Functional Componentwise Classifier
Trains the functional componentwise classifier
compclassf.train (dataf, labels, subset,
to.equalize = TRUE,
to.reduce = TRUE,
classifier.type = c("ddalpha", "maxdepth", "knnaff", "lda", "qda"),
...)dataf |
list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively |
labels |
list of output labels of the functional observations |
subset |
an optional vector specifying a subset of observations to be used in training the classifier. |
to.equalize |
Adjust the data to have equal (the largest) argument interval. |
to.reduce |
If the data spans a subspace only, project on it (by PCA). |
classifier.type |
the classifier which is used on the transformed space. The default value is 'ddalpha'. |
... |
additional parameters, passed to the classifier, selected with parameter |
The finite-dimensional space is directly constructed from the observed values. Delaigle, Hall and Bathia (2012) consider (almost) all sets of discretization points that have a given cardinality.
The usual classifiers are then trained on the constructed finite-dimensional space.
Trained functional componentwise classifier
Delaigle, A., Hall, P., and Bathia, N. (2012). Componentwise classification and clustering of functional data. Biometrika 99 299–313.
compclassf.classify for classification using functional componentwise classifier,
ddalphaf.train to train the functional DD-classifier,
dataf.* for functional data sets included in the package.
## 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 = compclassf.train (learn, labels, classifier.type = "ddalpha") classified = compclassf.classify(c, test) print(unlist(classified)) ## End(Not run)
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