", "Feature") } formulaText <- ifelse(ncol(plotObj)> 1, "Feature ~ Importance|Class", " Feature ~ Importance") if(x$model == "pam") { impSign <- factor(ifelse(stackedData$Importance > 0, "Positive", "Negative"), levels = c("Positive", "Negative")) stackedData$Importance <- abs(stackedData$Importance) impPlot <- dotplot(as.formula(formulaText), stackedData, groups = impSign, panel = panel.needle, ...) } else { impPlot <- dotplot(as.formula(formulaText), stackedData, panel = panel.needle, ...) } impPlot } caret/R/aucRoc.R0000644000175000017500000000073012453550064013570 0ustar rbalintrbalintaucRoc <- function(object) { warning("This function is deprecated a of 1/3/12. The computations now utilize the pROC package. This function will be removed in a few releases.") sens <- object[, "sensitivity"] omspec <- 1 - object[, "specificity"] newOrder <- order(omspec) sens <- sens[newOrder] omspec <- omspec[newOrder] rocArea <- sum(.5 *diff(omspec) * (sens[-1] + sens[-length(sens)])) rocArea <- max(rocArea, 1 - rocArea) rocArea } caret/R/getTrainPerf.R0000644000175000017500000000046212453550064014750 0ustar rbalintrbalintgetTrainPerf <- function(x) { bestPerf <- x$bestTune colnames(bestPerf) <- gsub("^\\.", "", colnames(bestPerf)) out <- merge(x$results, bestPerf) out <- out[, colnames(out) %in% x$perfNames, drop = FALSE] colnames(out) <- paste("Train", colnames(out), sep = "") out$method <- x$method out } caret/R/extractProb.R0000644000175000017500000001226512516214271014654 0ustar rbalintrbalint## TODO use foreach to parallelize extractProb <- function(models, testX = NULL, testY = NULL, unkX = NULL, unkOnly = !is.null(unkX) & is.null(testX), verbose = FALSE) { objectNames <- names(models) if(is.null(objectNames)) objectNames <- paste("Object", 1:length(models), sep = "") if(any(unlist(lapply(models, function(x) is.null(x$modelInfo$prob))))) stop("only classification models that produce probabilities are allowed") obsLevels <- levels(models[[1]]) if(!unkOnly) { trainX <- models[[1]]$trainingData[,!(colnames(models[[1]]$trainingData) %in% ".outcome")] trainY <- models[[1]]$trainingData$.outcome } if(verbose) { cat("Number of training samples:", length(trainY), "\n") cat("Number of test samples: ", length(testY), "\n\n") } predProb <- predClass <- obs <- modelName <- dataType <- objName <- NULL if(!is.null(testX)) { if(!is.data.frame(testX)) testX <- as.data.frame(testX) hasNa <- apply(testX, 1, function(data) any(is.na(data))) if(verbose) cat("There were ", sum(hasNa), "rows with missing values\n\n"); flush.console() } for(i in seq(along = models)) { if(verbose) cat("starting ", models[[i]]$method, "\n"); flush.console() if(!unkOnly) { tempTrainProb <- probFunction(models[[i]]$modelInfo, models[[i]]$finalModel, trainX, models[[i]]$preProcess) tempTrainPred <- apply(tempTrainProb, 1, which.max) tempTrainPred <- colnames(tempTrainProb)[tempTrainPred] tempTrainPred <- factor(tempTrainPred, levels = models[[i]]$modelInfo$levels(models[[i]]$finalModel)) if(verbose) cat(models[[i]]$method, ":", length(tempTrainPred), "training predictions were added\n"); flush.console() predProb <- if(is.null(predProb)) tempTrainProb else rbind(predProb, tempTrainProb) predClass <- c(predClass, as.character(tempTrainPred)) obs <- c(obs, as.character(trainY)) modelName <- c(modelName, rep(models[[i]]$method, length(tempTrainPred))) objName <- c(objName, rep(objectNames[[i]], length(tempTrainPred))) dataType <- c(dataType, rep("Training", length(tempTrainPred))) # Test Data if(!is.null(testX) & !is.null(testY)) { if(!is.data.frame(testX)) testX <- as.data.frame(testX) tempX <- testX tempY <- testY tempX$.outcome <- NULL tempTestProb <- probFunction(models[[i]]$modelInfo, models[[i]]$finalModel, tempX, models[[i]]$preProcess) tempTestPred <- apply(tempTestProb, 1, which.max) tempTestPred <- colnames(tempTestProb)[tempTestPred] tempTestPred <- factor(tempTestPred, levels = models[[i]]$modelInfo$levels(models[[i]]$finalModel)) if(verbose) cat(models[[i]]$method, ":", length(tempTestPred), "test predictions were added\n") predProb <- if(is.null(predProb)) tempTestProb else rbind(predProb, tempTestProb) predClass <- c(predClass, as.character(tempTestPred)) obs <- c(obs, as.character(testY)) modelName <- c(modelName, rep(models[[i]]$method, length(tempTestPred))) objName <- c(objName, rep(objectNames[[i]], length(tempTestPred))) dataType <- c(dataType, rep("Test", length(tempTestPred))) } } # Unknown Data if(!is.null(unkX)) { if(!is.data.frame(unkX)) unkX <- as.data.frame(unkX) tempX <- unkX tempX$.outcome <- NULL tempUnkProb <- probFunction(models[[i]]$modelInfo, models[[i]]$finalModel, tempX, models[[i]]$preProcess) tempUnkPred <- apply(tempUnkProb, 1, which.max) tempUnkPred <- colnames(tempUnkProb)[tempUnkPred] tempUnkPred <- factor(tempUnkPred, levels = models[[i]]$modelInfo$levels(models[[i]]$finalModel)) if(verbose) cat(models[[i]]$method, ":", length(tempUnkPred), "unknown predictions were added\n") predProb <- if(is.null(predProb)) tempUnkProb else rbind(predProb, tempUnkProb) predClass <- c(predClass, as.character(tempUnkPred)) obs <- c(obs, rep(NA, length(tempUnkPred))) modelName <- c(modelName, rep(models[[i]]$method, length(tempUnkPred))) objName <- c(objName, rep(objectNames[[i]], length(tempUnkPred))) dataType <- c(dataType, rep("Unknown", length(tempUnkPred))) } if(verbose) cat("\n") } predClass <- factor(predClass, levels = obsLevels) obs <- factor(obs, levels = obsLevels) out <- data.frame(predProb) out$obs <- obs out$pred <- predClass out$model <- modelName out$dataType <- dataType out$object <- objName out } caret/R/lift.R0000644000175000017500000001605512516214271013316 0ustar rbalintrbalintlift <- function(x, ...) UseMethod("lift") lift.default <- function(x, ...) stop("'x' should be a formula") lift.formula <- function(x, data = NULL, class = NULL, subset = TRUE, lattice.options = NULL, cuts = NULL, labels = NULL, ...) { if (!is.null(lattice.options)) { oopt <- lattice.options(lattice.options) on.exit(lattice.options(oopt), add = TRUE) } formula <- x groups <- NULL subset <- eval(substitute(subset), data, environment(x)) form <- latticeParseFormula(formula, data, subset = subset, groups = groups, multiple = TRUE, outer = TRUE, subscripts = TRUE, drop = TRUE) liftData <- data.frame(prob = form$y) probNames <- strsplit(form$right.name, " + ", fixed = TRUE)[[1]] if(!is.null(labels)) { if(length(labels) != length(probNames)) stop("labels should have an element for each term on the rhs of the formula") if(!all(probNames %in% names(labels))) stop(paste("labels should be a named vector or list with names:", paste(probNames, collapse = ", "))) } liftData <- data.frame(liftClassVar = rep(form$left, length(probNames)), liftProbVar = form$right) liftData$liftModelVar <- if(length(probNames) > 1) form$condition[[length(form$condition)]] else probNames if(length(form$condition) > 0 && any(names(form$condition) != "")) { ind <- sum(names(form$condition) != "") tmp <- as.data.frame(form$condition[1:ind]) liftData <- cbind(liftData, tmp) } if(!is.factor(liftData$liftClassVar)) stop("the left-hand side of the formula must be a factor of classes") splitVars <- names(liftData)[!(names(liftData) %in% c("liftClassVar", "liftProbVar"))] if(is.null(class)) class <- levels(liftData$liftClassVar)[1] plotData <- ddply(liftData, splitVars, liftCalc, class = class, cuts = cuts) if(!is.null(labels)) { plotData$originalName <- plotData$liftModelVar plotData$liftModelVar <- as.character(plotData$liftModelVar) for(i in seq(along = labels)) plotData$liftModelVar[plotData$liftModelVar == names(labels)[i]] <- labels[i] plotData$liftModelVar <- factor(plotData$liftModelVar, levels = labels) } out <- list(data = plotData, class = class, probNames = probNames, pct = mean(liftData$liftClassVar == class)*100, call = match.call()) class(out) <- "lift" out } print.lift <- function(x, ...) { printCall(x$call) cat("Models:", paste(unique(x$data$liftModelVar), collapse = ", "), "\n") cat("Event: ", x$class, " (", round( x$pct, 1), "%)\n", sep = "") invisible(x) } plot.lift <- function(x, y = NULL, ...) xyplot.lift(x = x, data = NULL, ...) xyplot.lift <- function(x, data = NULL, plot = "gain", values = NULL, ...){ if(!(plot %in% c("lift", "gain"))) stop(paste("'plot' should be either 'lift' or 'gain'")) if(plot == "gain") { lFormula <- "CumEventPct ~ CumTestedPct" rng <- extendrange(c(0, 100)) opts <- list(...) if(!any(names(opts) == "xlab")) opts$xlab <- "% Samples Tested" if(!any(names(opts) == "ylab")) opts$ylab <- "% Samples Found" if(!any(names(opts) == "type")) opts$type <- "l" if(!any(names(opts) == "ylim")) opts$ylim <- rng if(!any(names(opts) == "xlim")) opts$xlim <- rng if(!any(names(opts) == "panel")) opts$panel <- panel.lift2 } else { lFormula <- "lift ~ cuts" x$data <- x$data[order(x$data$liftModelVar, x$data$cuts),] rng <- extendrange(c(0, 100)) opts <- list(...) if(!any(names(opts) == "xlab")) opts$xlab <- "Cut-Off" if(!any(names(opts) == "ylab")) opts$ylab <- "Lift" if(!any(names(opts) == "type")) opts$type <- "l" } args <- list(x = as.formula(lFormula), data = x$data, pct = x$pc, values = values) if(length(x$probNames) > 1) args$groups <- x$data$liftModelVar args <- c(args, opts) do.call("xyplot", args) } liftCalc <- function(x, class = levels(x$liftClassVar)[1], cuts = NULL) { lvl <- levels(x$liftClassVar) x <- x[order(x$liftProbVar, decreasing = TRUE),] nEvents <- sum(x$liftClassVar == class) baseline <- mean(x$liftClassVar == class) if(!is.null(cuts)) { if(length(cuts) == 1) { cuts <- rev(seq(0, 1, length = cuts)) } else { cuts <- unique(c(1, sort(cuts, decreasing = TRUE), 0)) } } else { cuts <- sort(unique(x$liftProbVar), decreasing = TRUE) cuts <- unique(c(1, sort(cuts, decreasing = TRUE), 0)) } class2 <- levels(x$liftClassVar) class2 <- class2[class2 != class] tmp <- data.frame(cuts = cuts, events = NA, n = NA, Sn = NA, Sp = NA) for(i in seq(along = cuts)) { sub <- x$liftClassVar[x$liftProbVar >= tmp$cuts[i]] tmp$n[i] <- length(sub) tmp$events[i] <- sum(sub == class) prd <- factor(ifelse(x$liftProbVar >= tmp$cuts[i], class, class2), levels = levels(x$liftClassVar)) tmp$Sn[i] <- sensitivity(prd, x$liftClassVar, positive = class) tmp$Sp[i] <- specificity(prd, x$liftClassVar, negative = class2) } tmp$EventPct <- ifelse(tmp$n > 0, tmp$events/tmp$n*100, 0) tmp$CumEventPct <- tmp$events/nEvents*100 tmp$lift <- tmp$events/tmp$n/baseline tmp$CumTestedPct <- tmp$n/nrow(x)*100 tmp } panel.lift <- function(x, y, ...) { panel.xyplot(x, y, ...) panel.abline(0, 1, col = "black") } panel.lift2 <- function (x, y, pct = 0, values = NULL, ...) { polyx <- c(0, pct, 100, 0) polyy <- c(0, 100, 100, 0) regionStyle <- trellis.par.get("reference.line") panel.polygon(polyx, polyy, col = regionStyle$col, border = regionStyle$col) panel.xyplot(x, y, ...) if(!is.null(values)){ theDots <- list(...) if(any(names(theDots) == "groups")) { dat <- data.frame(x = x, y = y, groups = theDots$groups) ung <- unique(dat$groups) for(i in seq(along = ung)) { dat0 <- subset(dat, groups == ung[i]) plotRef(dat0$x, dat0$y, values, iter = i) } } else plotRef(x, y, values) } } plotRef <- function(x, y, v, iter = 0) { if(iter == 0) { lineStyle <- trellis.par.get("plot.line") } else { lineStyle <- trellis.par.get("superpose.line") lineStyle <- lapply(lineStyle, function(x, i) x[min(length(x), i)], i = iter) } erx <- extendrange(x) ery <- extendrange(y) values <- approx(y, x, xout = v) for(i in seq(along = values$x)) { panel.segments(values$y[i], ery[1], values$y[i], values$x[i], lty = lineStyle$lty, col = lineStyle$col, alpha = lineStyle$alpha, lwd = lineStyle$lwd) panel.segments(erx[1], values$x[i], values$y[i], values$x[i], lty = lineStyle$lty, col = lineStyle$col, alpha = lineStyle$alpha, lwd = lineStyle$lwd) } } caret/R/preProcess.R0000644000175000017500000004007612516214271014505 0ustar rbalintrbalintppMethods <- c("BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "pca", "ica", "spatialSign", "medianImpute") preProcess <- function(x, ...) UseMethod("preProcess") preProcess.default <- function(x, method = c("center", "scale"), thresh = 0.95, pcaComp = NULL, na.remove = TRUE, k = 5, knnSummary = mean, outcome = NULL, fudge = .2, numUnique = 3, verbose = FALSE, ...) { ppMethods <- c("BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica", "spatialSign") if(any(!(method %in% ppMethods))) stop(paste("'method' should be one of:", paste(ppMethods, collapse = ", "))) if(is.null(method)) stop("NULL values of 'method' are not allowed") if(any(method %in% "range") & any(method %in% c("center", "scale", "BoxCox"))) stop("centering, scaling and/or Box-Cox transformations are inconsistent with scaling to a range of [0, 1]") if(all(c("pca", "ica") %in% method)) { warning("fastICA automatically uncorrelates the data using PCA. method = 'pca' is not needed") method <- method[method != "pca"] } if(any(method %in% c("pca", "ica", "knnImpute", "spatialSign")) & !(any(method == "scale"))) method <- c(method, "scale") if(any(method %in% c("pca", "ica", "knnImpute", "spatialSign")) & !(any(method == "center"))) method <- c(method, "center") if(sum(c("knnImpute","bagImpute", "medianImpute") %in% method) > 1) stop("please pick only one imputation method") method <- unique(method) ## the row.norm option in fastICA states: "logical value indicating whether rows ## of the data matrix X should be standardized beforehand." Basically, this means that ## we would center *and* scale before the ICA step, so let's adjust the "scale" method too if(any(method == "ica")) { theDots <- list(...) row.norm <- if(is.null(list(...)$row.norm)) FALSE else list(...)$row.norm if(row.norm & !(any(method == "scale"))) method <- c(method, "scale") } if(is.matrix(x)) { if(!is.numeric(x)) stop("x must be numeric") } if(is.data.frame(x)) { isFactor <- unlist(lapply(x, is.factor)) isChar <- unlist(lapply(x, is.character)) if(any(isFactor | isChar)) stop("all columns of x must be numeric") } if(!is.matrix(x) & !is.data.frame(x)) { msg <- paste("preProcess is only designed for simple numeric", "matrices and data frames; your predictors have class(es): (", paste("'", class(x), "'", sep = "", collapse = ", "), ") and errors may occur") warning(msg) } theCall <- match.call(expand.dots = TRUE) if(any(method == "BoxCox")) { if(verbose) cat("Estimating Box-Cox transformations for the predictors...") bc <- lapply(x, BoxCoxTrans, fudge = fudge, na.rm = na.remove, numUnique = numUnique, x = if(is.null(outcome)) rep(1, nrow(x)) else outcome) if(verbose) cat(" applying them to training data\n") ## Find a better way of doing this for(i in seq(along = bc)) x[,i] <- predict(bc[[i]], x[,i]) } else bc <- NULL if(any(method == "YeoJohnson")) { yjWrap <- function(x, numUnique = numUnique) { if(length(unique(x)) >= numUnique) { out <- try(powerTransform(y ~ 1, data = data.frame(y = x[!is.na(x)]), family = "yjPower"), silent = TRUE) if(class(out)[1] == "try-error") out <- NA } else out <- NA out } if(verbose) cat("Estimating Yeo-Johnson transformations for the predictors...") yj <- lapply(x, yjWrap, numUnique = numUnique) if(verbose) cat(" applying them to training data\n") ## Find a better way of doing this lam <- unlist(lapply(yj, function(x) if(class(x) == "powerTransform") x$lambda else NA)) lam <- lam[!is.na(lam)] if(length(lam) > 0) { for(i in seq(along = lam)) { who <- gsub("\\.Y1$", "", names(lam)[i]) x[,who] <- yjPower(x[,who], yj[[who]]$lambda) } } } else yj <- NULL if(any(method == "expoTrans")) { if(verbose) cat("Estimating exponential transformations for the predictors...") et <- lapply(x, expoTrans.default, numUnique = numUnique) if(verbose) cat(" applying them to training data\n") for(i in seq(et)) x[,i] <- predict(et[[i]], x[,i]) } else et <- NULL if(any(method %in% c("center"))) { if(verbose) cat("Calculating means for centering\n") centerValue <- apply(x, 2, mean, na.rm = na.remove) } else centerValue <- NULL if(any(method %in% c("scale"))) { if(verbose) cat("Calculating standard deviations for scaling\n") scaleValue <- apply(x, 2, sd, na.rm = na.remove) } else scaleValue <- NULL if(any(method == "range")) { ranges <- apply(x, 2, function(x) c(min(x, na.rm = na.remove), max(x, na.rm = na.remove))) } else ranges <- NULL if(any(scaleValue == 0)) { warning( paste( "These variables have zero variances:", paste( names(scaleValue)[which(scaleValue == 0)], collapse = ", "))) scaleValue[which(scaleValue == 0)] <- 1 } if(any(method == "bagImpute")) { if(verbose) cat("Computing bagging models for each predictor...") bagModels <- as.list(colnames(x)) names(bagModels) <- colnames(x) bagModels <- lapply(bagModels, bagImp, x = x) if(verbose) cat(" done\n") } else bagModels <- NULL if (any(method == "medianImpute")) { if(verbose) cat("Computing medians for each predictor...") medianValue <- apply(x, 2, median, na.rm=TRUE) if (any(is.na(medianValue))) { warning( paste( "These variables are never filled:", paste(names(medianValue)[is.na(medianValue)], collapse = ", "))) medianValue[is.na(medianValue)] <- 0 } if(verbose) cat(" done\n") } else medianValue <- NULL x <- x[complete.cases(x),,drop = FALSE] if(any(method == "pca")) { if(verbose) cat("Computing PCA loadings\n") tmp <- prcomp(x, scale = TRUE, retx = FALSE) if(is.null(pcaComp)) { cumVar <- cumsum(tmp$sdev^2/sum(tmp$sdev^2)) numComp <- max(2, which.max(cumVar > thresh)) } else numComp <- pcaComp rot <- tmp$rotation[,1:numComp] } else { rot <- NULL numComp <- NULL } if(any(method == "ica")) { if(verbose) cat("Computing ICA loadings\n") requireNamespaceQuietStop("fastICA") x <- sweep(x, 2, centerValue, "-") if(!row.norm & any(method == "scale")) x <- sweep(x, 2, scaleValue, "/") tmp <- fastICA::fastICA(x, ...) ica <- list( ## S = tmp$S, ## was used for debugging row.norm = row.norm, K = tmp$K, W = tmp$W) } else { ica <- NULL } out <- list(call = theCall, dim = dim(x), bc = bc, yj = yj, et = et, mean = centerValue, std = scaleValue, ranges = ranges, rotation = rot, method = method, thresh = thresh, pcaComp = pcaComp, numComp = numComp, ica = ica, k = k, knnSummary = knnSummary, bagImp = bagModels, median = medianValue, data = if(any(method == "knnImpute")) scale(x[complete.cases(x),,drop = FALSE]) else NULL) structure(out, class = "preProcess") } predict.preProcess <- function(object, newdata, ...) { dataNames <- colnames(newdata) ## For centering and scaling, we can be flexible if a column in the ## original data set is not in newdata if(!is.null(object$mean)) { if(!all(names(object$mean) %in% dataNames)) { if(all(dataNames %in% names(object$mean))) { object$mean <- object$mean[names(object$mean) %in% dataNames] warning("newdata does not contain some variables") } else { vars <- dataNames[!