fdakma (version 1.2.1)

kma.show.results: Auxiliary function plotting results of kma function.

Description

kma.show.results graphically shows the output results of kma function or one of the outuput results of kma.compare.

The function generates the following plots:

- $d$ plots of original functions (one for each of the $d$ dimensions of the input functions -for unidimensional functions $d$ is 1-) with center (if y0 is given as input in kma/kma.compare function).

- $d$ plots of aligned functions with centers (if y0 is given as input in kma/kma.compare function).

- $d$ plots of original function first derivatives with center (if y1 is given as input and the chosen similarity.method concerns function first derivatives).

- $d$ plots of aligned function first derivatives with centers (if y1 is given as input and the chosen similarity.method concerns function first derivatives).

- Plot of warping functions.

- Boxplot of similarity/dissimilarity indexes of original and aligned functions.

Usage

kma.show.results(Result, lwd.functions = 1, lwd.centers = 3)

Arguments

Result

list: output of kma function or one of the outputs of kma.compare functions.

lwd.functions

integer: the desired line width of the curves. Default value is 1.

lwd.centers

integer: the desired line width of the centers. Default value is 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.

Sangalli, L.M., Secchi, P., Vantini, S., 2014. "Analysis of AneuRisk65 data: K-mean Alignment". Electronic Journal of Statistics, Special Section on "Statistics of Time Warpings and Phase Variations", Vol. 8, No. 2, 1891-1904.

See Also

kma, kma.compare, kma.similarity, fdakma, kma.data

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

## Not run: 
# # kma function with 2 clusters, allowing affine 
# # transformation for the abscissas and considering 
# # 'd1.pearson' as similarity.method.
# kma.show.results_example1 <- 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)
# )
# 
# # Example: kma.show.results shows the results of kma function
# kma.show.results(kma.show.results_example1)
# 
# 
# # Example using outputs of kma.compare function
# 
# # Results of kma function with 3 different 
# # numbers of clusters (1,2,3) combined with four alignment
# # methods ('NOalignment' by default, 'shift', 'dilation',
# # 'affine') and considering 'd1.pearson' as similarity.method.
# kma.show.results_example2 <- kma.compare (
#   x=x, y0=y0, y1=y1, n.clust = 1:3, 
#   warping.method = c('affine'), 
#   similarity.method = 'd1.pearson',
#   center.method = 'k-means', 
#   seeds = c(1,21,30),
#   plot.graph=1)
# 
# names (kma.show.results_example2)
# 
# # To see results for kma function with n.clust=2 
# # and warping.method='affine'.
# kma.show.results (kma.show.results_example2$Result.affine[[2]])
# 
# # Labels assigned to each function for the 
# # kma function with n.clust=2 and warping.method='affine'.
# kma.show.results_example2$Result.affine[[2]]$labels
# ## End(Not run)