stepArchetypesRawData_funct: Archetype algorithm to raw data with the functional Frobenius...

In adamethods: Archetypoid Algorithms and Anomaly Detection

Description

This is a slight modification of stepArchetypesRawData to use the functional archetype algorithm with the Frobenius norm.

Usage

stepArchetypesRawData_funct(data, numArch, numRep = 3, 
                            verbose = TRUE, saveHistory = FALSE, PM)

Arguments

data	Data to obtain archetypes.
numArch	Number of archetypes to compute, from 1 to numArch.
numRep	For each numArch, run the archetype algorithm numRep times.
verbose	If TRUE, the progress during execution is shown.
saveHistory	Save execution steps.
PM	Penalty matrix obtained with eval.penalty.

Value

A list with the archetypes.

Author(s)

Irene Epifanio

References

Cutler, A. and Breiman, L., Archetypal Analysis. Technometrics, 1994, 36(4), 338-347, https://doi.org/10.2307/1269949

Epifanio, I., Functional archetype and archetypoid analysis, 2016. Computational Statistics and Data Analysis 104, 24-34, https://doi.org/10.1016/j.csda.2016.06.007

Eugster, M.J.A. and Leisch, F., From Spider-Man to Hero - Archetypal Analysis in R, 2009. Journal of Statistical Software 30(8), 1-23, https://doi.org/10.18637/jss.v030.i08

Examples

## Not run: 
library(fda)
?growth
str(growth)
hgtm <- t(growth$hgtm)
# Create basis:
basis_fd <- create.bspline.basis(c(1,ncol(hgtm)), 10)
PM <- eval.penalty(basis_fd)
# Make fd object:
temp_points <- 1:ncol(hgtm)
temp_fd <- Data2fd(argvals = temp_points, y = growth$hgtm, basisobj = basis_fd)
data_archs <- t(temp_fd$coefs)

lass <- stepArchetypesRawData_funct(data = data_archs, numArch = 3, 
                                    numRep = 5, verbose = FALSE, 
                                    saveHistory = FALSE, PM)
str(lass)   
length(lass[[1]])
class(lass[[1]])  
class(lass[[1]][[5]])                                 

## End(Not run)                                         

adamethods documentation built on Aug. 4, 2020, 5:08 p.m.