(dataNames %in% names(object$mean))] stop(paste("The following variables were not pre-processed:", paste(vars, collapse = ","))) } } } if(!is.null(object$std)) { if(!all(names(object$std) %in% dataNames)) { if(all(dataNames %in% names(object$std))) { object$std <- object$std[names(object$std) %in% dataNames] } } } if(!is.null(object$rotation)) { if(!all(names(object$rotation) %in% dataNames)) { stop("newdata does not contain some variables") } } oldClass <- class(newdata) if(!is.null(object$bc)) { lam <- unlist(lapply(object$bc, function(x) x$lambda)) lamIndex <- which(!is.na(lam)) if(length(lamIndex) > 0) { for(i in names(lamIndex)) { tt<- newdata[,i] tt <- tt[!is.na(tt)] if(any(tt <= 0)) { cat(i, "\n") } newdata[,i] <- predict(object$bc[[i]], newdata[,i]) } } } if(!is.null(object$yj)) { lam <- unlist(lapply(object$yj, function(x) if(class(x) == "powerTransform") x$lambda else NA)) lam <- lam[!is.na(lam)] if(length(lam) > 0) { for(i in seq(along = lam)) { who <- gsub("\\.Y1$", "", names(lam)[i]) newdata[,who] <- yjPower(newdata[,who], object$yj[[who]]$lambda) } } } if(!is.null(object$et)) { for(i in seq(along = object$et)) { who <- names(object$et)[i] newdata[,who] <- predict(object$et[[who]], newdata[,who]) } } if(any(object$method == "range")) { newdata <- sweep(newdata, 2, object$ranges[1,], "-") newdata <- sweep(newdata, 2, object$ranges[2,] - object$ranges[1,], "/") } if(any(object$method == "center")) newdata <- sweep(newdata, 2, object$mean, "-") if(any(object$method %in% c("scale"))) newdata <- sweep(newdata, 2, object$std, "/") cc <- complete.cases(newdata) if(any(object$method == "knnImpute") && any(!cc)) { hasMiss <- newdata[!cc,,drop = FALSE] hasMiss <- apply(hasMiss, 1, nnimp, old = object$data, k = object$k, foo = object$knnSummary) hasMiss <- t(hasMiss) if(class(newdata)[1] == class(hasMiss)[1]) { newdata[!cc,] <- hasMiss } else { if(is.data.frame(newdata)) { newdata[!cc,] <- as.data.frame(hasMiss) } else newdata[!cc,] <- as.matrix(hasMiss) } } if(any(object$method == "bagImpute") && any(!cc)) { loadNamespace("ipred") hasMiss <- newdata[!cc,,drop = FALSE] missingVars <- apply(hasMiss, 2, function(x) any(is.na(x))) missingVars <- names(missingVars)[missingVars] ## ipred's bagging procedure only allows for data frames if(!is.data.frame(hasMiss)) hasMiss <- as.data.frame(hasMiss) for(i in seq(along = missingVars)) { preds <- predict(object$bagImp[[missingVars[i]]]$model, hasMiss[, !colnames(hasMiss) %in% missingVars[i]]) hasMiss[is.na(hasMiss[,missingVars[i]]), missingVars[i]] <- preds[is.na(hasMiss[,missingVars[i]])] } if(class(newdata)[1] == class(hasMiss)[1]) { newdata[!cc,] <- hasMiss } else { if(is.data.frame(newdata)) { newdata[!cc,] <- as.data.frame(hasMiss) } else newdata[!cc,] <- as.matrix(hasMiss) } } if (any(object$method == "medianImpute") && any(!cc)) { missingVars <- apply(newdata, 2, function(x) any(is.na(x))) missingVars <- if(is.null(names(missingVars))) which(missingVars) else names(missingVars)[missingVars] for (v in missingVars) { newdata[is.na(newdata[, v]), v] <- object$median[v] } } if(any(object$method == "pca")) { newdata <-if(is.matrix(newdata)) newdata %*% object$rotation else as.matrix(newdata) %*% object$rotation } if(any(object$method == "ica")) { if(!is.matrix(newdata)) newdata <- as.matrix(newdata) ##if(object$ica$row.norm) newdata <- apply(newdata, 1, function(u) u/sd(u)) newdata <- newdata %*% object$ica$K %*% object$ica$W colnames(newdata) <- paste("ICA", 1:ncol(object$ica$W), sep = "") } if(any(oldClass == "data.frame")) newdata <- as.data.frame(newdata) if(any(object$method == "spatialSign")) newdata <- spatialSign(newdata) if(!(any(object$method %in% c("pca", "ica")))) colnames(newdata) <- dataNames newdata } print.preProcess <- function(x, ...) { printCall(x$call) cat("Created from", x$dim[1], "samples and", x$dim[2], "variables\n") pp <- x$method pp <- gsub("BoxCox", "Box-Cox transformation", pp) pp <- gsub("YeoJohnson", "Yeo-Johnson transformation", pp) pp <- gsub("scale", "scaled", pp) pp <- gsub("center", "centered", pp) pp <- gsub("pca", "principal component signal extraction", pp) pp <- gsub("ica", "independent component signal extraction", pp) pp <- gsub("spatialSign", "spatial sign transformation", pp) pp <- gsub("knnImpute", paste(x$k, "nearest neighbor imputation"), pp) pp <- gsub("bagImpute", "bagged tree imputation", pp) pp <- gsub("range", "re-scaling to [0, 1]", pp) ppText <- paste("Pre-processing:", paste(pp, collapse = ", ")) cat(truncateText(ppText), "\n\n") if(any(x$method == "BoxCox")) { cat("Lambda estimates for Box-Cox transformation:\n") if(length(x$bc) < 11) { lmbda <- unlist(lapply(x$bc, function(x) x$lambda)) naLmbda <- sum(is.na(lmbda)) cat(paste(round(lmbda[!is.na(lmbda)], 2), collapse = ", ")) if(naLmbda > 0) cat(" (#NA: ", naLmbda, ")\n", sep = "") } else print(summary(unlist(lapply(x$bc, function(x) x$lambda)))) cat("\n") } if(any(x$method == "YeoJohnson")) { cat("Lambda estimates for Yeo-Johnson transformation:\n") if(length(x$yj) < 11) { lmbda <- unlist(lapply(x$yj, function(x) if(class(x) == "powerTransform") x$lambda else NA)) naLmbda <- sum(is.na(lmbda)) cat(paste(round(lmbda[!is.na(lmbda)], 2), collapse = ", ")) if(naLmbda > 0) cat(" (#NA: ", naLmbda, ")\n", sep = "") } else print(summary(unlist(lapply(x$yj, function(x) if(class(x) == "powerTransform") x$lambda else NA)))) cat("\n") } if(any(x$method == "pca")) { if(is.null(x$pcaComp)) { cat("PCA needed", x$numComp, ifelse(x$numComp > 1, "components", "component"), "to capture", round(x$thresh*100, 2), "percent of the variance\n") } else { cat("PCA used", x$pcaComp, ifelse(x$pcaComp > 1, "components", "component"), "as specified.\n") } } if(any(x$method == "ica")) cat("ICA used", ncol(x$ica$W), "components\n") } nnimp <- function(new, old, k, foo) { requireNamespaceQuietStop("RANN") if(all(is.na(new))) stop("cannot impute when all predictors are missing in the new data point") nms <- names(new) cols2 <- which(!is.na(new)) new <- matrix(new, ncol = length(new)) colnames(new) <- nms non_missing_cols <- cols2 nn <- RANN::nn2(old[, non_missing_cols, drop = FALSE], new[, non_missing_cols, drop = FALSE], k = k) tmp <- old[nn$nn.idx, -non_missing_cols, drop = FALSE] subs <- apply(tmp, 2, foo, na.rm = TRUE) new[, -non_missing_cols] <- subs new } bagImp <- function(var, x, B = 10) { requireNamespaceQuietStop("ipred") ## The formula interface is much slower than the ## (y, X) interface, but the latter would have to ## do case-wise deletion of samples from the ## training set. if(!is.data.frame(x)) x <- as.data.frame(x) mod <- ipred::bagging(as.formula(paste(var, "~.")), data = x, nbagg = B, x = FALSE, keepX = FALSE) trim_code <- getModelInfo("treebag", FALSE)[[1]]$trim list(var = var, model = trim_code(mod)) } caret/R/sensitivity.R0000644000175000017500000000352312453550064014751 0ustar rbalintrbalintsensitivity <- function(data, ...){ UseMethod("sensitivity") } "sensitivity.default" <- function(data, reference, positive = levels(reference)[1], na.rm = TRUE, ...) { if(!is.factor(reference) | !is.factor(data)) stop("inputs must be factors") ## todo: relax the =2 constraint and let ngative length be > 2 if(length(unique(c(levels(reference), levels(data)))) != 2) stop("input data must have the same two levels") if(na.rm) { cc <- complete.cases(data) & complete.cases(reference) if(any(!cc)) { data <- data[cc] reference <- reference[cc] } } numer <- sum(data %in% positive & reference %in% positive) denom <- sum(reference %in% positive) sens <- ifelse(denom > 0, numer / denom, NA) sens } "sensitivity.table" <- function(data, positive = rownames(data)[1], ...) { ## "truth" in columns, predictions in rows if(!all.equal(nrow(data), ncol(data))) stop("the table must have nrow = ncol") if(!all.equal(rownames(data), colnames(data))) stop("the table must the same groups in the same order") if(nrow(data) > 2) { tmp <- data data <- matrix(NA, 2, 2) colnames(data) <- rownames(data) <- c("pos", "neg") posCol <- which(colnames(tmp) %in% positive) negCol <- which(!(colnames(tmp) %in% positive)) data[1, 1] <- sum(tmp[posCol, posCol]) data[1, 2] <- sum(tmp[posCol, negCol]) data[2, 1] <- sum(tmp[negCol, posCol]) data[2, 2] <- sum(tmp[negCol, negCol]) data <- as.table(data) positive <- "pos" rm(tmp) } numer <- sum(data[positive, positive]) denom <- sum(data[, positive]) sens <- ifelse(denom > 0, numer / denom, NA) sens } "sensitivity.matrix" <- function(data, positive = rownames(data)[1], ...) { data <- as.table(data) sensitivity.table(data) } caret/R/varImp.R0000644000175000017500000002333412520454361013615 0ustar rbalintrbalint"varImp" <- function(object, ...){ UseMethod("varImp") } GarsonWeights <- function(object) { beta <- coef(object) abeta <- abs(beta) nms <- names(beta) i2h <- array(NA, dim = object$n[2:1]) h2o <- array(NA, dim = object$n[2:3]) for (hidden in 1:object$n[2]) { for (input in 1:object$n[1]) { label <- paste("i", input, "->h", hidden,"$", sep = "") i2h[hidden, input] <- abeta[grep(label, nms, fixed = FALSE)] } } for(hidden in 1:object$n[2]){ for(output in 1:object$n[3]){ label <- paste("h", hidden, "->o", ifelse(object$n[3] == 1, "", output), sep = "") h2o[hidden,output] <- abeta[grep(label, nms, fixed = TRUE)] } } if(FALSE) { ## Test case from Gevrey, M., Dimopoulos, I., & Lek, ## S. (2003). Review and comparison of methods to study the ## contribution of variables in artificial neural network ## models. ecological modelling, 160(3), 249-264. i2h <- matrix(c(-1.67624, 3.29022, 1.32466, -0.51874, -0.22921, -0.25526, -4.01764, 2.12486, -0.08168, -1.75691, -1.44702, 0.58286), ncol = 3, byrow = TRUE) h2o <- matrix(c(4.57857, -0.48815, -5.73901, -2.65221), ncol = 1) } ## From Gevrey et al. (2003): "For each hidden neuron i, multiply ## the absolute value of the hidden-output layer connection ## weight by the absolute value of the hidden-input layer ## connection weight. Do this for each input variable j. The ## following products Pij are obtained" ## We'll do this one response at a time. Gevrey et al. (2003) do ## not discuss multiple outputs, but the results are the same (at ## least in the case of classification). imp <- matrix(NA, nrow = object$n[1], ncol = object$n[3]) for(output in 1:object$n[3]) { Pij <- i2h * NA for(hidden in 1:object$n[2]) Pij[hidden,] <- i2h[hidden,] * h2o[hidden,output] ## "For each hidden neuron, divide Pij by the sum for all the ## input variables to obtain Qij. For example for Hidden 1, Q11 = ## P11/(P11+P12+P13). Qij <- Pij * NA for(hidden in 1:object$n[2]) Qij[hidden,] <- Pij[hidden,] / sum(Pij[hidden,]) ## "For each input neuron, sum the product Sj formed from the ## previous computations of Qij. For example, S1 = ## Q11+Q21+Q31+Q41." Sj <- apply(Qij, 2, sum) ## "Divide Sj by the sum for all the input variables. Expressed as ## a percentage, this gives the relative importance or ## distribution of all output weights attributable to the given ## input variable. For example, for the input neuron 1, the ## relative importance is equal to (S1/100)/(S1+S2+S3)" imp[,output] <- Sj/sum(Sj)*100 rm(Pij, Qij, Sj) } colnames(imp) <- if(!is.null(colnames(object$residuals))) colnames(object$residuals) else paste("Y", 1:object$n[3], sep = "") rownames(imp) <- if(!is.null(object$coefnames)) object$coefnames else paste("X", 1:object$n[1], sep = "") imp } varImp.bagEarth <- function(object, ...){ code <- getModelInfo("bagEarth", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.bagFDA <- function(object, ...){ code <- getModelInfo("bagFDA", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.C5.0 <- function(object, ...){ code <- getModelInfo("C5.0", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.cubist <- function(object, weights = c(0.5, 0.5), ...){ code <- getModelInfo("cubist", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, weights = weights, ...) } varImp.dsa <- function(object, cuts = NULL, ...){ code <- getModelInfo("partDSA", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, cuts = cuts, ...) } varImp.glm <- function(object, ...){ code <- getModelInfo("glm", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.glmnet <- function(object, lambda = NULL, ...){ code <- getModelInfo("glmnet", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, lambda = lambda, ...) } varImp.JRip <- function(object, ...){ code <- getModelInfo("JRip", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.multinom <- function(object, ...){ code <- getModelInfo("multinom", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.nnet <- function(object, ...){ code <- getModelInfo("nnet", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.PART <- function(object, ...){ code <- getModelInfo("PART", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.RRF <- function(object, ...){ code <- getModelInfo("RRF", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.rpart <- function(object, surrogates = FALSE, competes = TRUE, ...){ code <- getModelInfo("rpart", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, surrogates = surrogates, competes = competes, ...) } varImp.randomForest <- function(object, ...){ code <- getModelInfo("rf", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.gbm <- function(object, numTrees = NULL, ...){ code <- getModelInfo("gbm", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, numTrees = numTrees, ...) } varImp.classbagg <- function(object, ...){ code <- getModelInfo("treebag", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.regbagg <- function(object, ...){ code <- getModelInfo("treebag", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.pamrtrained <- function(object, threshold, data, ...){ code <- getModelInfo("pam", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, threshold = object$bestTune$threshold, data = object$finalModel$xData, ...) } varImp.lm <- function(object, ...){ code <- getModelInfo("lm", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.mvr <- function(object, estimate = NULL, ...){ code <- getModelInfo("pls", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, estimate = estimate, ...) } varImp.earth <- function(object, value = "gcv", ...){ code <- getModelInfo("earth", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, value = value, ...) } varImp.RandomForest <- function(object, ...){ code <- getModelInfo("cforest", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.plsda <- function(object, ...){ code <- getModelInfo("pls", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } varImp.fda <- function(object, value = "gcv", ...){ code <- getModelInfo("fda", regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, value = value, ...) } varImp.gam <- function(object, ...){ mod <- if(any(names(object) == "optimizer")) "gam" else "gamLoess" code <- getModelInfo(mod, regex = FALSE)[[1]] checkInstall(code$library) for(i in seq(along = code$library)) do.call("require", list(package = code$library[i])) code$varImp(object, ...) } caret/R/classDist.R0000644000175000017500000000547012453550064014313 0ustar rbalintrbalintclassDist <- function (x, ...) UseMethod("classDist") classDist.default <- function(x, y, groups = 5, pca = FALSE, keep = NULL, ...) { if(is.numeric(y)) { y <- cut(y, unique(quantile(y, probs = seq(0, 1, length = groups + 1))), include.lowest = TRUE) classLabels <- paste(round((1:groups)/groups*100, 2)) y <- factor(y) cuts <- levels(y) } else { classLabels <- levels(y) cuts <- NULL } p <- ncol(x) if(pca) { pca <- prcomp(x, center = TRUE, scale. = TRUE, tol = sqrt(.Machine$double.eps)) keep <- min(keep, ncol(pca$rotation)) if(!is.null(keep)) pca$rotation <- pca$rotation[, 1:keep, drop = FALSE] x <- as.data.frame(predict(pca, newdata = x)) } else pca <- NULL x <- split(x, y) getStats <- function(u) { if(nrow(u) < ncol(u)) stop("there must be more rows than columns for this class") A <- try(cov(u), silent = TRUE) if(class(A) == "try-error") stop("Cannot compute the covariance matrix") A <- try(solve(A), silent = TRUE) if(class(A) == "try-error") stop("Cannot invert the covariance matrix") list(means = colMeans(u, na.rm = TRUE), A = A) } structure( list(values = lapply(x, getStats), classes = classLabels, cuts = cuts, pca = pca, call = match.call(), p = p, n = unlist(lapply(x, nrow))), class = "classDist") } print.classDist <- function(x, ...) { printCall(x$call) if(!is.null(x$cuts)) { cat("Classes based on", length(x$cuts) - 1, "cuts of the data\n") paste(x$cuts, collapse = " ") cat("\n") } if(!is.null(x$pca)) cat("PCA applied,", ncol(x$pca$rotation), "components retained\n\n") cat("# predictors variables:", x$p, "\n") cat("# samples:", paste( paste(x$n, ifelse(is.null(x$cuts), " (", " "), names(x$n), ifelse(is.null(x$cuts), ")", ""), sep = ""), collapse = ", "), "\n") invisible(x) } predict.classDist <- function(object, newdata, trans = log, ...) { if(!is.null(object$pca)) { newdata <- predict(object$pca, newdata = newdata) } pred <- function(a, x) mahalanobis(x, center = a$means, cov = a$A, inverted = TRUE) out <- lapply(object$values, pred, x = newdata) out <- do.call("cbind", out) colnames(out) <- paste("dist.", object$classes, sep = "") if(!is.null(trans)) out <- apply(out, 2, trans) out } caret/R/plotObsVsPred.R0000644000175000017500000000300612453550064015121 0ustar rbalintrbalintplotObsVsPred <- function(object, equalRanges = TRUE, ...) { object <- object[object$dataType != "Unknown",] object$dataType <- factor(object$dataType) if(is.factor(object$obs)) { agreement <- object$obs == object$pred accuracyTable <- by(agreement, list(model = object$model, data = object$dataType), mean) accuracyDF <- data.frame(unclass(accuracyTable)) accuracyStacked <- stack(accuracyDF) accuracyStacked$model <- rep(dimnames(accuracyDF)[[1]], dim(accuracyDF)[2]) names(accuracyStacked) <- c("Accuracy", "Data", "Model") accuracyStacked$Data <- factor( ifelse(accuracyStacked$Data == "Training", "Training (uncorrected)", as.character(accuracyStacked$Data))) out <- dotplot(Model ~ Accuracy, accuracyStacked, groups = accuracyStacked$Data, ...) } else { if(equalRanges) { xLimits <- yLimits <- extendrange(c(object$obs, object$pred)) } else { xLimits <- extendrange(object$obs) yLimits <- extendrange(object$pred) } out <- xyplot(obs ~ pred|model * dataType, object, xlim = xLimits, ylim = yLimits, panel = function(x, y, groups, subscripts, ...) { panel.xyplot(x, y, cex = .6) panel.abline(0, 1, col = trellis.par.get("superpose.line")$col[1], lty = 2) panel.loess(x, y, span = .75) }, xlab = "Predicted", ylab = "Observed", ...) } out } caret/R/splsda.R0000644000175000017500000001001112453550064013633 0ustar rbalintrbalintsplsda <- function (x, ...) UseMethod("splsda") predict.splsda <- function(object, newdata = NULL, type = "class", ...) { requireNamespaceQuietStop("spls") tmpPred <- spls::predict.spls(object, newx = newdata) if(type == "raw") return(tmpPred) if(is.null(object$probModel)) { ## use softmax out <- switch(type, class = { classIndex <- object$obsLevels[apply(tmpPred, 1, which.max)] factor(classIndex, levels = object$obsLevels) }, prob = t(apply(tmpPred, 1, function(data) exp(data)/sum(exp(data))))) } else { requireNamespaceQuietStop("klaR") ## Bayes rule tmpPred <- as.data.frame(tmpPred[,-length(object$obsLevels)]) pred <- predict(object$probModel, tmpPred) out <- switch(type, class = pred$class, prob = pred$posterior) } out } splsda.default <- function(x, y, probMethod = "softmax", prior = NULL, ...) { requireNamespaceQuietStop("spls") funcCall <- match.call(expand.dots = TRUE) if(probMethod == "softmax") { if(!is.null(prior)) warning("Priors are ignored unless probMethod = \"Bayes\"") } if(is.factor(y)) { obsLevels <- levels(y) oldY <- y y <- class2ind(y) } else { if(is.matrix(y)) { test <- apply(y, 1, sum) if(any(test != 1)) stop("the rows of y must be 0/1 and sum to 1") obsLevels <- colnames(y) if(is.null(obsLevels)) stop("the y matrix must have column names") oldY <- obsLevels[apply(y, 1, which.max)] } else stop("y must be a matrix or a factor") } if(!is.matrix(x)) x <- as.matrix(x) tmpData <- data.frame(n = paste("row", 1:nrow(y), sep = "")) tmpData$y <- y tmpData$x <- x out <- spls::spls(x, y, ...) out$obsLevels <- obsLevels out$probMethod <- probMethod if(probMethod == "Bayes") { requireNamespaceQuietStop("klaR") makeModels <- function(x, y, pri) { probModel <- klaR::NaiveBayes(x, y, prior = pri, usekernel = TRUE) probModel$train <- predict(probModel)$posterior probModel$x <- NULL probModel } train <- predict(out, as.matrix(tmpData$x)) ## Get the raw model predictions, but leave one behind since the ## final class probs sum to one train <- train[, -length(obsLevels), drop = FALSE] out$probModel <- makeModels(train, oldY, pri = prior) } else out$probModel <- NULL ##out$call <- funcCall class(out) <- "splsda" out } print.splsda <- function (x, ...) { xmat <- x$x p <- ncol(xmat) A <- x$A xAnames <- colnames(xmat)[A] q <- ncol(x$y) eta <- x$eta K <- x$K kappa <- x$kappa select <- x$select fit <- x$fit if (q == 1) { cat("\nSparse Partial Least Squares for discriminant analysis\n") cat("----\n") cat(paste("Parameters: eta = ", eta, ", K = ", K, "\n", sep = "")) } if (q > 1) { cat("\nSparse Partial Least Squares for discriminant analysis\n") cat("----\n") cat(paste("Parameters: eta = ", eta, ", K = ", K, ", kappa = ", kappa, "\n", sep = "")) } cat(paste("PLS algorithm:\n", select, " for variable selection, ", fit, " for model fitting\n", sep = "")) switch(x$probMethod, softmax = cat("The softmax function was used to compute class probabilities.