classification {SpatFD}

Classification Function for Functional Data

Description

This function classifies new functional data based on PCA results from training data.

Usage

  classification(data.train.pca, new.basis, k, distance, mcov = NULL)

Arguments

data.train.pca

A list of PCA results from training data.

new.basis

Basis object from the test data

k

Number of nearest neighbors to consider for classification.

distance

Type of distance to use (e.g., "euclidean", "mahalanobis").

mcov

Optional covariance matrices for Mahalanobis distance.

Value

The predicted class for the new data.

Examples

  
  data(vowels)
  #### Create parameters and names for the data.
  p = 228 ; nelec = 21 ; nvow = 5
  names_vowels = c("a","e","i","o","u")
  n.basis<-c(14,13,12,13,11)
  s4.gfdata = gfdata(data=vowels,p=p,names=names_vowels,coords=vowels_coords,nbasis=n.basis)
  # Create train and test data
  s4.sep=gfd_clasif_data(s4.gfdata, 0.8,seed = 2910)
  s4.train=s4.sep$train
  s4.test=s4.sep$test
  
  # Classification
  cla<-classification(data.train.pca = s4.train,
                     new.basis=s4.test[[1]]$data_fd[[1]],
                     k=4,
                     distance='euclidean',
                     mcov = mcov
  )