ddalpha.test: Test DD-Classifier

View source: R/ddalpha.test.r

ddalpha.testR Documentation

Test DD-Classifier


Trains DD-classifier on the learning sequence of the data and tests it on the testing sequence.


ddalpha.test(learn, test, ...)



the learning sequence of the data. Matrix containing training sample where each of n rows is one object of the training sample where first d entries are inputs and the last entry is output (class label).


the testing sequence. Has the same format as learn


additional parameters passed to ddalpha.train



the part of incorrectly classified data


the number of correctly classified objects


the number of incorrectly classified objects


the number of classified objects


the number of ignored objects (outside the convex hull of the learning data)


the number of objects in the testing sequence


training time

See Also

ddalpha.train to train the DD-classifier, ddalpha.classify for classification using DD-classifier, ddalpha.getErrorRateCV and ddalpha.getErrorRatePart to get error rate of the DD-classifier on particular data.


# Generate a bivariate normal location-shift classification task
# containing 200 training objects and 200 to test with
class1 <- mvrnorm(200, c(0,0), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
class2 <- mvrnorm(200, c(2,2), 
                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
trainIndices <- c(1:100)
testIndices <- c(101:200)
propertyVars <- c(1:2)
classVar <- 3
trainData <- rbind(cbind(class1[trainIndices,], rep(1, 100)), 
                   cbind(class2[trainIndices,], rep(2, 100)))
testData <- rbind(cbind(class1[testIndices,], rep(1, 100)), 
                  cbind(class2[testIndices,], rep(2, 100)))
data <- list(train = trainData, test = testData)

# Train 1st DDalpha-classifier (default settings) 
# and get the classification error rate
stat <- ddalpha.test(data$train, data$test)
cat("1. Classification error rate (defaults): ", 
    stat$error, ".\n", sep = "")

# Train 2nd DDalpha-classifier (zonoid depth, maximum Mahalanobis 
# depth classifier with defaults as outsider treatment) 
# and get the classification error rate
stat2 <- ddalpha.test(data$train, data$test, depth = "zonoid", 
                          outsider.methods = "depth.Mahalanobis")
cat("2. Classification error rate (depth.Mahalanobis): ", 
    stat2$error, ".\n", sep = "")

ddalpha documentation built on May 29, 2024, 1:12 a.m.