fdakma (version 1.1.1)

fdakma-package: Functional Data Analysis: K-Mean Alignment

Description

fdakma jointly performs clustering and alignment of a functional dataset (multidimensional or unidimensional functions).

Details

ll{ Package: fdakma Type: Package Version: 1.1.1 Date: 2014-06-23 License: GPL-3 }

References

Sangalli, L.M., Secchi, P., Vantini, S., Vitelli, V., 2010. "K-mean alignment for curve clustering". Computational Statistics and Data Analysis, 54, 1219-1233.

See Also

kma.compare, kma.similarity, kma.data, kma, kma.show.results

Examples

data(kma.data)

x <- kma.data$x # abscissas
y0 <- kma.data$y0 # evaluations of original functions
y1 <- kma.data$y1 # evaluations of original function first derivatives

# Plot of original functions
matplot(t(x),t(y0), type='l', xlab='x', ylab='orig.func')
title ('Original functions')

# Plot of original function first derivatives
matplot(t(x),t(y1), type='l', xlab='x', ylab='orig.deriv')
title ('Original function first derivatives')


# Example: result of kma function with 2 clusters, 
# allowing affine transformation for the abscissas
# and considering 'd1.pearson' as similarity.method.
fdakma_example <- kma (
  x=x, y0=y0, y1=y1, n.clust = 2, 
  warping.method = 'affine', 
  similarity.method = 'd1.pearson',
  center.method = 'k-means', 
  seeds = c(1,21)
)

kma.show.results(fdakma_example)

names(fdakma_example)

# Labels assigned to each function
fdakma_example$labels

# Total shifts and dilations applied to the original 
# abscissa to obtain the aligned abscissa
fdakma_example$shift
fdakma_example$dilation