\n"), Bayes = cat("Bayes rule was used to compute class probabilities.\n")) cat(paste("\nSPLS chose ", length(A), " variables among ", p, " variables\n\n", sep = "")) cat("Selected variables: \n") if (!is.null(xAnames)) { for (i in 1:length(A)) { cat(paste(xAnames[i], "\t", sep = "")) if (i%%5 == 0) { cat("\n") } } } else { for (i in 1:length(A)) { cat(paste(A[i], "\t", sep = "")) if (i%%5 == 0) { cat("\n") } } } cat("\n") } caret/R/adaptive.R0000644000175000017500000014362312516214271014157 0ustar rbalintrbalint## TODO: clean up code duplication? ## add na.action to all eval functions ## change multiplier and alpha to confidence adaptiveWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, metric, maximize, testing = FALSE, ...) { loadNamespace("caret") ppp <- list(options = ppOpts) ppp <- c(ppp, ctrl$preProcOptions) printed <- format(info$loop, digits = 4) colnames(printed) <- gsub("^\\.", "", colnames(printed)) ## no 632, oob or loo resampleIndex <- ctrl$index `%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1) pkgs <- c("methods", "caret") if(!is.null(method$library)) pkgs <- c(pkgs, method$library) init_index <- seq(along = resampleIndex)[1:(ctrl$adaptive$min-1)] extra_index <- seq(along = resampleIndex)[-(1:(ctrl$adaptive$min-1))] init_result <- foreach(iter = seq(along = init_index), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:% foreach(parm = 1:nrow(info$loop), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op% { testing <- FALSE if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) loadNamespace("caret") if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter) modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] if(testing) cat("pre-model\n") if(is.null(info$submodels[[parm]]) || nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod <- try( createModel(x = x[modelIndex,,drop = FALSE ], y = y[modelIndex], wts = wts[modelIndex], method = method, tuneValue = info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...), silent = TRUE) if(class(mod)[1] != "try-error") { predicted <- try( predictionFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod), silent = TRUE) if(class(predicted)[1] == "try-error") { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("predictions failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(predicted), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } } else { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("model fit failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(mod), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } if(testing) print(head(predicted)) if(ctrl$classProbs) { if(class(mod)[1] != "try-error") { probValues <- probFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod) } else { probValues <- as.data.frame(matrix(NA, nrow = nPred, ncol = length(lev))) colnames(probValues) <- lev if(!is.null(submod)) { tmp <- probValues probValues <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = probValues)) probValues[[i]] <- tmp rm(tmp) } } if(testing) print(head(probValues)) } ################################## if(!is.null(submod)) { ## merge the fixed and seq parameter values together allParam <- expandParameters(info$loop[parm,,drop = FALSE], info$submodels[[parm]]) allParam <- allParam[complete.cases(allParam),, drop = FALSE] ## collate the predicitons across all the sub-models predicted <- lapply(predicted, function(x, y, wts, lv) { if(!is.factor(x) & is.character(x)) x <- factor(as.character(x), levels = lv) out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE) if(!is.null(wts)) out$weights <- wts out }, y = y[holdoutIndex], wts = wts[holdoutIndex], lv = lev) if(testing) print(head(predicted)) ## same for the class probabilities if(ctrl$classProbs) { for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) } if(ctrl$savePredictions) { tmpPred <- predicted for(modIndex in seq(along = tmpPred)) { tmpPred[[modIndex]]$rowIndex <- holdoutIndex tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex,,drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ## get the performance for this resample for each sub-model thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if(testing) print(head(thisResample)) ## for classification, add the cell counts if(length(lev) > 1) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { if(is.factor(y)) predicted <- factor(as.character(predicted), levels = lev) tmp <- data.frame(pred = predicted, obs = y[holdoutIndex], stringsAsFactors = FALSE) ## Sometimes the code above does not coerce the first ## columnn to be named "pred" so force it names(tmp)[1] <- "pred" if(!is.null(wts)) tmp$weights <- wts[holdoutIndex] if(ctrl$classProbs) tmp <- cbind(tmp, probValues) if(ctrl$savePredictions) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, info$loop[parm,,drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ################################## thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) ## if classification, get the confusion matrix if(length(lev) > 1) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample)) thisResample <- cbind(thisResample, info$loop[parm,,drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter, FALSE) list(resamples = thisResample, pred = tmpPred) } ## end initial loop over resamples and models init_resamp <- rbind.fill(init_result[names(init_result) == "resamples"]) init_pred <- if(ctrl$savePredictions) rbind.fill(init_result[names(init_result) == "pred"]) else NULL names(init_resamp) <- gsub("^\\.", "", names(init_resamp)) if(any(!complete.cases(init_resamp[,!grepl("^cell|Resample", colnames(init_resamp)),drop = FALSE]))) warning("There were missing values in resampled performance measures.") init_summary <- ddply(init_resamp[,!grepl("^cell|Resample", colnames(init_resamp)),drop = FALSE], ## TODO check this for seq models gsub("^\\.", "", colnames(info$loop)), MeanSD, exclude = gsub("^\\.", "", colnames(info$loop))) new_info <- info num_left <- Inf for(iter in ctrl$adaptive$min:length(resampleIndex)) { if(num_left > 1) { modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] printed <- format(new_info$loop, digits = 4) colnames(printed) <- gsub("^\\.", "", colnames(printed)) adapt_results <- foreach(parm = 1:nrow(new_info$loop), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op% { if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter, TRUE) if(is.null(new_info$submodels[[parm]]) || nrow(new_info$submodels[[parm]]) > 0) { submod <- new_info$submodels[[parm]] } else submod <- NULL mod <- try( createModel(x = x[modelIndex,,drop = FALSE ], y = y[modelIndex], wts = wts[modelIndex], method = method, tuneValue = new_info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...), silent = TRUE) if(class(mod)[1] != "try-error") { predicted <- try( predictionFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod), silent = TRUE) if(class(predicted)[1] == "try-error") { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("predictions failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(predicted), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } } else { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("model fit failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(mod), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } if(testing) print(head(predicted)) if(ctrl$classProbs) { if(class(mod)[1] != "try-error") { probValues <- probFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod) } else { probValues <- as.data.frame(matrix(NA, nrow = nPred, ncol = length(lev))) colnames(probValues) <- lev if(!is.null(submod)) { tmp <- probValues probValues <- vector(mode = "list", length = nrow(new_info$submodels[[parm]]) + 1) for(i in seq(along = probValues)) probValues[[i]] <- tmp rm(tmp) } } if(testing) print(head(probValues)) } ################################## if(!is.null(submod)) { ## merge the fixed and seq parameter values together allParam <- expandParameters(new_info$loop[parm,,drop = FALSE], submod) allParam <- allParam[complete.cases(allParam),, drop = FALSE] ## collate the predicitons across all the sub-models predicted <- lapply(predicted, function(x, y, wts, lv) { if(!is.factor(x) & is.character(x)) x <- factor(as.character(x), levels = lv) out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE) if(!is.null(wts)) out$weights <- wts out }, y = y[holdoutIndex], wts = wts[holdoutIndex], lv = lev) if(testing) print(head(predicted)) ## same for the class probabilities if(ctrl$classProbs) { for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) } if(ctrl$savePredictions) { tmpPred <- predicted for(modIndex in seq(along = tmpPred)) { tmpPred[[modIndex]]$rowIndex <- holdoutIndex tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex,,drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ## get the performance for this resample for each sub-model thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if(testing) print(head(thisResample)) ## for classification, add the cell counts if(length(lev) > 1) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { if(is.factor(y)) predicted <- factor(as.character(predicted), levels = lev) tmp <- data.frame(pred = predicted, obs = y[holdoutIndex], stringsAsFactors = FALSE) ## Sometimes the code above does not coerce the first ## columnn to be named "pred" so force it names(tmp)[1] <- "pred" if(!is.null(wts)) tmp$weights <- wts[holdoutIndex] if(ctrl$classProbs) tmp <- cbind(tmp, probValues) if(ctrl$savePredictions) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, new_info$loop[parm,,drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ################################## thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) ## if classification, get the confusion matrix if(length(lev) > 1) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample)) thisResample <- cbind(thisResample, new_info$loop[parm,,drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter, FALSE) list(resamples = thisResample, pred = tmpPred) } ## end initial loop over resamples and models } init_result <- c(init_result, adapt_results) rs <- do.call("rbind", init_result[names(init_result) == "resamples"]) current_mods <- get_id(rs, as.character(method$param$parameter)) if(iter > ctrl$adaptive$min) { latest <- do.call("rbind", adapt_results[names(adapt_results) == "resamples"]) latest <- latest[,as.character(method$param$parameter),drop = FALSE] latest <- latest[!duplicated(latest),,drop = FALSE] current_mods <- merge(current_mods, latest) } rs <- merge(rs, current_mods) if(iter == ctrl$adaptive$min) { rs <- filter_on_diff(rs, metric, cutoff = .01, maximize = maximize, verbose = ctrl$verboseIter) } if(ctrl$adaptive$method == "BT") { filtered_mods <- try(bt_eval(rs, metric = metric, maximize = maximize, alpha = ctrl$adaptive$alpha), silent = TRUE) } else { filtered_mods <- try(gls_eval(rs, metric = metric, maximize = maximize, alpha = ctrl$adaptive$alpha), silent = TRUE) } if(class(filtered_mods)[1] == "try-error") { filtered_mods <- current_mods cat("x parameter filtering failed\n") } if(ctrl$verboseIter) { excluded <- unique(rs$model_id)[!(unique(rs$model_id) %in% filtered_mods)] if(length(excluded) > 0) { cat(paste("o", length(excluded), "eliminated;")) } else cat("o no models eliminated;", nrow(current_mods), ifelse(nrow(current_mods) > 1, "remain\n", "remains\n")) } current_mods <- current_mods[current_mods$model_id %in% filtered_mods,,drop = FALSE] if(iter == ctrl$adaptive$min) { last_mods <- current_mods } current_mods$model_id <- NULL num_left <- nrow(current_mods) if(ctrl$verboseIter && length(excluded) > 0) cat(num_left, ifelse(num_left > 1, "remain\n", "remains\n")) if(!is.null(method$loop)) { new_info <- method$loop(current_mods) } else new_info$loop <- current_mods last_iter <- iter if(num_left == 1) break } ## finish up last resamples if(ctrl$adaptive$complete && last_iter < length(ctrl$index)) { printed <- format(new_info$loop, digits = 4) colnames(printed) <- gsub("^\\.", "", colnames(printed)) final_index <- seq(along = resampleIndex)[(last_iter+1):length(ctrl$index)] final_result <- foreach(iter = final_index, .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:% foreach(parm = 1:nrow(new_info$loop), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op% { testing <- FALSE if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm]) loadNamespace("caret") if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter) modelIndex <- resampleIndex[[iter]] holdoutIndex <- ctrl$indexOut[[iter]] if(testing) cat("pre-model\n") if(is.null(info$submodels[[parm]]) || nrow(info$submodels[[parm]]) > 0) { submod <- info$submodels[[parm]] } else submod <- NULL mod <- try( createModel(x = x[modelIndex,,drop = FALSE ], y = y[modelIndex], wts = wts[modelIndex], method = method, tuneValue = new_info$loop[parm,,drop = FALSE], obsLevels = lev, pp = ppp, classProbs = ctrl$classProbs, ...), silent = TRUE) if(class(mod)[1] != "try-error") { predicted <- try( predictionFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod), silent = TRUE) if(class(predicted)[1] == "try-error") { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("predictions failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(predicted), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } } else { wrn <- paste(colnames(printed[parm,,drop = FALSE]), printed[parm,,drop = FALSE], sep = "=", collapse = ", ") wrn <- paste("model fit failed for ", names(resampleIndex)[iter], ": ", wrn, " ", as.character(mod), sep = "") if(ctrl$verboseIter) cat(wrn, "\n") warning(wrn) rm(wrn) ## setup a dummy results with NA values for all predictions nPred <- length(holdoutIndex) if(!is.null(lev)) { predicted <- rep("", nPred) predicted[seq(along = predicted)] <- NA } else { predicted <- rep(NA, nPred) } if(!is.null(submod)) { tmp <- predicted predicted <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = predicted)) predicted[[i]] <- tmp rm(tmp) } } if(testing) print(head(predicted)) if(ctrl$classProbs) { if(class(mod)[1] != "try-error") { probValues <- probFunction(method = method, modelFit = mod$fit, newdata = x[holdoutIndex,, drop = FALSE], preProc = mod$preProc, param = submod) } else { probValues <- as.data.frame(matrix(NA, nrow = nPred, ncol = length(lev))) colnames(probValues) <- lev if(!is.null(submod)) { tmp <- probValues probValues <- vector(mode = "list", length = nrow(info$submodels[[parm]]) + 1) for(i in seq(along = probValues)) probValues[[i]] <- tmp rm(tmp) } } if(testing) print(head(probValues)) } ################################## if(!is.null(submod)) { ## merge the fixed and seq parameter values together allParam <- expandParameters(new_info$loop[parm,,drop = FALSE], new_info$submodels[[parm]]) allParam <- allParam[complete.cases(allParam),, drop = FALSE] ## collate the predicitons across all the sub-models predicted <- lapply(predicted, function(x, y, wts, lv) { if(!is.factor(x) & is.character(x)) x <- factor(as.character(x), levels = lv) out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE) if(!is.null(wts)) out$weights <- wts out }, y = y[holdoutIndex], wts = wts[holdoutIndex], lv = lev) if(testing) print(head(predicted)) ## same for the class probabilities if(ctrl$classProbs) { for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) } if(ctrl$savePredictions) { tmpPred <- predicted for(modIndex in seq(along = tmpPred)) { tmpPred[[modIndex]]$rowIndex <- holdoutIndex tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]], allParam[modIndex,,drop = FALSE], all = TRUE) } tmpPred <- rbind.fill(tmpPred) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ## get the performance for this resample for each sub-model thisResample <- lapply(predicted, ctrl$summaryFunction, lev = lev, model = method) if(testing) print(head(thisResample)) ## for classification, add the cell counts if(length(lev) > 1) { cells <- lapply(predicted, function(x) flatTable(x$pred, x$obs)) for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]]) } thisResample <- do.call("rbind", thisResample) thisResample <- cbind(allParam, thisResample) } else { if(is.factor(y)) predicted <- factor(as.character(predicted), levels = lev) tmp <- data.frame(pred = predicted, obs = y[holdoutIndex], stringsAsFactors = FALSE) ## Sometimes the code above does not coerce the first ## columnn to be named "pred" so force it names(tmp)[1] <- "pred" if(!is.null(wts)) tmp$weights <- wts[holdoutIndex] if(ctrl$classProbs) tmp <- cbind(tmp, probValues) if(ctrl$savePredictions) { tmpPred <- tmp tmpPred$rowIndex <- holdoutIndex tmpPred <- merge(tmpPred, new_info$loop[parm,,drop = FALSE], all = TRUE) tmpPred$Resample <- names(resampleIndex)[iter] } else tmpPred <- NULL ################################## thisResample <- ctrl$summaryFunction(tmp, lev = lev, model = method) ## if classification, get the confusion matrix if(length(lev) > 1) thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs)) thisResample <- as.data.frame(t(thisResample)) thisResample <- cbind(thisResample, new_info$loop[parm,,drop = FALSE]) } thisResample$Resample <- names(resampleIndex)[iter] if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], names(resampleIndex), iter, FALSE) list(resamples = thisResample, pred = tmpPred) } ## end final loop to finish cleanup resamples and models init_result <- c(init_result, final_result) } resamples <- rbind.fill(init_result[names(init_result) == "resamples"]) pred <- if(ctrl$savePredictions) rbind.fill(init_result[names(init_result) == "pred"]) else NULL names(resamples) <- gsub("^\\.", "", names(resamples)) if(any(!complete.cases(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE]))) warning("There were missing values in resampled performance measures.") out <- ddply(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE], ## TODO check this for seq models gsub("^\\.", "", colnames(info$loop)), MeanSD, exclude = gsub("^\\.", "", colnames(info$loop))) num_resamp <- ddply(resamples, gsub("^\\.", "", colnames(info$loop)), function(x) c(.B = nrow(x))) out <- merge(out, num_resamp) list(performance = out, resamples = resamples, predictions = if(ctrl$savePredictions) pred else NULL) } long2wide <- function(x, metric) { x2 <- reshape(x[, c(metric, "model_id", "Resample")], idvar = "Resample", timevar = "model_id", v.names = metric, direction = "wide") colnames(x2) <- gsub(paste(metric, "\\.", sep = ""), "", colnames(x2)) x2[order(x2$Resample), c("Resample", sort(as.character(unique(x$model_id))))] } get_id <- function(x, param) { params <- x[,param,drop=FALSE] params <- params[!duplicated(params),,drop=FALSE] params$model_id <- paste("m", gsub(" ", "0", format(1:nrow(params))), sep = "") rownames(params) <- NULL params } bt_eval <- function(rs, metric, maximize, alpha = 0.05) { se_thresh <- 100 constant <- qnorm(1 - alpha) rs <- rs[order(rs$Resample, rs$model_id),] scores <- ddply(rs, .(Resample), get_scores, maximize = maximize, metric = metric) scores <- ddply(scores, .(player1, player2), function(x) c(win1 = sum(x$win1), win2 = sum(x$win2))) if(length(unique(rs$Resample)) >= 5) scores <- skunked(scores) best_mod <- ddply(rs, .