ddalpha (version 1.3.16)

ddalphaf.classify: Classify using Functional DD-Classifier

Description

Classifies data using the functional DD-classifier.

Usage

ddalphaf.classify(ddalphaf, objectsf, subset, ...)
# S3 method for ddalphaf
predict(object, objectsf, subset, ...)

Value

List containing class labels.

Arguments

ddalphaf, object

Functional DD-classifier (obtained by ddalphaf.train).

objectsf

list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively

subset

an optional vector specifying a subset of observations to be classified.

...

additional parameters, passed to the classifier, selected with parameter classifier.type in ddalphaf.train.

References

Mosler, K. and Mozharovskyi, P. (2017). Fast DD-classification of functional data. Statistical Papers 58 1055--1089.

Mozharovskyi, P. (2015). Contributions to Depth-based Classification and Computation of the Tukey Depth. Verlag Dr. Kovac (Hamburg).

See Also

ddalphaf.train to train the functional DD\(\alpha\)-classifier.

Examples

if (FALSE) {
## load the Growth dataset
dataf = dataf.growth()

learn = c(head(dataf$dataf, 49), tail(dataf$dataf, 34))
labels= c(head(dataf$labels, 49), tail(dataf$labels, 34)) 
test  = tail(head(dataf$dataf, 59), 10)    # elements 50:59. 5 girls, 5 boys

c = ddalphaf.train (learn, labels, classifier.type = "ddalpha")

classified = ddalphaf.classify(c, test)

print(unlist(classified))

}