1\name{ddalpha.test} 2\alias{ddalpha.test} 3\title{ 4Test DD-Classifier 5} 6\description{ 7Trains DD-classifier on the learning sequence of the data and tests it on the testing sequence. 8} 9\usage{ 10ddalpha.test(learn, test, ...) 11} 12\arguments{ 13 \item{learn}{ 14the learning sequence of the data. Matrix containing training sample where each of \eqn{n} rows is one object of the training sample where first \eqn{d} entries are inputs and the last entry is output (class label). 15} 16 \item{test}{ 17the testing sequence. Has the same format as \code{learn} 18} 19 \item{\dots}{ 20additional parameters passed to \code{\link{ddalpha.train}} 21} 22} 23 24\value{ 25 26 \item{error}{ 27 the part of incorrectly classified data 28 } 29 \item{correct}{ 30 the number of correctly classified objects 31 } 32 \item{incorrect}{ 33 the number of incorrectly classified objects 34 } 35 \item{total}{ 36 the number of classified objects 37 } 38 \item{ignored}{ 39 the number of ignored objects (outside the convex hull of the learning data) 40 } 41 \item{n}{ 42 the number of objects in the testing sequence 43 } 44 \item{time}{ 45 training time 46 } 47 48} 49 50 51\seealso{ 52\code{\link{ddalpha.train}} to train the DD-classifier, 53\code{\link{ddalpha.classify}} for classification using DD-classifier, 54\code{\link{ddalpha.getErrorRateCV}} and \code{\link{ddalpha.getErrorRatePart}} to get error rate of the DD-classifier on particular data. 55} 56\examples{ 57 58# Generate a bivariate normal location-shift classification task 59# containing 200 training objects and 200 to test with 60class1 <- mvrnorm(200, c(0,0), 61 matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE)) 62class2 <- mvrnorm(200, c(2,2), 63 matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE)) 64trainIndices <- c(1:100) 65testIndices <- c(101:200) 66propertyVars <- c(1:2) 67classVar <- 3 68trainData <- rbind(cbind(class1[trainIndices,], rep(1, 100)), 69 cbind(class2[trainIndices,], rep(2, 100))) 70testData <- rbind(cbind(class1[testIndices,], rep(1, 100)), 71 cbind(class2[testIndices,], rep(2, 100))) 72data <- list(train = trainData, test = testData) 73 74# Train 1st DDalpha-classifier (default settings) 75# and get the classification error rate 76stat <- ddalpha.test(data$train, data$test) 77cat("1. Classification error rate (defaults): ", 78 stat$error, ".\n", sep = "") 79 80# Train 2nd DDalpha-classifier (zonoid depth, maximum Mahalanobis 81# depth classifier with defaults as outsider treatment) 82# and get the classification error rate 83stat2 <- ddalpha.test(data$train, data$test, depth = "zonoid", 84 outsider.methods = "depth.Mahalanobis") 85cat("2. Classification error rate (depth.Mahalanobis): ", 86 stat2$error, ".\n", sep = "") 87 88} 89% Add one or more standard keywords, see file 'KEYWORDS' in the 90% R documentation directory. 91\keyword{ benchmark } 92