Test DD-Classifier
Trains DD-classifier on the learning sequence of the data and tests it on the testing sequence.
ddalpha.test(learn, test, ...)
learn |
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). |
test |
the testing sequence. Has the same format as |
... |
additional parameters passed to |
error |
the part of incorrectly classified data |
correct |
the number of correctly classified objects |
incorrect |
the number of incorrectly classified objects |
total |
the number of classified objects |
ignored |
the number of ignored objects (outside the convex hull of the learning data) |
n |
the number of objects in the testing sequence |
time |
training time |
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 = "")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.