(model_id), function(x, metric) mean(x[, metric]), metric = metric) best_mod <- if(maximize) best_mod$model_id[which.max(best_mod$V1)] else best_mod$model_id[which.min(best_mod$V1)] btModel <- BTm(cbind(win1, win2), player1, player2, data = scores, refcat = best_mod) upperBound <- BTabilities(btModel)[,1] + constant*BTabilities(btModel)[,2] if(any(BTabilities(btModel)[,2] > se_thresh)) { ## These players either are uniformly dominated (='dom') or dominating dom1 <- BTabilities(btModel)[,2] > se_thresh dom2 <- if(maximize) BTabilities(btModel)[,1] <= 0 else BTabilities(btModel)[,1] >= 0 dom <- dom1 & dom2 } else dom <- rep(FALSE, length(upperBound)) bound <- upperBound >= 0 keepers <- names(upperBound)[bound & !dom] unique(c(best_mod, keepers)) } get_scores <- function(x, maximize = NULL, metric = NULL) { delta <- outer(x[,metric], x[,metric], "-") tied <- ifelse(delta == 0, 1, 0)*.5 diag(tied) <- 0 binary <- if(maximize) ifelse(delta > 0, 1, 0) else ifelse(delta > 0, 0, 1) binary <- binary + tied diag(binary) <- 0 rownames(binary) <- colnames(binary) <- x$model_id countsToBinomial(as.table(binary)) } skunked <- function(scores, verbose = TRUE) { p1 <- ddply(scores, .(player1), function(x) sum(x$win1)) p2 <- ddply(scores, .(player2), function(x) sum(x$win2)) names(p1)[1] <- names(p2)[1] <- "playa" by_player <- ddply(rbind(p1, p2), .(playa), function(x) c(wins = sum(x$V1))) if(any(by_player$wins < 1)) { skunked <- as.character(by_player$playa[by_player$wins < 1]) if(verbose) cat("o", sum(by_player$wins < 1), ifelse(sum(by_player$wins < 1) > 1, "models were", "model was"), "skunked\n") scores <- subset(scores, !(player1 %in% skunked)) scores <- subset(scores, !(player2 %in% skunked)) levs <- sort(unique(c(as.character(scores$player1), as.character(scores$player2)))) scores$player1 <- factor(as.character(scores$player1), levels = levs) scores$player2 <- factor(as.character(scores$player2), levels = levs) } scores } gls_eval <- function(x, metric, maximize, alpha = 0.05) { means <- ddply(x[, c(metric, "model_id")], .(model_id), function(x, met) c(mean = mean(x[, met])), met = metric) means <- if(maximize) means[order(-means$mean),] else means[order(means$mean),] levs <- as.character(means$model_id) bl <- levs[1] bldat <- subset(x, model_id == bl)[, c("Resample", metric)] colnames(bldat)[2] <- ".baseline" x2 <- merge(bldat, x[, c("Resample", "model_id", metric)]) x2$value <- if(maximize) x2$.baseline - x2[, metric] else x2[, metric] - x2$.baseline x2 <- subset(x2, model_id != bl) x2$model_id <- factor(x2$model_id, levels = levs[-1]) if(length(levs) > 2) { gls_fit <- try(gls(value ~ model_id - 1, data = x2, corCompSymm(form = ~ 1 | Resample), na.action = na.omit), silent = TRUE) if(class(gls_fit)[1] != "try-error") { lvl2 <- 1 - (2*alpha) ## convert to one sided ci <- intervals(gls_fit, which = "coef", level = lvl2)$coef[,1] keepers <- ci <= 0 if(any(is.na(keepers))) keepers[is.na(keepers)] <- TRUE keepers <- names(ci)[keepers] keepers <- gsub("model_id", "", keepers) } else keepers <- levs } else { ttest <- t.test(x2$value, alternative = "greater")$p.value keepers <- if(!is.na(ttest) && ttest >= alpha) levs[-1] else NULL } unique(c(bl, keepers)) } seq_eval <- function(x, metric, maximize, alpha = 0.05) { means <- ddply(x[, c(metric, "model_id")], .(model_id), function(x, met) c(mean = mean(x[, met])), met = metric) means <- if(maximize) means[order(-means$mean),] else means[order(means$mean),] levs <- as.character(means$model_id) bl <- levs[1] bldat <- subset(x, model_id == bl)[, c("Resample", metric)] colnames(bldat)[2] <- ".baseline" x2 <- merge(bldat, x[, c("Resample", "model_id", metric)]) x2$value <- if(maximize) x2$.baseline - x2[, metric] else x2[, metric] - x2$.baseline x2 <- subset(x2, model_id != bl) x2$model_id <- factor(x2$model_id, levels = levs[-1]) if(length(levs) > 2) { fit <- lm(value ~ . - 1, data = x2[, c("model_id", "value")]) fit <- summary(fit) pvals <- pt(coef(fit)[, 3], fit$df[2], lower.tail = FALSE) names(pvals) <- gsub("model_id", "", names(pvals)) keepers <- pvals >= alpha if(any(is.na(keepers))) keepers[is.na(keepers)] <- TRUE keepers <- names(pvals)[keepers] keepers <- gsub("model_id", "", keepers) } else { ttest <- t.test(x2$value, alternative = "greater")$p.value keepers <- if(!is.na(ttest) && ttest >= alpha) levs[-1] else NULL } unique(c(bl, keepers)) } retrospective <- function(x, B = 5, method = "BT", alpha = 0.05) { rs <- x$resample if(!is.factor(rs$Resample)) rs$Resample <- factor(rs$Resample) rs <- subset(rs, as.numeric(Resample) <= B) current_mods <- get_id(rs, as.character(x$modelInfo$param$parameter)) # current_mods <- merge(current_mods, new_loop) rs <- merge(rs, current_mods) if(method == "BT") { filtered_mods <- try(bt_eval(rs, metric = x$metric, maximize = x$maximize, alpha = alpha), silent = TRUE) } else { filtered_mods <- try(gls_eval(rs, metric = x$metric, maximize = x$maximize, alpha = alpha), silent = TRUE) } list(models = filtered_mods, mods = subset(current_mods, model_id %in% filtered_mods), long = rs, wide = long2wide(rs, x$metric)) } cccmat <- function(dat) { p <- ncol(dat) out <- matrix(1, ncol = p, nrow = p) for(i in 1:p) { for(j in i:p) { if(i > j) { tmp <- ccc(dat[,i], dat[,j]) out[i, j] <- out[j, i] <- tmp } } } out[lower.tri(out)] <- out[upper.tri(out)] colnames(out) <- rownames(out) <- colnames(dat) out } ccc <- function(x, y) { covm <- cov(cbind(x, y), use = "pairwise.complete.obs") mnx <- mean(x, na.rm = TRUE) mny <- mean(y, na.rm = TRUE) 2*covm[1,2]/(covm[1,1] + covm[2,2] + (mnx - mny)^2) } diffmat <- function(dat) { p <- ncol(dat) out <- matrix(NA, ncol = p, nrow = p) for(i in 1:p) { for(j in i:p) { if(i < j) { x <- dat[,i] - dat[,j] tmpm <- abs(mean(x, na.rm = TRUE)) tmps <- sd(x, na.rm = TRUE) out[i, j] <- out[j, i] <- if(tmps < .Machine$double.eps^0.5) 0 else tmpm/tmps } } } out[lower.tri(out)] <- out[upper.tri(out)] colnames(out) <- rownames(out) <- colnames(dat) out } filter_on_diff <- function(dat, metric, cutoff = 0.1, maximize = TRUE, verbose = FALSE) { x <- long2wide(x = dat, metric = metric) mns <- colMeans(x[, -1]) if(!maximize) mns <- -mns x <- diffmat(x[, -1]) varnum <- dim(x)[1] originalOrder <- 1:varnum averageCorr <- function(x) mean(x, na.rm = TRUE) tmp <- x diag(tmp) <- NA maxAbsCorOrder <- order(apply(tmp, 2, averageCorr), decreasing = TRUE) x <- x[maxAbsCorOrder, maxAbsCorOrder] mns <- mns[maxAbsCorOrder] newOrder <- originalOrder[maxAbsCorOrder] deletecol <- 0 for (i in 1:(varnum - 1)) { for (j in (i + 1):varnum) { if (!any(i == deletecol) & !any(j == deletecol)) { if (abs(x[i, j]) < cutoff) { if (mns[i] < mns[j]) { deletecol <- unique(c(deletecol, i)) } else { deletecol <- unique(c(deletecol, j)) } } } } } deletecol <- deletecol[deletecol != 0] if(length(deletecol) > 0) { dumped <- colnames(x)[newOrder[deletecol]] if (verbose) cat(paste("o", length(deletecol), ifelse(length(deletecol) > 1, "models were", "model was"), "eliminated due to linear dependencies\n")) dat <- subset(dat, !(model_id %in% dumped)) } dat } filter_on_corr <- function(dat, metric, cutoff, verbose = FALSE) { x <- long2wide(x = dat, metric = metric) # x <- cor(x[, -1], use = "pairwise.complete.obs") x <- cccmat(x[, -1]) varnum <- dim(x)[1] if (!isTRUE(all.equal(x, t(x)))) stop("correlation matrix is not symmetric") if (varnum == 1) stop("only one variable given") x <- abs(x) originalOrder <- 1:varnum averageCorr <- function(x) mean(x, na.rm = TRUE) tmp <- x diag(tmp) <- NA maxAbsCorOrder <- order(apply(tmp, 2, averageCorr), decreasing = TRUE) x <- x[maxAbsCorOrder, maxAbsCorOrder] newOrder <- originalOrder[maxAbsCorOrder] deletecol <- 0 for (i in 1:(varnum - 1)) { for (j in (i + 1):varnum) { if (!any(i == deletecol) & !any(j == deletecol)) { if (abs(x[i, j]) > cutoff) { if (mean(x[i, -i]) > mean(x[-j, j])) { deletecol <- unique(c(deletecol, i)) } else { deletecol <- unique(c(deletecol, j)) } } } } } deletecol <- deletecol[deletecol != 0] if(length(deletecol) > 0) { dumped <- colnames(x)[newOrder[deletecol]] if (verbose) cat(paste("o", length(deletecol), ifelse(length(deletecol) > 1, "models were", "model was"), "eliminated due to linear dependencies\n")) dat <- subset(dat, !(model_id %in% dumped)) } dat } caret/R/ggplot.R0000644000175000017500000001341512453550064013654 0ustar rbalintrbalintggplot.train <- function(data = NULL, metric = data$metric[1], plotType = "scatter", output = "layered", nameInStrip = FALSE, ...) { if(!(output %in% c("data", "layered", "ggplot"))) stop("'outout' should be either 'data', 'ggplot' or 'layered'") params <- data$modelInfo$parameters$parameter paramData <- data$modelInfo$parameters if(grepl("adapt", data$control$method)) warning("When using adaptive resampling, this plot may not accurately capture the relationship between the tuning parameters and model performance.") plotIt <- "yes" if(all(params == "parameter")) { plotIt <- "There are no tuning parameters for this model." } else { dat <- data$results ## Some exceptions for pretty printing if(data$method == "nb") dat$usekernel <- factor(ifelse(dat$usekernel, "Nonparametric", "Gaussian")) if(data$method == "gam") dat$select <- factor(ifelse(dat$select, "Feature Selection", "No Feature Selection")) if(data$method == "qrnn") dat$bag <- factor(ifelse(dat$bag, "Bagging", "No Bagging")) if(data$method == "C5.0") dat$winnow <- factor(ifelse(dat$winnow, "Winnowing", "No Winnowing")) if(data$method == "M5") dat$rules <- factor(ifelse(dat$rules == "Yes", "Rules", "Trees")) ## Check to see which tuning parameters were varied paramValues <- apply(dat[,params,drop = FALSE], 2, function(x) length(unique(x))) if(any(paramValues > 1)) { params <- names(paramValues)[paramValues > 1] paramData <- subset(paramData, parameter %in% params) } else plotIt <- "There are no tuning parameters with more than 1 value." } if(plotIt == "yes") { p <- length(params) dat <- dat[, c(metric, params)] resampText <- resampName(data, FALSE) resampText <- paste(metric, resampText) } else stop(plotIt) p <- ncol(dat) - 1 if(p > 1) { numUnique <- unlist(lapply(dat[, -1], function(x) length(unique(x)))) numUnique <- sort(numUnique, decreasing = TRUE) dat <- dat[, c(metric, names(numUnique))] } if(output == "data") return(dat) if(plotType == "scatter") { dnm <- names(dat) if(p > 1 && is.numeric(dat[, 3])) dat[, 3] <- factor(format(dat[, 3])) if(p > 2 & nameInStrip) { strip_vars <- names(dat)[-(1:3)] strip_lab <- as.character(subset(data$modelInfo$parameters, parameter %in% strip_vars)$label) for(i in seq_along(strip_vars)) dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": ")) } out <- ggplot(dat, aes_string(x = dnm[2], y = dnm[1])) out <- out + ylab(resampText) out <- out + xlab(paramData$label[paramData$parameter == names(dat)[2]]) if(output == "layered") { if(p >= 2) { leg_name <- paramData$label[paramData$parameter == names(dat)[3]] out <- out + geom_point(aes_string(color = dnm[3], shape = dnm[3])) out <- out + geom_line(aes_string(color = dnm[3], shape = dnm[3])) out <- out + scale_colour_discrete(name = leg_name) + scale_shape_discrete(name = leg_name) } else out <- out + geom_point() + geom_line() ## TODO change legend (to blank when needed) if(p == 3) { out <- out + facet_wrap(as.formula(paste("~", names(dat)[4]))) } if(p == 4) { out <- out + facet_grid(as.formula(paste(names(dat)[4], "~", names(dat)[5]))) } if(p > 4) stop("The function can only handle <= 4 tuning parameters for scatter plots. Use output = 'ggplot' to create your own") } } if(plotType == "level") { if(p == 1) stop("Two tuning parameters are required for a level plot") dnm <- names(dat) if(is.numeric(dat[,2])) dat[,2] <- factor(format(dat[,2])) if(is.numeric(dat[,3])) dat[,3] <- factor(format(dat[,3])) if(p > 2 & nameInStrip) { strip_vars <- names(dat)[-(1:3)] strip_lab <- as.character(subset(data$modelInfo$parameters, parameter %in% strip_vars)$label) for(i in seq_along(strip_vars)) dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": ")) } ## TODO: use factor(format(x)) to make a solid block of colors? out <- ggplot(dat, aes_string(x = dnm[2], y = dnm[3], fill = metric)) out <- out + ylab(paramData$label[paramData$parameter == names(dat)[3]]) out <- out + xlab(paramData$label[paramData$parameter == names(dat)[2]]) if(output == "layered") { out <- out + geom_tile() if(p == 3) { out <- out + facet_wrap(as.formula(paste("~", names(dat)[4]))) } if(p == 4) { out <- out + facet_wrap(as.formula(paste("~", names(dat)[5]))) } if(p == 5) { out <- out + facet_grid(as.formula(paste(names(dat)[5], "~", names(dat)[6]))) } if(p > 6) stop("The function can only handle <= 6 tuning parameters for level plots. Use output = 'ggplot' to create your own") } } out } ggplot.rfe <- function(data = NULL, metric = data$metric[1], output = "layered", ...) { if(!(output %in% c("data", "layered", "ggplot"))) stop("'outout' should be either 'data', 'ggplot' or 'layered'") resampText <- resampName(data, FALSE) resampText <- paste(metric, resampText) if(output == "data") return(data$results) notBest <- subset(data$results, Variables != data$bestSubset) best <- subset(data$results, Variables == data$bestSubset) out <- ggplot(data$results, aes_string(x = "Variables", y = metric)) if(output == "ggplot") return(out) out <- out + geom_line() out <- out + ylab(resampText) out <- out + geom_point(data = notBest, aes_string(x = "Variables", y = metric)) out <- out + geom_point(data=best, aes_string(x = "Variables", y = metric), size = 3, colour="blue") out } caret/R/predict.PLS.R0000644000175000017500000000322212453550064014442 0ustar rbalintrbalint predict.PLS <- function(object, newdata, ncomp = NULL, type = ifelse(object$isRegression, "response", "class"), ...) { if(is.null(ncomp) & !is.null(object$bestIter$.ncomp)) ncomp <- object$bestIter$.ncomp if(is.null(ncomp)) stop("the number of components must be given") # adapted from the pls package if(object$isRegression & c(type %in% c("class", "prob"))) stop("cannot get a class estimate if the original y was not a factor") if(!(type %in% c("response", "class", "prob"))) stop("type must be either response, class or prob") if(missing(newdata)) newdata <- object$x if(!is.matrix(newdata)) newdata <- as.matrix(newdata) # from coef.mvr in pls package B <- object$coefficients[, , 1:ncomp, drop = FALSE] dB <- dim(B) dB[1] <- dB[1] + 1 dnB <- dimnames(B) dnB[[1]] <- c("(Intercept)", dnB[[1]]) BInt <- array(dim = dB, dimnames = dnB) BInt[-1, , ] <- B for (i in seq(along = 1:ncomp)) BInt[1, , i] <- object$Ymeans - object$Xmeans %*% B[, , i] B <- BInt # stop # from predict.mvr in pls package dPred <- dim(B) dPred[1] <- dim(newdata)[1] dnPred <- dimnames(B) dnPred[1] <- dimnames(newdata)[1] pred <- array(dim = dPred, dimnames = dnPred) predY <- sweep(newdata %*% B[-1, , ncomp], 2, B[1, , ncomp], "+") # stop out <- switch( type, response = predY, class = { classNum <- apply(predY, 1, which.max) factor(object$yLevels[classNum], levels = object$yLevels) }, # use softmax technique here prob = t(apply(predY, 1, function(data) exp(data)/sum(exp(data))))) out } caret/R/negPredValue.R0000644000175000017500000000374012453550064014741 0ustar rbalintrbalintnegPredValue <- function(data, ...){ UseMethod("negPredValue") } "negPredValue.default" <- function(data, reference, negative = levels(reference)[2], prevalence = NULL, ...) { if(!is.factor(reference) | !is.factor(data)) stop("input data must be a factor") if(length(unique(c(levels(reference), levels(data)))) != 2) stop("input data must have the same two levels") lvls <- levels(data) if(is.null(prevalence)) prevalence <- mean(reference == lvls[lvls != negative]) sens <- sensitivity(data, reference, lvls[lvls != negative]) spec <- specificity(data, reference, negative) (spec * (1-prevalence))/(((1-sens)*prevalence) + ((spec)*(1-prevalence))) } "negPredValue.table" <- function(data, negative = rownames(data)[-1], prevalence = NULL, ...) { ## "truth" in columns, predictions in rows if(!all.equal(nrow(data), ncol(data))) stop("the table must have nrow = ncol") if(!all.equal(rownames(data), colnames(data))) stop("the table must the same groups in the same order") if(nrow(data) > 2) { tmp <- data data <- matrix(NA, 2, 2) colnames(data) <- rownames(data) <- c("pos", "neg") negCol <- which(colnames(tmp) %in% negative) posCol <- which(!(colnames(tmp) %in% negative)) data[1, 1] <- sum(tmp[posCol, posCol]) data[1, 2] <- sum(tmp[posCol, negCol]) data[2, 1] <- sum(tmp[negCol, posCol]) data[2, 2] <- sum(tmp[negCol, negCol]) data <- as.table(data) negative <- "neg" rm(tmp) } positive <- colnames(data)[colnames(data) != negative] if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data) sens <- sensitivity(data, positive) spec <- specificity(data, negative) (spec * (1-prevalence))/(((1-sens)*prevalence) + ((spec)*(1-prevalence))) } "negPredValue.matrix" <- function(data, negative = rownames(data)[-1], prevalence = NULL, ...) { data <- as.table(data) negPredValue.table(data, prevalence = prevalence) } caret/R/createModel.R0000644000175000017500000000236312453550064014604 0ustar rbalintrbalint "createModel" <-function(x, y, wts, method, tuneValue, obsLevels, pp = NULL, last = FALSE, classProbs, ...) { if(!is.null(pp$options)) { pp$method <- pp$options pp$options <- NULL if("ica" %in% pp$method) pp$n.comp <- pp$ICAcomp pp$ICAcomp <- NULL pp$x <- x ppObj <- do.call("preProcess", pp) ppObj$call <- "scrubed" x <- predict(ppObj, x) rm(pp) } else ppObj <- NULL modelFit <- method$fit(x = x, y = y, wts = wts, param = tuneValue, lev = obsLevels, last = last, classProbs = classProbs, ...) ## for models using S4 classes, you can't easily append data, so ## exclude these and we'll use other methods to get this information if(is.null(method$label)) method$label <- "" if(!isS4(modelFit) & !(method$label %in% c("Ensemble Partial Least Squares Regression", "Ensemble Partial Least Squares Regression with Feature Selection"))) { modelFit$xNames <- colnames(x) modelFit$problemType <- if(is.factor(y)) "Classification" else "Regression" modelFit$tuneValue <- tuneValue modelFit$obsLevels <- obsLevels } list(fit = modelFit, preProc = ppObj) }caret/R/avNNet.R0000644000175000017500000001331012520454326013544 0ustar rbalintrbalint avNNet <- function (x, ...) UseMethod("avNNet") ## this is a near copy of nnet.formula avNNet.formula <- function (formula, data, weights, ..., repeats = 5, bag= FALSE, allowParallel = TRUE, subset, na.action, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) ## bag <- m$bag ## repeats <- m$repeats m$... <- m$contrasts <- m$bag <- m$repeats <- m$allowParallel <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] w <- model.weights(m) if (length(w) == 0) w <- rep(1, nrow(x)) y <- model.response(m) res <- avNNet.default(x, y, weights = w, repeats = repeats, bag = bag, allowParallel = allowParallel, ...) res$terms <- Terms res$coefnames <- colnames(x) res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) class(res) <- c("avNNet.formula", "avNNet") res } avNNet.default <- function(x, y, repeats = 5, bag = FALSE, allowParallel = TRUE, ...) { requireNamespaceQuietStop("nnet") ## check for factors ## this is from nnet.formula ind <- seq(along = y) if(is.factor(y)) { classLev <- levels(y) y <- class2ind(y) } else classLev <- NULL if(is.matrix(y)) classLev <- colnames(y) theDots <- list(...) ## to avoid a "no visible binding for global variable 'i'" warning: i <- NULL `%op%` <- if(allowParallel) `%dopar%` else `%do%` mods <- foreach(i = 1:repeats, .verbose = FALSE, .packages = "caret", .errorhandling = "stop") %op% { if(any(names(theDots) == "trace")) { if(theDots$trace) cat("\nFitting Repeat", i, "\n\n") } else cat("Fitting Repeat", i, "\n\n") if(bag) ind <- sample(1:nrow(x)) thisMod <- if(is.null(classLev)) nnet::nnet(x[ind,,drop = FALSE], y[ind], ...) else nnet::nnet(x[ind,,drop = FALSE], y[ind,], ...) thisMod$lev <- classLev thisMod } ## return results out <- list(model = mods, repeats = repeats, bag = bag, names = colnames(x)) class(out) <- "avNNet" out } print.avNNet <- function (x, ...) { cat("Model Averaged Neural Network with", x$repeats, "Repeats", ifelse(x$bag, "and Bagging", ""), "\n\n") print(x$model[[1]]) cat("\n") invisible(x) } predict.avNNet <- function(object, newdata, type = c("raw", "class", "prob"), ...) { loadNamespace("nnet") if (!inherits(object, "avNNet")) stop("object not of class \"avNNet\"") if (missing(newdata)) { if(is.null(object$model[[1]]$lev)) { out <- lapply(object$model, fitted.values) out <- do.call("cbind", out) return(apply(out, 1, mean)) } else { for(i in 1:object$repeats) { rawTmp <- fitted.values(object$model[[i]]) rawTmp <- t(apply(rawTmp, 1, function(x) exp(x)/sum(exp(x)))) scores <- if(i == 1) rawTmp else scores + rawTmp } scores <- scores/object$repeats classes <- colnames(scores)[apply(scores, 1, which.max)] classes <- factor(as.character(classes), levels = object$model[[1]]$lev) if(type[1]== "raw") out <- scores if(type[1]== "class") out <- (classes) if(type[1]== "prob") out <- t(apply(scores, 1, function(x) x/sum(x))) } } else { if (inherits(object, "avNNet.