RPEnsemble

Help for package RPEnsemble const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {RPEnsemble} Contents RPEnsemble-package Other.classifier R RPChoose RPChooseSS RPEnsembleClass RPGenerate RPModel RPParallel RPalpha Version: 0.5 Date: 2021-02-23 Title: Random Projection Ensemble Classification Author: Timothy I. Cannings and Richard J. Samworth Maintainer: Timothy I. Cannings <timothy.cannings@ed.ac.uk> Description: Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment. Depends: R (≥ 3.4.0), MASS, parallel Imports: class, stats License: GPL-3 URL: https://arxiv.org/abs/1504.04595 , https://www.maths.ed.ac.uk/~tcannings/ NeedsCompilation: no Packaged: 2021-02-24 12:36:23 UTC; tc Repository: CRAN Date/Publication: 2021-02-24 13:40:08 UTC Random Projection Ensemble Classification Description Implements the methodology of Cannings and Samworth (2017). The random projection ensemble classifier is a very general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment. Details RPChoose chooses the projection from a block of size B2 that minimises an estimate of the test error (see Cannings and Samworth, 2017, Section 3), and classifies the training and test sets using the base classifier on the projected data. RPParallel makes many calls to RPChoose in parallel. RPalpha chooses the best empirical value of alpha (see Cannings and Samworth, 2017, Section 5.1). RPEnsembleClass combines the results of many base classifications to classify the test set. The method can be used with any base classifier, any test error estimate and any distribution of the random projections. This package provides code for the following options: Classifiers – linear discriminant analysis, quadratic discriminant analysis and the k-nearest neighbour classifier. Error estimates – resubstitution and leave-one-out, we also provide code for the sample-splitting method described in Cannings and Samworth (2017, Section 7) (this can be done by setting estmethod = samplesplit ). Projection distribution – Haar, Gaussian or axis-aligned projections. The package provides the option to add your own base classifier and estimation method, this can be done by editing the code in the function Other.classifier . Moreover, one could edit the RPGenerate function to generate projections from different distributions. Author(s) Timothy I. Cannings and Richard J. Samworth Maintainer: Timothy I. Cannings <timothy.cannings@ed.ac.uk> References Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035 Examples #generate data from Model 1 set.seed(101) Train <- RPModel(2, 50, 100, 0.5) Test <- RPModel(2, 100, 100, 0.5) #Classify the training and test set for B1 = 10 independent projections, each #one carefully chosen from a block of size B2 = 10, using the "knn" base #classifier and the leave-one-out test error estimate Out <- RPParallel(XTrain = Train$x, YTrain = Train$y, XTest = Test$x, d = 2, B1 = 10, B2 = 10, base = "knn", projmethod = "Haar", estmethod = "loo", splitsample = FALSE, k = seq(1, 25, by = 3), clustertype = "Default") #estimate the class 1 prior probability phat <- sum(Train$y == 1)/50 #choose the best empirical value of the voting threshold alpha alphahat <- RPalpha(RP.out = Out, Y = Train$y, p1 = phat) #combine the base classifications Class <- RPEnsembleClass(RP.out = Out, n = 50, n.test = 100, p1 = phat, alpha = alphahat) #calculate the error mean(Class != Test$y) #Code for sample splitting version of the above #n.val <- 25 #s <- sample(1:50,25) #OutSS <- RPParallel(XTrain = Train$x[-s,], YTrain = Train$y[-s], #XVal = Train$x[s,], YVal = Train$y[s], XTest = Test$x, d = 2, #B1 = 50, B2 = 10, base = "knn", projmethod = "Haar", estmethod = "samplesplit", #k = seq(1,13, by = 2), clustertype = "Fork", cores = 1) #alphahatSS <- RPalpha(RP.out = OutSS, Y = Train$y[s], p1 = phat) #ClassSS <- RPEnsembleClass(RP.out = OutSS, n.val = 25, n.test = 100, #p1 = phat, samplesplit = TRUE, alpha = alphahatSS) #mean(ClassSS != Test$y) The users favourite classifier Description User defined code to convert existing R code for classification to the correct format Usage Other.classifier(x, grouping, xTest, CV, ...) Arguments x An n by p matrix containing the training dataset grouping A vector of length n containing the training data classes xTest An n.test by p test dataset CV If TRUE perform cross-validation (or otherwise) to classify training set. If FALSE , classify test set. ... Optional arguments e.g. tuning parameters Details User editable code for your choice of base classifier. Value class a vector of classes of the training or test set Author(s) Timothy I. Cannings and Richard J. Samworth References Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035 A rotation matrix Description The 100 by 100 rotation matrix used in Model 2 in Cannings and Samworth (2017). Usage data(R) Format A 100 by 100 rotation matrix References Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959–1035 Examples data(R) head(R%*%t(R)) Chooses projection Description Chooses a the best projection from a set of size B2 based on a test error estimate, then classifies the training and test sets using the chosen projection. Usage RPChoose(XTrain, YTrain, XTest, d, B2 = 10, base = "LDA", k = c(3,5), projmethod = "Haar", estmethod = "training", ...) Arguments XTrain An n by p matrix containing the training data feature vectors YTrain A vector of length n of the classes (either 1 or 2) of the training data XTest An n.test by p matrix of the test data d The lower dimension of the image space of the projections B2 The block size base The base classifier one of "knn","LDA","QDA" or "other" k The options for k if base is "knn" projmethod Either "Haar" , "Gaussian" or "axis" estmethod Method for estimating the test errors to choose the projection: either training error "training" or leave-one-out "loo" ... Optional further arguments if base = "other" Details Randomly projects the the data B2 times. Chooses the projection yielding the smallest estimate of the test error. Classifies the training set (via the same method as estmethod ) and test set using the chosen projection. Value Returns a vector of length n + n.test : the first n entries are the estimated classes of the training set, the l