ddalphaf.classify: Classify using Functional DD-Classifier

In ddalpha: Depth-Based Classification and Calculation of Data Depth

Classify using Functional DD-Classifier

Description

Classifies data using the functional DD-classifier.

Usage

ddalphaf.classify(ddalphaf, objectsf, subset, ...)

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

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.

Value

List containing class labels.

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 DDα-classifier.

Examples

## Not run: 
## 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))


## End(Not run)

ddalpha documentation built on March 23, 2022, 9:07 a.m.