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.omit, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) x <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] } else { if (is.null(dim(newdata))) dim(newdata) <- c(1, length(newdata)) x <- as.matrix(newdata) if (any(is.na(x))) stop("missing values in 'x'") keep <- 1:nrow(x) rn <- rownames(x) } if(!is.null(object$names)) x <- x[, object$names, drop = FALSE] if(is.null(object$model[[1]]$lev)) { out <- lapply(object$model, predict, newdata = x) out <- do.call("cbind", out) return(apply(out, 1, mean)) } else { for(i in 1:object$repeats) { scores <- if(i == 1) predict(object$model[[i]], newdata = x) else scores + predict(object$model[[i]], newdata = x) } scores <- scores/object$repeats classes <- colnames(scores)[apply(scores, 1, which.max)] classes <- factor(as.character(classes), levels = object$model[[1]]$lev) if(type[1]== "raw") out <- scores if(type[1]== "class") out <- (classes) if(type[1]== "prob") out <- t(apply(scores, 1, function(x) x/sum(x))) } } out } caret/R/predictionFunction.R0000644000175000017500000000053212453550064016222 0ustar rbalintrbalintpredictionFunction <- function(method, modelFit, newdata, preProc = NULL, param = NULL) { if(!is.null(preProc)) newdata <- predict(preProc, newdata) out <- method$predict(modelFit = modelFit, newdata = newdata, submodels = param) ## TODO convert to character with classification out } caret/R/plot.train.R0000644000175000017500000002052412453550064014451 0ustar rbalintrbalint"plot.train" <- function(x, plotType = "scatter", metric = x$metric[1], digits = getOption("digits") - 3, xTrans = NULL, nameInStrip = FALSE, ...) { ## Error trap if(!(plotType %in% c("level", "scatter", "line"))) stop("plotType must be either level, scatter or line") cutpoints <- c(0, 1.9, 2.9, 3.9, Inf) ## define some functions prettyVal <- function(u, dig, Name = NULL) { if(is.numeric(u)) { if(!is.null(Name)) u <- paste(gsub(".", " ", Name, fixed = TRUE), ": ", format(u, digits = dig), sep = "") return(factor(u)) } else return(if(!is.factor(u)) as.factor(u) else u) } ## Get tuning parameter info params <- x$modelInfo$parameters$parameter if(grepl("adapt", x$control$method)) warning("When using adaptive resampling, this plot may not accurately capture the relationship between the tuning parameters and model performance.") plotIt <- "yes" if(all(params == "parameter")) { plotIt <- "There are no tuning parameters for this model." } else { dat <- x$results ## Some exceptions for pretty printing if(x$method == "nb") dat$usekernel <- factor(ifelse(dat$usekernel, "Nonparametric", "Gaussian")) if(x$method == "gam") dat$select <- factor(ifelse(dat$select, "Feature Selection", "No Feature Selection")) if(x$method == "qrnn") dat$bag <- factor(ifelse(dat$bag, "Bagging", "No Bagging")) if(x$method == "C5.0") dat$winnow <- factor(ifelse(dat$winnow, "Winnowing", "No Winnowing")) ## if(x$method %in% c("M5Rules", "M5", "PART")) dat$pruned <- factor(ifelse(dat$pruned == "Yes", "Pruned", "Unpruned")) ## if(x$method %in% c("M5Rules", "M5")) dat$smoothed <- factor(ifelse(dat$smoothed == "Yes", "Smoothed", "Unsmoothed")) if(x$method == "M5") dat$rules <- factor(ifelse(dat$rules == "Yes", "Rules", "Trees")) ## if(x$method == "vbmpRadial") dat$estimateTheta <- factor(ifelse(dat$estimateTheta == "Yes", "Estimate Theta", "Do Not Estimate Theta")) ## Check to see which tuning parameters were varied paramValues <- apply(dat[,params,drop = FALSE], 2, function(x) length(unique(x))) ##paramValues <- paramValues[order(paramValues)] if(any(paramValues > 1)) { params <- names(paramValues)[paramValues > 1] } else plotIt <- "There are no tuning parameters with more than 1 value." } if(plotIt == "yes") { p <- length(params) dat <- dat[, c(metric, params)] if(p > 1) { numUnique <- unlist(lapply(dat[, -1], function(x) length(unique(x)))) numUnique <- sort(numUnique, decreasing = TRUE) dat <- dat[, c(metric, names(numUnique))] params <- names(numUnique) } ## The conveintion is that the first parameter (in ## position #2 of dat) is plotted on the x-axis, ## the second parameter is the grouping variable ## and the rest are conditioning variables if(!is.null(xTrans) & plotType == "scatter") dat[,2] <- xTrans(dat[,2]) ## We need to pretty-up some of the values of grouping ## or conditioning variables resampText <- resampName(x, FALSE) if(plotType %in% c("line", "scatter")) { if(plotType == "scatter") { if(p >= 2) for(i in 3:ncol(dat)) dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL) } else { for(i in 2:ncol(dat)) dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL) } for(i in 2:ncol(dat)) if(is.logical(dat[,i])) dat[,i] <- factor(dat[,i]) if(p > 2 & nameInStrip) { strip_vars <- params[-(1:2)] strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label for(i in seq_along(strip_vars)) dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": ")) } ## make formula form <- if(p <= 2) { as.formula( paste(metric, "~", params[1], sep = "")) } else as.formula(paste(metric, "~", params[1], "|", paste(params[-(1:2)], collapse = "*"), sep = "")) defaultArgs <- list(x = form, data = dat, groups = if(p > 1) dat[,params[2]] else NULL) if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...)) lNames <- names(defaultArgs) if(!("ylab" %in% lNames)) defaultArgs$ylab <- paste(metric, resampText) if(!("type" %in% lNames) & plotType == "scatter") defaultArgs$type <- c("g", "o") if(!("type" %in% lNames) & plotType == "line") defaultArgs$type <- c("g", "o") if(p > 1) { ## I apologize in advance for the following 3 line kludge. groupCols <- 4 if(length(unique(dat[,3])) < 4) groupCols <- length(unique(dat[,3])) if(length(unique(dat[,3])) %in% 5:6) groupCols <- 3 groupCols <- as.numeric( cut(length(unique(dat[,3])), cutpoints, include.lowest = TRUE)) if(!(any(c("key", "auto.key") %in% lNames))) defaultArgs$auto.key <- list(columns = groupCols, lines = TRUE, title = as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]], cex.title = 1) } if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]] if(plotType == "scatter") { out <- do.call("xyplot", defaultArgs) } else { ## line plot ######################### out <- do.call("stripplot", defaultArgs) } } if(plotType == "level") { if(p == 1) stop("There must be at least 2 tuning parameters with multiple values") for(i in 2:ncol(dat)) dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL) if(p > 2 & nameInStrip) { strip_vars <- params[-(1:2)] strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label for(i in seq_along(strip_vars)) dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": ")) } ## make formula form <- if(p <= 2) { as.formula(paste(metric, "~", params[1], "*", params[2], sep = "")) } else as.formula(paste(metric, "~", params[1], "*", params[2], "|", paste(params[-(1:2)], collapse = "*"), sep = "")) defaultArgs <- list(x = form, data = dat) if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...)) lNames <- names(defaultArgs) if(!("sub" %in% lNames)) defaultArgs$sub <- paste(metric, resampText) if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]] if(!("ylab" %in% lNames)) defaultArgs$ylab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]] out <- do.call("levelplot", defaultArgs) } } else stop(plotIt) out } caret/R/common_code.R0000644000175000017500000000513112453550064014636 0ustar rbalintrbalintget_fitness_differences <- function(pnames, subsets, fitness, label) { signs <- lapply(subsets, index2vec, vars = length(pnames)) signs <- do.call("rbind", signs) colnames(signs) <- pnames nzv <- apply(signs, 2, function(x) min(table(factor(x, levels = paste(0:1)))) > 1) if(all(!nzv)) return(NULL) signs <- signs[, nzv, drop = FALSE] if(!is.matrix(fitness)) fitness <- as.matrix(fitness) snr <- function(x, y) { apply(y, 2, function(outcome, ind) t.test(outcome[ind == 1], outcome[ind == 0])$statistic, ind = x) } melt(apply(signs, 2, snr, y = fitness)) } process_diffs <- function(x, pnames) { x <- x[!is.null(x)] x <- do.call("rbind", x) mean_diffs <- ddply(x, c("Var1", "Var2"), function(x) c(mean = mean(x$value, na.rm = TRUE))) mean_diffs <- reshape(mean_diffs, direction = "wide", v.names = "mean", idvar = "Var2", timevar = "Var1") names(mean_diffs) <- gsub("mean\\.", "", names(mean_diffs)) names(mean_diffs)[1] <- "Variable" included <- pnames %in% as.character(mean_diffs$Variable) if(any(!included)) { extras <- data.frame(Variable = pnames[!included]) for(i in 2:ncol(mean_diffs)) extras[, names(mean_diffs)[i]] <- NA mean_diffs <- rbind(mean_diffs, extras) } mean_diffs } same_args <- function(a, b) { if(length(a) != length(b)) return(FALSE) if(!isTRUE(all.equal(sort(a), sort(b)))) return(FALSE) TRUE } getOper <- function(x) if(x) `%dopar%` else `%do%` jack_sim <- function(a, b) { if(is.matrix(a) && nrow(a) > 1) a <- a[1,,drop=FALSE] if(is.matrix(a) && nrow(b) > 1) b <- b[1,,drop=FALSE] sum(a ==1 & b ==1)/(sum(a == 1 & b == 0)+sum(a == 0 & b == 1)+sum(a ==1 & b ==1))*100 } index2vec <- function(x, vars, sign = FALSE) { bin <- rep(0, vars) bin[x] <- 1 if(sign) bin <- ifelse(bin == 0, -1, 1) bin } change_text <- function(old, new, p, show_diff = TRUE) { a <- index2vec(new, p) b <- index2vec(old, p) size_diff <- length(new) - length(old) if(show_diff) { if(abs(size_diff) >= 0) { num_text <- if(size_diff >= 0) paste0(length(old), "+", size_diff) else paste0(length(old), size_diff) } else num_text <- paste(length(old)) } else { old_len <- length(old) new_len <- length(new) num_text <- paste0(format(1:p)[old_len], "->", format(1:p)[new_len]) } sim <- sprintf("%.1f", jack_sim(a, b)) num_text <- paste0(" (", num_text, ", ", sim, "%)") num_text } predictors.gafs <- function(x, ...) { x$best_vars } predictors.safs <- function(x, ...) { x$best_vars } caret/R/knn3Train.R0000644000175000017500000000277412453550064014235 0ustar rbalintrbalint knn3Train <- function(train, test, cl, k=1, l=0, prob = TRUE, use.all=TRUE) { train <- as.matrix(train) if(is.null(dim(test))) dim(test) <- c(1, length(test)) test <- as.matrix(test) if(any(is.na(train)) || any(is.na(test)) || any(is.na(cl))) stop("no missing values are allowed") p <- ncol(train) ntr <- nrow(train) if(length(cl) != ntr) stop("'train' and 'class' have different lengths") if(ntr < k) { warning(gettextf("k = %d exceeds number %d of patterns", k, ntr), domain = NA) k <- ntr } if (k < 1) stop(gettextf("k = %d must be at least 1", k), domain = NA) nte <- nrow(test) if(ncol(test) != p) stop("dims of 'test' and 'train differ") clf <- as.factor(cl) nc <- max(unclass(clf)) Z <- .C("knn3", as.integer(k), as.integer(l), as.integer(ntr), as.integer(nte), as.integer(p), as.double(train), as.integer(unclass(clf)), as.double(test), integer(nc+1), as.integer(nc), as.integer(FALSE), as.integer(use.all), all_vote=double(as.integer(nte*nc)) ) classProbs <- matrix(Z$all_vote,nrow=nte,ncol=nc,byrow=TRUE) colnames(classProbs)<-sort(unique(clf)) bestClass <- function(x) { out <- which(x == max(x)) if(length(out) > 1) out <- sample(out, 1) out } res <- colnames(classProbs)[apply(classProbs, 1, bestClass)] votes <- apply(classProbs * k, 1, max) inDoubt <- (votes < l) if(any(inDoubt)) res[inDoubt] <- NA if (prob) attr(res, "prob") <- classProbs res } caret/R/knnreg.R0000644000175000017500000000760112516214271013641 0ustar rbalintrbalint"knnreg" <- function(x, ...) UseMethod("knnreg") knnreg.default <- function(x, ...) { if(!any(class(x) %in% "formula")) stop("knnreg only implemented for formula objects") } knnreg.formula <- function (formula, data, subset, na.action, k = 5, ...) { if (missing(formula) || (length(formula) != 3) || (length(attr(terms(formula[-2], data = data), "term.labels")) < 1) || (length(attr(terms(formula[-3], data = data), "term.labels")) != 1)) stop("formula missing or incorrect") m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data, parent.frame()))) m$data <- as.data.frame(data) m[[1]] <- as.name("model.frame") m$... <- NULL m$k <- NULL m <- eval(m, parent.frame()) Terms <- attr(m, "terms") y <- model.extract(m, "response") x <- model.matrix(Terms, m) xvars <- as.character(attr(Terms, "variables"))[-1] if ((yvar <- attr(Terms, "response")) > 0) xvars <- xvars[-yvar] xlev <- if (length(xvars) > 0) { xlev <- lapply(m[xvars], levels) xlev[!sapply(xlev, is.null)] } xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] RET <- list(learn = list(y = y, X = x)) RET$k <- k RET$terms <- Terms RET$contrasts <- attr(x, "contrasts") RET$xlevels <- xlev RET$theDots <- list(...) attr(RET, "na.message") <- attr(m, "na.message") if (!is.null(attr(m, "na.action"))) RET$na.action <- attr(m, "na.action") class(RET) <- "knn3" RET } knnreg.matrix <- function(x, y, k = 5, ...) { if(!is.matrix(x)) x <- as.matrix(x) if(!is.numeric(y)) stop("y must be numeric") RET <- list(learn = list(y = y, X = x)) RET$k <- k RET$terms <- NULL RET$contrasts <- NULL RET$theDots <- list(...) class(RET) <- "knnreg" RET } knnreg.data.frame <- function(x, y, k = 5, ...) { x <- as.data.frame(x) if(!is.numeric(y)) stop("y must be numeric") RET <- list(learn = list(y = y, X = x)) RET$k <- k RET$terms <- NULL RET$contrasts <- NULL RET$theDots <- list(...) class(RET) <- "knnreg" RET } print.knnreg <- function (x, ...) { cat(x$k, "-nearest neighbor regression model\n", sep = "") invisible(x) } predict.knnreg <- function (object, newdata, ...) { if (!inherits(object, "knnreg")) stop("object not of class knnreg") if (!is.null(Terms <- object$terms)) { if (missing(newdata)) newdata <- model.frame(object) else { newdata <- model.frame(as.formula(delete.response(Terms)), newdata) } x <- model.matrix(delete.response(Terms), newdata, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] } else { x <- as.matrix(newdata) } argList <- list(train = object$learn$X, test = x, y = object$learn$y, k = object$k) RET <- do.call("knnregTrain", argList) RET } knnregTrain <- function(train, test, y, k = 5, use.all=TRUE) { train <- as.matrix(train) if(is.null(dim(test))) dim(test) <- c(1, length(test)) test <- as.matrix(test) if(any(is.na(train)) || any(is.na(test)) || any(is.na(y))) stop("no missing values are allowed") p <- ncol(train) ntr <- nrow(train) if(length(y) != ntr) stop("'train' and 'class' have different lengths") if(ntr < k) { warning(gettextf("k = %d exceeds number %d of patterns", k, ntr), domain = NA) k <- ntr } if (k < 1) stop(gettextf("k = %d must be at least 1", k), domain = NA) nte <- nrow(test) if(ncol(test) != p) stop("dims of 'test' and 'train differ") Z <- .C("knn3reg", as.integer(k), as.integer(ntr), as.integer(nte), as.integer(p), as.double(train), as.double(y), as.double(test), double(nte), as.integer(FALSE), as.integer(use.all)) Z[[8]] } caret/R/selectByFilter.R0000644000175000017500000005731512516214271015304 0ustar rbalintrbalint sbfIter <- function(x, y, testX, testY, sbfControl = sbfControl(), ...) { if(is.null(colnames(x))) stop("x must have column names") if(is.null(testX) | is.null(testY)) stop("a test set must be specified") if(sbfControl$multivariate) { scores <- sbfControl$functions$score(x, y) if(length(scores) != ncol(x)) stop(paste("when control$multivariate == TRUE, 'scores'", "should return a vector with", ncol(x), "numeric values")) } else { scores <- apply(x, 2, sbfControl$functions$score, y = y) } retained <- sbfControl$functions$filter(scores, x, y) ## deal with zero length results testX <- testX[, which(retained), drop = FALSE] fitObject <- sbfControl$functions$fit(x[, which(retained), drop = FALSE], y, ...) modelPred <- sbfControl$functions$pred(fitObject, testX) if(is.data.frame(modelPred) | is.matrix(modelPred)) { if(is.matrix(modelPred)) modelPred <- as.data.frame(modelPred) modelPred$obs <- testY } else modelPred <- data.frame(pred = modelPred, obs = testY) list(variables = names(retained)[which(retained)], pred = modelPred) } ###################################################################### ###################################################################### sbf <- function (x, ...) UseMethod("sbf") "sbf.default" <- function(x, y, sbfControl = sbfControl(), ...) { startTime <- proc.time() funcCall <- match.call(expand.dots = TRUE) numFeat <- ncol(x) classLevels <- levels(y) if(sbfControl$method == "oob") stop("out-of-bag resampling cannot be used with this function") if(is.null(sbfControl$index)) sbfControl$index <- switch( tolower(sbfControl$method), cv = createFolds(y, sbfControl$number, returnTrain = TRUE), repeatedcv = createMultiFolds(y, sbfControl$number, sbfControl$repeats), loocv = createFolds(y, length(y), returnTrain = TRUE), boot =, boot632 = createResample(y, sbfControl$number), test = createDataPartition(y, 1, sbfControl$p), lgocv = createDataPartition(y, sbfControl$number, sbfControl$p)) if(is.null(names(sbfControl$index))) names(sbfControl$index) <- prettySeq(sbfControl$index) if(is.null(sbfControl$indexOut)){ sbfControl$indexOut <- lapply(sbfControl$index, function(training, allSamples) allSamples[-unique(training)], allSamples = seq(along = y)) names(sbfControl$indexOut) <- prettySeq(sbfControl$indexOut) } ## check summary function and metric testOutput <- data.frame(pred = sample(y, min(10, length(y))), obs = sample(y, min(10, length(y)))) if(is.factor(y)) { for(i in seq(along = classLevels)) testOutput[, classLevels[i]] <- runif(nrow(testOutput)) } test <- sbfControl$functions$summary(testOutput, lev = classLevels) perfNames <- names(test) ## Set or check the seeds when needed if(is.null(sbfControl$seeds)) { sbfControl$seeds <- sample.int(n = 1000000, size = length(sbfControl$index) + 1) } else { if(!