Classify using Functional DD-Classifier
Classifies data using the functional DD-classifier.
ddalphaf.classify(ddalphaf, objectsf, subset, ...) ## S3 method for class 'ddalphaf' predict(object, objectsf, subset, ...)
ddalphaf, object |
Functional DD-classifier (obtained by |
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 |
List containing class labels.
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).
ddalphaf.train to train the functional DDα-classifier.
## 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)
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