(length(sbfControl$seeds) == 1 && is.na(sbfControl$seeds))) { if(length(sbfControl$seeds) != length(sbfControl$index) + 1) stop(paste("Bad seeds: the seed object should be an integer vector of length", length(sbfControl$index) + 1)) } } ######################################################################### if(sbfControl$method == "LOOCV") { tmp <- looSbfWorkflow(x = x, y = y, ppOpts = preProcess, ctrl = sbfControl, lev = classLevels, ...) resamples <- do.call("rbind", tmp$everything[names(tmp$everything) == "pred"]) rownames(resamples) <- 1:nrow(resamples) selectedVars <- tmp$everything[names(tmp$everything) == "variables"] performance <- tmp$performance } else { tmp <- nominalSbfWorkflow(x = x, y = y, ppOpts = preProcess, ctrl = sbfControl, lev = classLevels, ...) resamples <- do.call("rbind", tmp$everything[names(tmp$everything) == "resamples"]) rownames(resamples) <- 1:nrow(resamples) selectedVars <- tmp$everything[names(tmp$everything) == "selectedVars"] performance <- tmp$performance } ######################################################################### varList <- unique(unlist(selectedVars)) if(sbfControl$multivariate) { scores <- sbfControl$functions$score(x, y) if(length(scores) != ncol(x)) stop(paste("when control$multivariate == TRUE, 'scores'", "should return a vector with", ncol(x), "numeric values")) } else { scores <- apply(x, 2, sbfControl$functions$score, y = y) } retained <- sbfControl$functions$filter(scores, x, y) finalTime <- system.time( fit <- sbfControl$functions$fit(x[, retained, drop = FALSE], y, ...)) performance <- data.frame(t(performance)) performance <- performance[,!grepl("\\.cell|Resample", colnames(performance))] if(is.factor(y) & any(names(resamples) == ".cell1")) { keepers <- c("Resample", grep("\\.cell", names(resamples), value = TRUE)) resampledCM <- resamples[,keepers] resamples <- resamples[, -grep("\\.cell", names(resamples))] } else resampledCM <- NULL resamples <- switch(sbfControl$returnResamp, none = NULL, all =, final = resamples) endTime <- proc.time() times <- list(everything = endTime - startTime, final = finalTime) ######################################################################### ## Now, based on probability or static ranking, figure out the best vars ## and the best subset size and fit final model out <- structure( list( pred = if(sbfControl$saveDetails) tmp else NULL, variables = selectedVars, results = performance, fit = fit, optVariables = names(retained)[retained], call = funcCall, control = sbfControl, resample = resamples, metrics = perfNames, times = times, resampledCM = resampledCM, obsLevels = classLevels, dots = list(...)), class = "sbf") if(sbfControl$timingSamps > 0) { out$times$prediction <- system.time(predict(out, x[1:min(nrow(x), sbfControl$timingSamps),,drop = FALSE])) } else out$times$prediction <- rep(NA, 3) out } sbf.formula <- function (form, data, ..., subset, na.action, contrasts = NULL) { m <- match.call(expand.dots = FALSE) if (is.matrix(eval.parent(m$data))) m$data <- as.data.frame(data) m$... <- m$contrasts <- NULL m[[1]] <- as.name("model.frame") m <- eval.parent(m) Terms <- attr(m, "terms") x <- model.matrix(Terms, m, contrasts) cons <- attr(x, "contrast") xint <- match("(Intercept)", colnames(x), nomatch = 0) if (xint > 0) x <- x[, -xint, drop = FALSE] y <- model.response(m) res <- sbf(as.data.frame(x), y, ...) res$terms <- Terms res$coefnames <- colnames(x) res$call <- match.call() res$na.action <- attr(m, "na.action") res$contrasts <- cons res$xlevels <- .getXlevels(Terms, m) class(res) <- c("sbf", "sbf.formula") res } ###################################################################### ###################################################################### print.sbf <- function(x, top = 5, digits = max(3, getOption("digits") - 3), ...) { cat("\nSelection By Filter\n\n") resampleN <- unlist(lapply(x$control$index, length)) numResamp <- length(resampleN) resampText <- resampName(x) cat("Outer resampling method:", resampText, "\n") cat("\nResampling performance:\n\n") print(format(x$results, digits = digits), row.names = FALSE) cat("\n") if(length(x$optVariables) > 0) { cat("Using the training set, ", length(x$optVariables), ifelse(length(x$optVariables) > 1, " variables were selected:\n ", " variable was selected:\n "), paste(x$optVariables[1:min(top, length(x$optVariables))], collapse = ", "), ifelse(length(x$optVariables) > top, "..", ""), ".\n\n", sep = "") } else cat("No variables were selected from the training set.\n\n") vars <- sort(table(unlist(x$variables)), decreasing = TRUE) top <- min(top, length(vars)) smallVars <- vars[1:top] smallVars <- round(smallVars/length(x$control$index)*100, 1) varText <- paste(names(smallVars), " (", smallVars, "%)", sep = "") varText <- paste(varText, collapse = ", ") if(!all(is.na(smallVars))) { cat("During resampling, the top ", top, " selected variables (out of a possible ", length(vars), "):\n ", varText, "\n\n", sep = "") cat("On average, ", round(mean(unlist(lapply(x$variables, length))), 1), " variables were selected (min = ", round(min(unlist(lapply(x$variables, length))), 1), ", max = ", round(max(unlist(lapply(x$variables, length))), 1), ")\n", sep = "") } else { cat("During resampling, no variables were selected.\n\n") } invisible(x) } ###################################################################### ###################################################################### predict.sbf <- function(object, newdata = NULL, ...) { if(!all(object$optVariables %in% colnames(newdata))) stop("required columns in newdata are missing") if(!is.null(newdata)) { if (inherits(object, "sbf.formula")) { newdata <- as.data.frame(newdata) rn <- row.names(newdata) Terms <- delete.response(object$terms) m <- model.frame(Terms, newdata, na.action = na.omit, xlev = object$xlevels) if (!is.null(cl <- attr(Terms, "dataClasses"))) .checkMFClasses(cl, m) keep <- match(row.names(m), rn) newdata <- model.matrix(Terms, m, contrasts = object$contrasts) xint <- match("(Intercept)", colnames(newdata), nomatch = 0) if (xint > 0) newdata <- newdata[, -xint, drop = FALSE] } newdata <- newdata[, object$optVariables, drop = FALSE] out <- object$control$functions$pred(object$fit, newdata) } else { out <- object$control$functions$pred(object$fit) } out } ###################################################################### ###################################################################### sbfControl <- function(functions = NULL, method = "boot", saveDetails = FALSE, number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25), repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number), verbose = FALSE, returnResamp = "final", p = .75, index = NULL, indexOut = NULL, timingSamps = 0, seeds = NA, allowParallel = TRUE, multivariate = FALSE) { list( functions = if(is.null(functions)) caretSBF else functions, method = method, saveDetails = saveDetails, number = number, repeats = repeats, returnResamp = returnResamp, verbose = verbose, p = p, index = index, indexOut = indexOut, timingSamps = timingSamps, seeds = seeds, allowParallel = allowParallel, multivariate = multivariate) } ###################################################################### ###################################################################### ## some built-in functions for certain models anovaScores <- function(x, y) { pv <- try(anova(lm(x ~ y), test = "F")[1, "Pr(>F)"], silent = TRUE) if(any(class(pv) == "try-error") || is.na(pv) || is.nan(pv)) pv <- 1 pv } gamScores <- function(x, y) { requireNamespaceQuietStop("gam") pv <- try(anova(gam::gam(y ~ s(x)), test = "F")[2, "Pr(F)"], silent = TRUE) if(any(class(pv) == "try-error")) pv <- try(anova(lm(x ~ y), test = "F")[1, "Pr(>F)"], silent = TRUE) if(any(class(pv) == "try-error") || is.na(pv) || is.nan(pv)) pv <- 1 pv } caretSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { train(x, y, ...) } else nullModel(y = y) }, pred = function(object, x) { if(class(object) != "nullModel") { tmp <- predict(object, x) if(object$modelType == "Classification" & !is.null(object$modelInfo$prob)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } else { tmp <- predict(object, x) if(!is.null(object$levels)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } out }, score = function(x, y) { ## should return a named logical vector if(is.factor(y)) anovaScores(x, y) else gamScores(x, y) }, filter = function(score, x, y) score <= 0.05 ) rfSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { loadNamespace("randomForest") randomForest::randomForest(x, y, ...) } else nullModel(y = y) }, pred = function(object, x) { if(class(object) == "nullModel") { tmp <- predict(object, x) if(!is.null(object$levels)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } else { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } out }, score = function(x, y) { ## should return a named logical vector if(is.factor(y)) anovaScores(x, y) else gamScores(x, y) }, filter = function(score, x, y) score <= 0.05 ) lmSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { tmp <- as.data.frame(x) tmp$y <- y lm(y~., data = tmp) } else nullModel(y = y) }, pred = function(object, x) { predict(object, x) }, score = function(x, y) { anovaScores(y, x) }, filter = function(score, x, y) score <= 0.05 ) ldaSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { loadNamespace("MASS") MASS::lda(x, y, ...) } else nullModel(y = y) }, pred = function(object, x) { if(class(object) == "nullModel") { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp), as.data.frame( predict(object, x, type = "prob"))) } else { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp$class), as.data.frame(tmp$posterior)) } out }, score = function(x, y) { ## should return a named logical vector anovaScores(x, y) }, filter = function(score, x, y) score <= 0.05 ) nbSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { loadNamespace("klaR") klaR::NaiveBayes(x, y, usekernel = TRUE, fL = 2, ...) } else nullModel(y = y) }, pred = function(object, x) { if(class(object) == "nullModel") { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp), as.data.frame( predict(object, x, type = "prob"))) } else { tmp <- predict(object, x) out <- cbind(data.frame(pred = tmp$class), as.data.frame(tmp$posterior)) } out }, pred = function(object, x) { predict(object, x)$class }, score = function(x, y) { ## should return a named logical vector anovaScores(x, y) }, filter = function(score, x, y) score <= 0.05 ) treebagSBF <- list(summary = defaultSummary, fit = function(x, y, ...) { if(ncol(x) > 0) { loadNamespace("ipred") ipred::ipredbagg(y, x, ...) } else nullModel(y = y) }, pred = function(object, x) { if(class(object) == "nullModel") { tmp <- predict(object, x) if(!is.null(object$levels)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } else { tmp <- predict(object, x) if(is.factor(object$y)) { out <- cbind(data.frame(pred = tmp), as.data.frame(predict(object, x, type = "prob"))) } else out <- tmp } out }, score = function(x, y) { ## should return a named logical vector anovaScores(x, y) }, filter = function(score, x, y) score <= 0.05 ) ###################################################################### ###################################################################### ## lattice functions densityplot.sbf <- function(x, data = NULL, metric = x$metric[1], ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) form <- as.formula(paste("~", metric)) densityplot(form, data = data, ...) } histogram.sbf <- function(x, data = NULL, metric = x$metric[1], ...) { if (!is.null(match.call()$data)) warning("explicit 'data' specification ignored") if(x$control$method %in% c("oob", "LOOCV")) stop("Resampling plots cannot be done with leave-out-out CV or out-of-bag resampling") data <- as.data.frame(x$resample) form <- as.formula(paste("~", metric)) histogram(form, data = data, ...) } ###################################################################### ###################################################################### ## other functions predictors.sbf <- function(x, ...) x$optVariables varImp.sbf <- function(object, onlyFinal = TRUE, ...) { vars <- sort(table(unlist(object$variables)), decreasing = TRUE)/length(object$control$index) out <- as.data.frame(vars) names(out) <- "Overall" if(onlyFinal) out <- subset(out, rownames(out) %in% object$optVariables) out[order(-out$Overall),,drop = FALSE] } ###################################################################### ## what to do when no predictors are selected? nullModel <- function (x, ...) UseMethod("nullModel") nullModel.default <- function(x = NULL, y, ...) { if(is.factor(y)) { lvls <- levels(y) tab <- table(y) value <- names(tab)[which.max(tab)] pct <- tab/sum(tab) } else { lvls <- NULL pct <- NULL value <- mean(y, na.rm = TRUE) } structure( list(call = match.call(), value = value, levels = lvls, pct = pct, n = length(y)), class = "nullModel") } print.nullModel <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("Null", ifelse(is.null(x$levels), "Classification", "Regression"), "Model\n") printCall(x$call) cat("Predicted Value:", ifelse(is.null(x$levels), format(x$value, digitis = digits), x$value), "\n") } predict.nullModel <- function (object, newdata = NULL, type = NULL, ...) { if(is.null(type)) { type <- if(is.null(object$levels)) "raw" else "class" } n <- if(is.null(newdata)) object$n else nrow(newdata) if(!is.null(object$levels)) { if(type == "prob") { out <- matrix(rep(object$pct, n), nrow = n, byrow = TRUE) colnames(out) <- object$levels out <- as.data.frame(out) } else { out <- factor(rep(object$value, n), levels = object$levels) } } else { if(type %in% c("prob", "class")) stop("ony raw predicitons are applicable to regression models") out <- rep(object$value, n) } out } caret/R/createFolds.R0000644000175000017500000000567012516214271014614 0ustar rbalintrbalint"createFolds" <- function(y, k = 10, list = TRUE, returnTrain = FALSE) { if(is.numeric(y)) { ## Group the numeric data based on their magnitudes ## and sample within those groups. ## When the number of samples is low, we may have ## issues further slicing the numeric data into ## groups. The number of groups will depend on the ## ratio of the number of folds to the sample size. ## At most, we will use quantiles. If the sample ## is too small, we just do regular unstratified ## CV cuts <- floor(length(y)/k) if(cuts < 2) cuts <- 2 if(cuts > 5) cuts <- 5 breaks <- unique(quantile(y, probs = seq(0, 1, length = cuts))) y <- cut(y, breaks, include.lowest = TRUE) } if(k < length(y)) { ## reset levels so that the possible levels and ## the levels in the vector are the same y <- factor(as.character(y)) numInClass <- table(y) foldVector <- vector(mode = "integer", length(y)) ## For each class, balance the fold allocation as far ## as possible, then resample the remainder. ## The final assignment of folds is also randomized. for(i in 1:length(numInClass)) { ## create a vector of integers from 1:k as many times as possible without ## going over the number of samples in the class. Note that if the number ## of samples in a class is less than k, nothing is producd here. min_reps <- numInClass[i] %/% k if(min_reps > 0) { spares <- numInClass[i] %% k seqVector <- rep(1:k, min_reps) ## add enough random integers to get length(seqVector) == numInClass[i] if(spares > 0) seqVector <- c(seqVector, sample(1:k, spares)) ## shuffle the integers for fold assignment and assign to this classes's data foldVector[which(y == names(numInClass)[i])] <- sample(seqVector) } else { ## Here there are less records in the class than unique folds so ## randomly sprinkle them into folds. foldVector[which(y == names(numInClass)[i])] <- sample(1:k, size = numInClass[i]) } } } else foldVector <- seq(along = y) if(list) { out <- split(seq(along = y), foldVector) names(out) <- paste("Fold", gsub(" ", "0", format(seq(along = out))), sep = "") if(returnTrain) out <- lapply(out, function(data, y) y[-data], y = seq(along = y)) } else out <- foldVector out } createMultiFolds <- function(y, k = 10, times = 5) { prettyNums <- paste("Rep", gsub(" ", "0", format(1:times)), sep = "") for(i in 1:times) { tmp <- createFolds(y, k = k, list = TRUE, returnTrain = TRUE) names(tmp) <- paste("Fold", gsub(" ", "0", format(seq(along = tmp))), ".", prettyNums[i], sep = "") out <- if(i == 1) tmp else c(out, tmp) } out } caret/R/trim.R0000644000175000017500000000155012516214271013325 0ustar rbalintrbalinttrim <- function(object, ...) UseMethod("trim") trim.train <- function(object, ...) { removals <- c("results", "pred", "bestTune", "call", "dots", "metric", "trainingData", "resample", "resampledCM", "perfNames", "maxmimize", "times") for(i in removals) if(i %in% names(object)) object[i] <- NULL c_removals <- c('method', 'number', 'repeats', 'p', 'initialWindow', 'horizon', 'fixedWindow', 'verboseIter', 'returnData', 'returnResamp', 'savePredictions', 'summaryFunction', 'selectionFunction', 'index', 'indexOut', 'timingSamps', 'trim', 'yLimits') for(i in c_removals) if(i %in% names(object$control)) object$control[i] <- NULL if(!is.null(object$modelInfo$trim)) object$finalModel <- object$modelInfo$trim(object$finalModel) object }caret/R/aaa.R0000644000175000017500000002005712520743335013102 0ustar rbalintrbalint## This file is a cheat to minimize the false positives flagged during R CMD check. such as ## ## "bwplot.diff.resamples: no visible binding for global variable 'Metric'" ## "bwplot.resamples: no visible binding for global variable 'Model'" ## "bwplot.resamples: no visible binding for global variable 'Metric'" ## ## when ## ## bwplot.resamples <- function (x, data = NULL, models = x$models, metric = x$metric, ...) ## { ## ... ## plotData <- subset(plotData, Model %in% models & Metric %in% metric) ## ... ## } ## ## and other examples. Metric <- Model <- NULL ## densityplot(~ values|Metric, data = plotData, groups = ind, ## xlab = "", ...) ind <- NULL ## avPerf <- ddply(subset(results, Metric == metric[1] & X2 == "Estimate"), ## .(Model), ## function(x) c(Median = median(x$value, na.rm = TRUE))) X2 <- NULL ## x[[i]]$resample <- subset(x[[i]]$resample, Variables == x[[i]]$bestSubset) Variables <- NULL ## calibCalc: no visible binding for global variable 'obs' ## calibCalc: no visible binding for global variable 'bin' ## ## calibCalc <- function(x, class = levels(obs)[1], cuts = 11) ## { ## binData <- data.frame(prob = x$calibProbVar, ## bin = cut(x$calibProbVar, (0:cuts)/cuts, include.lowest = TRUE), ## class = x$calibClassVar) obs <- bin <- NULL ## ## checkConditionalX: no visible binding for global variable '.outcome' ## checkConditionalX <- function(x, y) ## { ## x$.outcome <- y ## unique(unlist(dlply(x, .(.outcome), zeroVar))) ## } .outcome <- NULL ## classLevels.splsda: no visible global function definition for 'ilevels' ## ## classLevels.splsda <- function(x, ...) ## { ## ## objects from package caret and spls have the ## ## same class name, but this works for either ## ilevels(x$y) ## } ilevels <- NULL ## looRfeWorkflow: no visible binding for global variable 'iter' ## looSbfWorkflow: no visible binding for global variable 'iter' ## looTrainWorkflow: no visible binding for global variable 'parm' ## looTrainWorkflow: no visible binding for global variable 'iter' ## nominalRfeWorkflow: no visible binding for global variable 'iter' ## nominalRfeWorkflow: no visible binding for global variable 'method' ## nominalRfeWorkflow: no visible binding for global variable 'Resample' ## nominalSbfWorkflow: no visible binding for global variable 'dat' ## nominalSbfWorkflow: no visible binding for global variable 'iter' ## nominalTrainWorkflow: no visible binding for global variable 'parm' ## nominalTrainWorkflow: no visible binding for global variable 'iter' ## nominalTrainWorkflow: no visible binding for global variable 'Resample' ## oobTrainWorkflow: no visible binding for global variable 'parm' ## ## result <- foreach(iter = seq(along = resampleIndex), ## .combine = "c", .verbose = FALSE, ## .packages = "caret", .errorhandling = "stop") %:% ## foreach(parm = 1:nrow(info$loop), .combine = "c", ## .verbose = FALSE, .packages = "caret", ## .errorhandling = "stop") %dopar% ## { ## iter <- parm <- method <- Resample <- dat <- NULL ## tuneScheme: no visible binding for global variable '.alpha' ## tuneScheme: no visible binding for global variable '.phi' ## tuneScheme: no visible binding for global variable '.lambda' ## ## seqParam[[i]] <- data.frame(.lambda = subset(grid, ## subset = .phi == loop$.phi[i] & ## .lambda < loop$.lambda[i])$.lambda) .alpha <- .phi <- .lambda <- NULL ## createGrid : somDims: no visible binding for global variable '.xdim' ## createGrid : somDims: no visible binding for global variable '.ydim' ## createGrid : lvqGrid: no visible binding for global variable '.k' ## createGrid : lvqGrid: no visible binding for global variable '.size' ## ## out <- expand.grid(.xdim = 1:x, .ydim = 2:(x+1), ## .xweight = seq(.5, .9, length = len)) ## .xdim <- .ydim <- .k <- .size <- NULL ## createModel: possible error in rda(trainX, trainY, gamma = ## tuneValue$.gamma, lambda = tuneValue$.lambda, ...): unused ## argument(s) (gamma = tuneValue$.gamma, lambda = tuneValue$.lambda) ## createModel: no visible global function definition for ## 'randomForestNWS' ## createModel: no visible global function definition for 'rfLSF' ## createModel: possible error in rvm(as.matrix(trainX), trainY, kernel = ## polydot, kpar = list(degree = tuneValue$.degree, scale = ## tuneValue$.scale, offset = 1), ...): unused argument(s) (kernel = ## polydot, kpar = list(degree = tuneValue$.degree, scale = ## tuneValue$.scale, offset = 1)) ## createModel: possible error in rvm(as.matrix(trainX), trainY, kernel = ## rbfdot, kpar = list(sigma = tuneValue$.sigma), ...): unused ## argument(s) (kernel = rbfdot, kpar = list(sigma = tuneValue$.sigma)) ## createModel: possible error in rvm(as.matrix(trainX), trainY, kernel = ## vanilladot(), ...): unused argument(s) (kernel = vanilladot()) ## ## ???? ## ## > formals(klaR::rda.default) ## $x ## ## $gamma ## [1] NA ## ## $lambda ## [1] NA ## predictionFunction: no visible binding for global variable '.alpha' ## ## delta <- subset(param, .alpha == uniqueA[i])$.delta ## .alpha <- NULL ## predictors.gbm: no visible binding for global variable 'rel.inf' ## predictors.sda: no visible binding for global variable 'varIndex' ## predictors.smda: no visible binding for global variable 'varIndex' ## ## varUsed <- as.character(subset(relImp, rel.inf != 0)$var) rel.inf <- varIndex <- NULL ## plotClassProbs: no visible binding for global variable 'Observed' ## ## out <- densityplot(form, data = stackProbs, groups = Observed, ...) Observed <- NULL ## plot.train: no visible binding for global variable 'parameter' ## ## paramLabs <- subset(modelInfo, parameter %in% params)$label parameter <- NULL ## plot.rfe: no visible binding for global variable 'Selected' ## ## out <- xyplot(plotForm, data = results, groups = Selected, panel = panel.profile, ...) Selected <- NULL ## icr.formula: no visible binding for global variable 'thresh' ## ## res <- icr.default(x, y, weights = w, thresh = thresh, ...) thresh <- NULL probValues <- min_prob <- NULL altTrainWorkflow <- function(x) x groups <- NULL trainData <- NULL j <- NULL x <- NULL .B <- NULL model_id <- player1 <- player2 <- playa <- win1 <- win2 <- name <- NULL object <- Iter <- lvls <- Mean <- Estimate <- NULL ################################################################### ## best <- function(x, metric, maximize) { bestIter <- if(maximize) which.max(x[,metric]) else which.min(x[,metric]) bestIter } defaultSummary <- function(data, lev = NULL, model = NULL) { if(is.character(data$obs)) data$obs <- factor(data$obs, levels = lev) postResample(data[,"pred"], data[,"obs"]) } twoClassSummary <- function (data, lev = NULL, model = NULL) { requireNamespaceQuietStop('pROC') if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) stop("levels of observed and predicted data do not match") rocObject <- try(pROC::roc(data$obs, data[, lev[1]]), silent = TRUE) rocAUC <- if(class(rocObject)[1] == "try-error") NA else rocObject$auc out <- c(rocAUC, sensitivity(data[, "pred"], data[, "obs"], lev[1]), specificity(data[, "pred"], data[, "obs"], lev[2])) names(out) <- c("ROC", "Sens", "Spec") out } mnLogLoss <- function(data, lev = NULL, model = NULL){ if(is.null(lev)) stop("'lev' cannot be NULL") if(!all(lev %in% colnames(data))) stop("'data' should have columns consistent with 'lev'") if(!all(sort(lev) %in% sort(levels(data$obs)))) stop("'data$obs' should have levels consistent with 'lev'") eps <- 1e-15 probs <- as.matrix(data[, lev, drop = FALSE]) probs[probs > 1 - eps] <- 1 - eps probs[probs < eps] <- eps inds <- getFromNamespace("class2ind", "caret")(data$obs)[, lev, drop = FALSE] c(logLoss = -mean(apply(inds*log(probs), 1, sum), na.rm = TRUE)) } oob_mods <- c("rf", "treebag", "cforest", "bagEarth", "bagEarthGCV", "bagFDA","bagFDAGCV", "parRF") caret/R/caretTheme.R0000644000175000017500000000622212453550064014437 0ustar rbalintrbalintcaretTheme <- function() list( plot.polygon = list(alpha = 1, col = "aliceblue", border = "black", lty = 1, lwd = 1), background = list(col = "transparent"), bar.fill = list(col = "#cce6ff"), box.rectangle = list(col = "black"), box.umbrella = list(col = "black"), dot.line = list(col = "#e8e8e8"), dot.symbol = list(col = "black"), plot.line = list(col = "black"), plot.symbol = list(col = "black"), regions = list(col = c( "#FEF8FA", "#FDF6F9", "#FBF5F9", "#FAF3F8", "#F8F2F7", "#F7F0F7", "#F5EEF6", "#F4EDF5", "#F2EBF5", "#F1EAF4", "#EFE8F3", "#EDE7F2", "#ECE5F1", "#EAE4F1", "#E8E2F0", "#E6E1EF", "#E4DFEE", "#E2DEED", "#E0DCEC", "#DEDAEB", "#DCD9EA", "#D9D7E9", "#D7D6E8", "#D4D4E7", "#D1D2E6", "#CED1E5", "#CCCFE4", "#C8CEE3", "#C5CCE2", "#C2CAE1", "#BFC9E0", "#BBC7DF", "#B8C5DF", "#B4C4DE", "#B1C2DD", "#ADC0DC", "#A9BFDB", "#A6BDDA", "#A2BBD9", "#9EB9D9", "#9BB8D8", "#97B6D7", "#93B4D6", "#8FB2D5", "#8BB0D4", "#87AFD3", "#83ADD2", "#7FABD1", "#7AA9D0", "#76A7CF", "#71A5CE", "#6CA3CC", "#68A1CB", "#63A0CA", "#5D9EC9", "#589CC8", "#539AC6", "#4E98C5", "#4996C4", "#4493C3", "#3F91C1", "#3A8FC0", "#358DBF", "#308BBE", "#2C89BD", "#2887BC", "#2385BB", "#1F83BA", "#1C80B9", "#187EB7", "#157CB6", "#127AB5", "#0F78B3", "#0D76B2", "#0A73B0", "#0971AE", "#076FAC", "#066DAA", "#056AA7", "#0568A5") ), strip.shingle = list(col = c( "#ff7f00", "#00ff00", "#00ffff", "#ff00ff", "#ff0000", "#ffff00", "#0080ff")), strip.background = list(col = c( "#ffe5cc", "#ccffcc", "#ccffff", "#ffccff", "#ffcccc", "#ffffcc", "#cce6ff")), reference.line = list(col = "#e8e8e8"), superpose.line = list( col = c( "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black"), lty = rep(1:6, each = 6)), superpose.symbol = list( pch = c( 1, 4, 6, 0, 5, 17, 4, 6, 0, 5, 17, 1, 6, 0, 5, 17, 1, 4, 0, 5, 17, 1, 4, 6, 5, 17, 1, 4, 6, 0 , 17, 1, 4, 6, 0, 5), cex = rep(0.7, 6 * 6), col = c( "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black", "#9E0142", "#3288BD", "#F46D43", "#5E4FA2", "#66C2A5", "black"))) caret/R/sortImp.R0000644000175000017500000000166112453550064014015 0ustar rbalintrbalintsortImp <- function(object, top) { if(object$calledFrom == "varImp") { best <- switch( object$model, pam = "maxabs", "max") } else { best <- "max" } featureRank <- switch(best, max = rank(-apply(object$importance, 1, max, na.rm = TRUE)), min = rank(apply(object$importance, 1, min, na.rm = TRUE)), maxabs = rank(-apply(abs(object$importance), 1, max, na.rm = TRUE))) tiedRanks <- as.numeric(names(table(featureRank)[table(featureRank) > 1])) if(length(tiedRanks) > 0) { for(i in seq(along = tiedRanks)) { tmp <- featureRank[featureRank == tiedRanks[i]] featureRank[featureRank == tiedRanks[i]] <- tmp + runif(length(tmp), min = 0.001, max = 0.999) } } featureOrder <- order(featureRank) out <- object$importance[featureOrder,, drop = FALSE] out <- out[1:top,, drop = FALSE] out } caret/R/probFunction.R0000644000175000017500000000076612453550064015035 0ustar rbalintrbalintprobFunction <- function(method, modelFit, newdata, preProc = NULL, param = NULL) { if(!is.null(preProc)) newdata <- predict(preProc, newdata) obsLevels <- levels(modelFit) classProb <- method$prob(modelFit = modelFit, newdata = newdata, submodels = param) if(!is.data.frame(classProb) & is.null(param)) { classProb <- as.data.frame(classProb) if(!is.null(obsLevels)) classprob <- classProb[, obsLevels] } classProb } caret/R/suggestions.R0000644000175000017500000000431412453550064014730 0ustar rbalintrbalintsuggestions <- function(model) { out <- c("center" = FALSE, "scale" = FALSE, "nzv" = FALSE, "corr" = FALSE) if(model %in% c("enet", "gaussprLinear", "gaussprPoly", "gaussprRadial", "glmnet", "gpls", "knn", "lars", "lars2", "lasso", "lssvmLinear", "lssvmPoly", "lssvmRadial", "multinom", "neuralnet", "nnet", "penalized", "pls", "relaxo", "rocc", "rvmLinear", "rvmPoly", "rvmRadial", "smda", "sparseLDA", "spls", "superpc", "svmLinear", "svmPoly", "svmRadial")) out["center"] <- TRUE if(model %in% c("gaussprLinear", "gaussprPoly", "gaussprRadial", "glmnet", "gpls", "knn", "lars", "lars2", "lasso", "lssvmLinear", "lssvmPoly", "lssvmRadial", "multinom", "neuralnet", "nnet", "penalized", "pls", "relaxo", "rocc", "rvmLinear", "rvmPoly", "rvmRadial", "smda", "sparseLDA", "spls", "superpc", "svmLinear", "svmPoly", "svmRadial")) out["scale"] <- TRUE if(model %in% c("enet", "gaussprLinear", "gaussprPoly", "gaussprRadial", "gpls", "hda", "hdda", "icr", "knn", "lars", "lars2", "lasso", "lda", "knn", "lm", "lmStepAIC", "glm", "glmStepAIC", "lssvmLinear", "lssvmPoly", "lssvmRadial", "lvq", "mda", "multinom", "nb", "neuralnet", "nnet", "pam", "pcaNNet", "pcr", "pda", "pda2", "penalized", "pls", "qda", "QdaCov", "rda", "relaxo", "rlm", "rocc", "rvmLinear", "rvmPoly", "rvmRadial", "scrda", "sda", "sddaLDA", "sddaQDA", "slda", "smda", "sparseLDA", "spls", "stepLDA", "stepQDA", "superpc", "svmLinear", "svmPoly", "svmRadial", "vbmpRadial")) out["nzv"] <- TRUE if(model %in% c("enet", "lars", "lars2", "lasso", "lda", "lm", "lmStepAIC", "glm", "glmStepAIC", "multinom", "nb", "neuralnet", "nnet", "pda", "pda2", "penalized", "relaxo", "rlm" )) out["nzv"] <- TRUE out } caret/R/maxDissim.R0000644000175000017500000000514312516214271014312 0ustar rbalintrbalintmaxDissim <- function(a, b, n = 2, obj = minDiss, useNames = FALSE, randomFrac = 1, verbose = FALSE, ...) { loadNamespace("proxy") if(nrow(b) < 2) stop("there must be at least 2 samples in b") if(ncol(a) != ncol(b)) stop("a and b must have the same number of columns") if(nrow(b) < n) stop("n must be less than nrow(b)") if(randomFrac > 1 | randomFrac <= 0) stop("randomFrac must be in (0, 1]") if(useNames) { if(is.null(rownames(b))) { warning("Cannot use rownames; swithcing to indices") free <- 1:nrow(b) } else free <- rownames(b) } else free <- 1:nrow(b) inSubset <- NULL newA <- a if(verbose) cat(" adding:") for(i in 1:n) { pool <- if(randomFrac == 1) free else sample(free, max(2, floor(randomFrac * length(free)))) if(verbose) { cat("\nIter", i, "\n") cat("Number of candidates:", length(free), "\n") cat("Sampling from", length(pool), "samples\n") } diss <- proxy::dist(newA, b[pool,, drop = FALSE], ...) bNames <- colnames(b)[pool] tmp <- pool[which.max(apply(diss, 2, obj))] if(verbose)cat("new sample:", tmp, "\n") inSubset <- c(inSubset, tmp) newA <- rbind(newA, b[tmp,, drop = FALSE]) free <- free[!(free %in% inSubset)] } inSubset } minDiss <- function(u) min(u, na.rm = TRUE) sumDiss <- function(u) sum(u, na.rm = TRUE) splitter <- function(x, p = .8, start = NULL, ...) { n <- nrow(x) if(is.null(start)) start <- sample(1:n, 1) n2 <- n - length(start) m <- ceiling(p * n2) pool <- maxDissim(x[ start,,drop = FALSE], x[-start,,drop = FALSE], n = m, ...) c(start, pool) } splitByDissim <- function(x, p = .8, y = NULL, start = NULL, ...) { if(!is.data.frame(x)) x <- as.data.frame(x) if(!is.null(y)) { if(!is.factor(y)) y <- as.factor(y) lvl <- levels(y) ind <- split(seq(along = y), y) ind2 <- lapply(ind, function(x) seq(along = x)) start2 <- lapply(ind, function(x, start) which(x %in% start), start = start) for(i in seq(along = lvl)) { tmp <- splitter(x[ind[[i]],, drop = FALSE], p = p, start = start2[[i]], ...) tmp2 <- ind[[i]][which(ind2[[i]] %in% tmp)] out <- if(i == 1) tmp2 else c(tmp2, out) } } else { out <- splitter(x, p = p, start = start, ...) } out } caret/R/expoTrans.R0000644000175000017500000000604512453550064014344 0ustar rbalintrbalint expoTrans <- function(y, ...) UseMethod("expoTrans") expoTrans.default <- function(y, na.rm = TRUE, init = 0, lim = c(-4, 4), method = "Brent", numUnique = 3, ...) { requireNamespaceQuietStop("e1071") if(any(is.na(y)) & !na.rm) stop("missing data found") call <- match.call() rat <- max(y, na.rm = TRUE)/min(y, na.rm = TRUE) if(length(unique(y[!is.na(y)])) >= numUnique) { results <- optim(init, manlyLik, x = y[!is.na(y)], neg = TRUE, method = method, lower = lim[1], upper = lim[2]) out <- list(lambda = results$par, est = manly(y, results$par)) if(length(unique(out$est)) == 1 | results$convergence > 0) out <- list(lambda = NA, est = y) } else out <- list(lambda = NA, est = y) out$n <- sum(!is.na(y)) out$skewness <- e1071::skewness(y, na.rm = TRUE) out$summary <- summary(y) out$ratio <- max(y, na.rm = TRUE)/min(y, na.rm = TRUE) out$class <- class class(out) <- "expoTrans" out } expoTrans.numeric <- function(y, na.rm = TRUE, init = 0, lim = c(-4, 4), method = "Brent", numUnique = 3, ...) { requireNamespaceQuietStop("e1071") if(any(is.na(y)) & !na.rm) stop("missing data found") call <- match.call() rat <- max(y, na.rm = TRUE)/min(y, na.rm = TRUE) if(length(unique(y[!is.na(y)])) >= numUnique) { results <- optim(init, manlyLik, x = y[!is.na(y)], neg = TRUE, method = method, lower = lim[1], upper = lim[2]) out <- list(lambda = results$par, est = manly(y, results$par)) if(length(unique(out$est)) == 1 | results$convergence > 0) out <- list(lambda = NA, est = y) } else out <- list(lambda = NA, est = y) out$n <- sum(!is.na(y)) out$skewness <- e1071::skewness(y, na.rm = TRUE) out$summary <- summary(y) out$ratio <- max(y, na.rm = TRUE)/min(y, na.rm = TRUE) out$class <- class class(out) <- "expoTrans" out } print.expoTrans <- function(x, digits = max(3L, getOption("digits") - 3L), ...) { cat("Exponential Transformation\n\n") cat(x$n, "data points used to estimate Lambda\n\n") cat("Input data summary:\n") print(x$summary) cat("\nLargest/Smallest:", signif(x$ratio, digits), "\n") cat("Sample Skewness:", signif(x$skewness, digits), "\n") if(!is.na(x$lambda)) { cat("\nEstimated Lambda:", signif(x$lambda, digits), "\n") } else cat("\nLambda could not be estimated; no transformation is applied\n") cat("\n") invisible(x) } predict.expoTrans <- function(object, newdata, ...) { if(!is.vector(newdata) || !is.numeric(newdata)) stop("newdata should be a numeric vector") if(is.na(object$lambda)) { out <- newdata } else out <- manly(newdata, object$lambda) out } manly <- function(x, lambda) if(lambda == 0) x else (exp(lambda*x) - 1)/lambda manlyLik <- function(lambda, x, neg = FALSE) { y <- manly(x, lambda) v <- var(y, na.rm = TRUE) L1 <- lambda * sum(x, na.rm= TRUE) L2 <- .5 * sum(y - mean(y, na.rm = TRUE))/v L3 <- sum(!is.na(x)) * log(sqrt(2*pi)*sqrt(v)) out <- L1 - L2 - L3 if(!is.finite(out) | is.na(out)) out <- .Machine$double.xmax if(neg) -out else out } caret/R/plotClassProbs.R0000644000175000017500000000412012453550064015323 0ustar rbalintrbalintplotClassProbs <- function(object, plotType = "histogram", useObjects = FALSE, ...) { obsLevels <- levels(object$obs) stackProbs <- melt(object, id.vars = c("obs", "model", "object", "dataType"), measure.vars = if(length(obsLevels) == 2) obsLevels[1] else obsLevels) names(stackProbs)[names(stackProbs) == "variable"] <- "Class" names(stackProbs)[names(stackProbs) == "value"] <- "Probability" names(stackProbs)[names(stackProbs) == "obs"] <- "Observed" stackProbs$Observed <- paste("Data:", as.character(stackProbs$Observed)) stackProbs$Class <- paste("Prob:", as.character(stackProbs$Class)) keepVars <- "Observed" if(length(unique(stackProbs$dataType)) > 1) keepVars <- c(keepVars, "dataType") if(length(unique(stackProbs$model)) > 1) keepVars <- c(keepVars, "model") if(any(names(object) == "object") & useObjects) { if(length(unique(stackProbs$object)) > 1) keepVars <- c(keepVars, "object") } if(plotType == "histogram") { form <- if(length(obsLevels) == 2) { form <- if(length(keepVars) > 0) paste("~ Probability|", paste(keepVars, collapse = "*")) else "~ Probability" form <- as.formula(form) out <- histogram(form, data = stackProbs, xlab = paste("Probability of", obsLevels[1]), ...) } else { form <- if(length(keepVars) > 0) paste("~ Probability|Class*", paste(keepVars, collapse = "*")) else "~ Probability|Class" form <- as.formula(form) out <- histogram(form, data = stackProbs, ...) } } else { keepVars <- keepVars[keepVars != "Observed"] form <- if(length(keepVars) > 0) paste("~ Probability|", paste(keepVars, collapse = "*")) else "~ Probability" form <- as.formula(form) out <- densityplot(form, data = stackProbs, groups = Observed, ...) } out } caret/R/roc.R0000644000175000017500000000205112453550064013135 0ustar rbalintrbalintroc <- function(data, class, dataGrid = TRUE, gridLength = 100, positive = levels(class)[1]) { warning("This function is deprecated a of 1/3/12. The computations now utilize the pROC package. This function will be removed in a few releases.") if(!is.character(positive) | length(positive) != 1) stop("positive argument should be a single character value") if(!(positive %in% levels(class))) stop("wrong level specified") if(length(levels(class)) != 2) stop("wrong number of levels") if(dataGrid) cutoffDF <- data.frame(value = sort(unique(data))) else cutoffDF <- data.frame(value = seq( from = min(data, na.rm = TRUE), to = max(data, na.rm = TRUE), length = gridLength)) numCuts <- dim(cutoffDF)[1] out <- matrix(NA, ncol = 3, nrow = numCuts + 1) out[2:(numCuts + 1), ] <- t(apply(cutoffDF, 1, rocPoint, x = data, y = class, positive = positive)) out[1, ] <- c(NA, 1, 0) colnames(out) <- c("cutoff", "sensitivity", "specificity") out } caret/NAMESPACE0000644000175000017500000002536412520743335013261 0ustar rbalintrbalintuseDynLib(caret) import(foreach, methods, plyr, reshape2, ggplot2, lattice, nlme, BradleyTerry2) importFrom(car, powerTransform, yjPower) importFrom(stats, prcomp, hclust) export(anovaScores, avNNet, avNNet.default, bag, bag.default, bagControl, bagEarth, bagEarth.default, bagEarth.formula, bagEarthStats, bagFDA, bagFDA.default, bagFDA.formula, best, BoxCoxTrans, calibration, caretFuncs, caretGA, caretSA, caretSBF, caretTheme, cforestStats, checkConditionalX, checkInstall, checkResamples, classDist, classDist.default, cluster, cluster.resamples, compare_models, confusionMatrix, confusionMatrix.train, contr.dummy, contr.ltfr, createDataPartition, createFolds, createModel, createMultiFolds, createResample, createTimeSlices, ctreeBag, defaultSummary, densityplot.rfe, densityplot.train, dotPlot, downSample, dummyVars, dummyVars.default, expandParameters, expoTrans, extractPrediction, extractProb, featurePlot, filterVarImp, findCorrelation, findLinearCombos, flatTable, gafs_initial, gafs_lrSelection, gafs_rwSelection, gafs_tourSelection, gafs_uCrossover, gafs_spCrossover, gafs_raMutation, gafs, gafs.default, gafsControl, gamFormula, gamFuncs, gamScores, getModelInfo, getTrainPerf, ggplot.rfe, ggplot.train, hasTerms, histogram.rfe, icr, icr.default, icr.formula, index2vec, ipredStats, knn3, knn3.formula, knn3.matrix, knn3.data.frame, knn3Train, knnreg, knnreg.data.frame, knnreg.default, knnreg.matrix, knnregTrain, ldaBag, ldaFuncs, ldaSBF, lift, lmFuncs, lmSBF, lrFuncs, LPH07_1, LPH07_2, maxDissim, MeanSD, minDiss, mnLogLoss, modelCor, modelLookup, nbBag, nbFuncs, nbSBF, nearZeroVar, nzv, negPredValue, negPredValue.default, negPredValue.table, nnetBag, nullModel, nullModel.default, oneSE, panel.calibration, panel.lift, panel.lift2, panel.needle, parallelplot.resamples, pcaNNet, pcaNNet.default, pickSizeBest, pickSizeTolerance, pickVars, plot.gafs, plot.rfe, plot.train, plotClassProbs, plotObsVsPred, plsBag, plsda, plsda.default, posPredValue, posPredValue.default, posPredValue.table, postResample, predict.avNNet, predict.bag, predict.bagEarth, predict.bagFDA, predict.classDist, predict.dummyVars, predict.gafs, predict.icr, predict.knn3, predict.knnreg, predict.list, predict.nullModel, predict.pcaNNet, predict.plsda, predict.preProcess, predict.splsda, predict.train, predictionFunction, predictors, preProcess, preProcess.default, print.train, probFunction, progress, R2, resampleHist, resamples, resamples.default, resampleSummary, resampleWrapper, rfe, rfe.default, rfeControl, rfeIter, rfFuncs, rfGA, rfSA, rfSBF, rfStats, RMSE, safs_initial, safs_perturb, safs_prob, safs, safs.default, safsControl, sbf, sbf.default, sbf.formula, sbfControl, sbfIter, sensitivity, sensitivity.default, sensitivity.table, SLC14_1, SLC14_2, sortImp, spatialSign, spatialSign.data.frame, spatialSign.default, spatialSign.matrix, specificity, specificity.default, specificity.table, splsda, splsda.default, stripplot.rfe, sumDiss, summary.bagEarth, summary.bagFDA, svmBag, tolerance, train, train.default, train.formula, trainControl, treebagFuncs, treebagGA, treebagSA, treebagSBF, twoClassSim, twoClassSummary, upSample, var_seq, varImp, varImp.bagEarth, varImp.bagFDA, varImp.plsda, well_numbered, xyplot.rfe ) S3method(confusionMatrix, default) S3method(confusionMatrix, train) S3method(spatialSign, default) S3method(enumLC, default) S3method(train, default) S3method(plsda, default) S3method(splsda, default) S3method(bagEarth, default) S3method(bagEarth, formula) S3method(bagFDA, default) S3method(bagFDA, formula) S3method(preProcess, default) S3method(pcaNNet, default) S3method(avNNet, default) S3method(sensitivity, default) S3method(specificity, default) S3method(posPredValue, default) S3method(negPredValue, default) S3method(rfe, default) S3method(classDist, default) S3method(knnreg, default) S3method(knnreg, matrix) S3method(knnreg, data.frame) S3method(knnreg, formula) S3method(sbf, default) S3method(nullModel, default) S3method(resamples, default) S3method(icr, default) S3method(bag, default) S3method(dummyVars, default) S3method(BoxCoxTrans, default) S3method(cluster, default) S3method(expoTrans, default) S3method(calibration, formula) S3method(lift, formula) S3method(train, formula) S3method(pcaNNet, formula) S3method(avNNet, formula) S3method(rfe, formula) S3method(sbf, formula) S3method(icr, formula) S3method(varImp, train) S3method(varImp, rpart) S3method(varImp, randomForest) S3method(varImp, gbm) S3method(varImp, classbagg) S3method(varImp, regbagg) S3method(varImp, pamrtrained) S3method(varImp, lm) S3method(varImp, glm) S3method(varImp, mvr) S3method(varImp, earth) S3method(varImp, bagEarth) S3method(varImp, bagFDA) S3method(varImp, RandomForest) S3method(varImp, rfe) S3method(varImp, sbf) S3method(varImp, dsa) S3method(varImp, plsda) S3method(varImp, fda) S3method(varImp, multinom) S3method(varImp, cubist) S3method(varImp, RRF) S3method(varImp, PART) S3method(varImp, JRip) S3method(varImp, C5.0) S3method(varImp, nnet) S3method(varImp, glmnet) S3method(varImp, gam) S3method(varImp, gafs) S3method(varImp, safs) S3method(densityplot, train) S3method(histogram, train) S3method(stripplot, train) S3method(xyplot, train) S3method(xyplot, lift) S3method(xyplot, calibration) S3method(densityplot, rfe) S3method(histogram, rfe) S3method(stripplot, rfe) S3method(xyplot, rfe) S3method(densityplot, sbf) S3method(histogram, sbf) S3method(densityplot, resamples) S3method(bwplot, resamples) S3method(dotplot, resamples) S3method(splom, resamples) S3method(xyplot, resamples) S3method(parallelplot,resamples) S3method(prcomp, resamples) S3method(cluster, resamples) S3method(densityplot, diff.resamples) S3method(bwplot, diff.resamples) S3method(levelplot, diff.resamples) S3method(dotplot, diff.resamples) S3method(plot, train) S3method(plot, varImp.train) S3method(plot, rfe) S3method(plot, prcomp.resamples) S3method(plot, lift) S3method(plot, calibration) S3method(plot, gafs) S3method(plot, safs) S3method(confusionMatrix, train) S3method(confusionMatrix, rfe) S3method(confusionMatrix, sbf) S3method(print, train) S3method(print, varImp.train) S3method(print, confusionMatrix) S3method(print, confusionMatrix.train) S3method(print, confusionMatrix.rfe) S3method(print, confusionMatrix.sbf) S3method(print, knn3) S3method(print, plsda) S3method(print, splsda) S3method(print, bagEarth) S3method(print, bagFDA) S3method(print, summary.bagEarth) S3method(print, summary.bagFDA) S3method(print, preProcess) S3method(print, pcaNNet) S3method(print, avNNet) S3method(print, rfe) S3method(print, classDist) S3method(print, knnreg) S3method(print, sbf) S3method(print, nullModel) S3method(print, resamples) S3method(print, summary.resamples) S3method(print, diff.resamples) S3method(print, summary.diff.resamples) S3method(print, icr) S3method(print, bag) S3method(print, summary.bag) S3method(print, prcomp.resamples) S3method(print, dummyVars) S3method(print, BoxCoxTrans) S3method(print, lift) S3method(print, calibration) S3method(print, expoTrans) S3method(print, gafs) S3method(print, safs) S3method(predict, plsda) S3method(predict, splsda) S3method(predict, knn3) S3method(predict, bagEarth) S3method(predict, bagFDA) S3method(predict, preProcess) S3method(predict, pcaNNet) S3method(predict, avNNet) S3method(predict, list) S3method(predict, train) S3method(predict, classDist) S3method(predict, knnreg) S3method(predict, nullModel) S3method(predict, sbf) S3method(predict, icr) S3method(predict, bag) S3method(predict, rfe) S3method(predict, dummyVars) S3method(predict, BoxCoxTrans) S3method(predict, expoTrans) S3method(predict, gafs) S3method(predict, safs) S3method(summary, bagEarth) S3method(summary, bagFDA) S3method(summary, train) S3method(residuals, train) S3method(format, bagEarth) S3method(predictors, train) S3method(predictors, terms) S3method(predictors, formula) S3method(predictors, list) S3method(predictors, default) S3method(predictors, rfe) S3method(predictors, sbf) S3method(predictors, gafs) S3method(predictors, safs) S3method(confusionMatrix, table) S3method(sensitivity, table) S3method(specificity, table) S3method(posPredValue, table) S3method(negPredValue, table) S3method(posPredValue, matrix) S3method(negPredValue, matrix) S3method(as.table, confusionMatrix) S3method(as.matrix, confusionMatrix) S3method(summary, resamples) S3method(summary, bag) S3method(diff, resamples) S3method(sort, resamples) S3method(summary, diff.resamples) S3method(update, train) S3method(update, rfe) S3method(update, gafs) S3method(update, safs) S3method(fitted, train) S3method(residuals, train) S3method(levels, train) S3method(ggplot, train) S3method(ggplot, rfe) S3method(oob_pred, train) S3method(oob_pred, rfe) S3method(oob_pred, sbf) S3method(oob_pred, list) S3method(gafs, default) S3method(safs, default) S3method(trim, train) S3method(spatialSign, matrix) S3method(spatialSign, data.frame) S3method(knn3, matrix) S3method(knn3, data.frame) S3method(knn3, formula) caret/vignettes/0000755000175000017500000000000012633002545014034 5ustar rbalintrbalintcaret/vignettes/caret.Rnw0000644000175000017500000003665212453550065015643 0ustar rbalintrbalint% \VignetteIndexEntry{ A Short Introduction to the caret Package} % \VignetteDepends{caret} % \VignettePackage{caret} \documentclass[12pt]{article} \usepackage{colortbl} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{Sweave} \SweaveOpts{keep.source=TRUE} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\codeheading}[1]{\mbox{\color{darkblue}\texttt{#1}}} \newcommand{\code}[1]{\mbox{\footnotesize\color{darkblue}\texttt{#1}}} \newcommand{\pkg}[1]{{\fontseries{b}\selectfont #1}} \renewcommand{\pkg}[1]{{\textsf{#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \providecommand{\SetAlgoLined}{\SetLine} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0truein} \setlength{\parskip}{0.10truein} \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontshape=sl,formatcom=\color{darkblue}} \fvset{listparameters={\setlength{\topsep}{0pt}}} \renewenvironment{Schunk}{\vspace{\topsep}}{\vspace{\topsep}} \fvset{fontsize=\footnotesize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{The {\tt caret} Package} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{A Short Introduction to the \pkg{caret} Package} \author{Max Kuhn \\ max.kuhn@pfizer.com} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \thispagestyle{empty} \vspace{.2in} \renewcommand{\baselinestretch}{1} <>= library(caret) library(mlbench) data(Sonar) library(pROC) library(pls) options(useFancyQuotes = FALSE) getInfo <- function(what = "Suggests") { text <- packageDescription("caret")[what][[1]] text <- gsub("\n", ", ", text, fixed = TRUE) text <- gsub(">=", "$\\\\ge$", text, fixed = TRUE) eachPkg <- strsplit(text, ", ", fixed = TRUE)[[1]] eachPkg <- gsub(",", "", eachPkg, fixed = TRUE) #out <- paste("\\\\pkg{", eachPkg[order(tolower(eachPkg))], "}", sep = "") #paste(out, collapse = ", ") length(eachPkg) } @ The \pkg{caret} package (short for {\bf{\color{blue}{c}}}lassification {\bf{\color{blue}{a}}}nd {\bf{\color{blue}{re}}}gression {\bf{\color{blue}{t}}}raining) contains functions to streamline the model training process for complex regression and classification problems. The package utilizes a number of R packages but tries not to load them all at package start-up\footnote{By adding formal package dependencies, the package startup time can be greatly decreased}. The package ``suggests'' field includes \Sexpr{getInfo("Suggests")} packages. \pkg{caret} loads packages as needed and assumes that they are installed. Install \pkg{caret} using <>= install.packages("caret", dependencies = c("Depends", "Suggests")) @ to ensure that all the needed packages are installed. The {\bf main help pages} for the package are at: \begin{center} \url{http://caret.r-forge.r-project.org/} \end{center} Here, there are extended examples and a large amount of information that previously found in the package vignettes. \pkg{caret} has several functions that attempt to streamline the model building and evaluation process, as well as feature selection and other techniques. One of the primary tools in the package is the \code{train} function which can be used to \begin{itemize} \item evaluate, using resampling, the effect of model tuning parameters on performance \item choose the ``optimal'' model across these parameters \item estimate model performance from a training set \end{itemize} More formally: \begin{center} \includegraphics[clip, width = .9\textwidth]{train_algo} \end{center} There are options for customizing almost every step of this process (e.g. resampling technique, choosing the optimal parameters etc). To demonstrate this function, the Sonar data from the \pkg{mlbench} package will be used. The Sonar data consist of \Sexpr{nrow(Sonar)} data points collected on \Sexpr{ncol(Sonar)-1} predictors. The goal is to predict the two classes (\texttt{M} for metal cylinder or \texttt{R} for rock). First, we split the data into two groups: a training set and a test set. To do this, the \code{createDataPartition} function is used: <>= library(caret) library(mlbench) data(Sonar) set.seed(107) inTrain <- createDataPartition(y = Sonar$Class, ## the outcome data are needed p = .75, ## The percentage of data in the ## training set list = FALSE) ## The format of the results ## The output is a set of integers for the rows of Sonar ## that belong in the training set. str(inTrain) @ Bt default, \code{createDataPartition} does a stratified random split of the data. To partition the data: <>= training <- Sonar[ inTrain,] testing <- Sonar[-inTrain,] nrow(training) nrow(testing) @ To tune a model using Algorithm \ref{A:tune}, the \code{train} function can be used. More details on this function can be found at: \begin{center} \url{http://caret.r-forge.r-project.org/training.html} \end{center} Here, a partial least squares discriminant analysis (PLSDA) model will be tuned over the number of PLS components that should be retained. The most basic syntax to do this is: <>= plsFit <- train(Class ~ ., data = training, method = "pls", ## Center and scale the predictors for the training ## set and all future samples. preProc = c("center", "scale")) @ However, we would probably like to customize it in a few ways: \begin{itemize} \item expand the set of PLS models that the function evaluates. By default, the function will tune over three values of each tuning parameter. \item the type of resampling used. The simple bootstrap is used by default. We will have the function use three repeats of 10--fold cross--validation. \item the methods for measuring performance. If unspecified, overall accuracy and the Kappa statistic are computed. For regression models, root mean squared error and $R^2$ are computed. Here, the function will be altered to estimate the area under the ROC curve, the sensitivity and specificity \end{itemize} To change the candidate values of the tuning parameter, either of the \code{tuneLength} or \code{tuneGrid} arguments can be used. The \code{train} function can generate a candidate set of parameter values and the \code{tuneLength} argument controls how many are evaluated. In the case of PLS, the function uses a sequence of integers from 1 to \code{tuneLength}. If we want to evaluate all integers between 1 and 15, setting \code{tuneLength = 15} would achieve this. The \code{tuneGrid} argument is used when specific values are desired. A data frame is used where each row is a tuning parameter setting and each column is a tuning parameter. An example is used below to illustrate this. The syntax for the model would then be: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > plsFit <- train(Class ~ ., + data = training, + method = "pls", + \textcolor{red}{tuneLength = 15}, + preProc = c("center", "scale")) \end{Verbatim} To modify the resampling method, a \code{trainControl} function is used. The option \code{method} controls the type of resampling and defaults to \code{"boot"}. Another method, \code{"repeatedcv"}, is used to specify repeated $K$--fold cross--validation (and the argument \code{repeats} controls the number of repetitions). $K$ is controlled by the \code{number} argument and defaults to 10. The new syntax is then: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > \textcolor{red}{ctrl <- trainControl(method = "repeatedcv",} + \textcolor{red}{ repeats = 3)} > plsFit <- train(Class ~ ., + data = training, + method = "pls", + tuneLength = 15, + \textcolor{red}{trControl = ctrl}, + preProc = c("center", "scale")) \end{Verbatim} Finally, to choose different measures of performance, additional arguments are given to \code{trainControl}. The \code{summaryFunction} argument is used to pas in a function that takes the observed and predicted values and estimate some measure of performance. Two such functions are already included in the package: \code{defaultSummary} and \code{twoClassSummary}. The latter will compute measures specific to two--class problems, such as the area under the ROC curve, the sensitivity and specificity. Since the ROC curve is based on the predicted class probabilities (which are not computed automatically), another option is required. The \code{classProbs = TRUE} option is used to include these calculations. Lastly, the function will pick the tuning parameters associated with the best results. Since we are using custom performance measures, the criterion that should be optimized must also be specified. In the call to \code{train}, we can use \code{metric = "ROC"} to do this. The final model fit would then be: \begin{Verbatim}[fontshape=sl,formatcom=\color{darkblue},fontsize=\footnotesize,commandchars=\\\{\}] > ctrl <- trainControl(method = "repeatedcv", + repeats = 3, + \textcolor{red}{ classProbs = TRUE}, + \textcolor{red}{ summaryFunction = twoClassSummary}) > plsFit <- train(Class ~ ., + data = training, + method = "pls", + tuneLength = 15, + trControl = ctrl, + \textcolor{red}{metric = "ROC"}, + preProc = c("center", "scale")) \end{Verbatim} <>= ctrl <- trainControl(method = "repeatedcv", repeats = 3, classProbs = TRUE, summaryFunction = twoClassSummary) set.seed(123) plsFit <- train(Class ~ ., data = training, method = "pls", tuneLength = 15, trControl = ctrl, metric = "ROC", preProc = c("center", "scale")) @ <>= plsFit @ In this output the grid of results are the average resampled estimates of performance. The note at the bottom tells the user that \Sexpr{plsFit$bestTune$.ncomp} PLS components were found to be optimal. Based on this value, a final PLS model is fit to the whole data set using this specification and this is the model that is used to predict future samples. The package has several functions for visualizing the results. One method for doing this is the \code{plot} function for \code{train} objects. The command \code{plot(plsFit)} produced the results seen in Figure \ref{F:pls} and shows the relationship between the resampled performance values and the number of PLS components. \setkeys{Gin}{width=.65\textwidth} \begin{figure} \begin{center} <>= trellis.par.set(caretTheme()) print(plot(plsFit)) @ \caption{ \code{plot(plsFit)} shows the relationship between the number of PLS components and the resampled estimate of the area under the ROC curve. } \label{F:pls} \end{center} \end{figure} To predict new samples, \code{predict.train} can be used. For classification models, the default behavior is to calculated the predicted class. Using the option \code{type = "prob"} can be used to compute class probabilities from the model. For example: <>= plsClasses <- predict(plsFit, newdata = testing) str(plsClasses) plsProbs <- predict(plsFit, newdata = testing, type = "prob") head(plsProbs) @ \pkg{caret} contains a function to compute the confusion matrix and associated statistics for the model fit: <>= confusionMatrix(data = plsClasses, testing$Class) @ To fit an another model to the data, \code{train} can be invoked with minimal changes. Lists of models available can be found at: \begin{center} \url{http://caret.r-forge.r-project.org/modelList.html} \url{http://caret.r-forge.r-project.org/bytag.html} \end{center} For example, to fit a regularized discriminant model to these data, the following syntax can be used: <>= ## To illustrate, a custom grid is used rdaGrid = data.frame(gamma = (0:4)/4, lambda = 3/4) set.seed(123) rdaFit <- train(Class ~ ., data = training, method = "rda", tuneGrid = rdaGrid, trControl = ctrl, metric = "ROC") rdaFit rdaClasses <- predict(rdaFit, newdata = testing) confusionMatrix(rdaClasses, testing$Class) @ How do these models compare in terms of their resampling results? The \code{resamples} function can be used to collect, summarize and contrast the resampling results. Since the random number seeds were initialized to the same value prior to calling \code{train}, the same folds were used for each model. To assemble them: <>= resamps <- resamples(list(pls = plsFit, rda = rdaFit)) summary(resamps) @ There are several functions to visualize these results. For example, a Bland--Altman type plot can be created using \code{xyplot(resamps, what = "BlandAltman")} (see Figure \ref{F:BA}). The results look similar. Since, for each resample, there are paired results a paired $t$--test can be used to assess whether there is a difference in the average resampled area under the ROC curve. The \code{diff.resamples} function can be used to compute this: <>= diffs <- diff(resamps) summary(diffs) @ Based on this analysis, the difference between the models is \Sexpr{round(diffs$statistics$ROC[[1]]$estimate, 3)} ROC units (the RDA model is slightly higher) and the two--sided $p$--value for this difference is \Sexpr{format.pval(diffs$statistics$ROC[[1]]$p.value)}. \setkeys{Gin}{width=.65\textwidth} \begin{figure} \begin{center} <>= plotTheme <- caretTheme() plotTheme$plot.symbol$col <- rgb(.2, .2, .2, .5) plotTheme$plot.symbol$pch <- 16 trellis.par.set(plotTheme) print(xyplot(resamps, what = "BlandAltman")) @ \caption{A Bland--Altman plot of the resampled ROC values produced using \code{xyplot(resamps, what = "BlandAltman")}. } \label{F:BA} \end{center} \end{figure} \end{document}