stepArchetypesRawData_funct_multiv_robust: Archetype algorithm to raw data with the functional...

In adamethods: Archetypoid Algorithms and Anomaly Detection

Description

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

Usage

stepArchetypesRawData_funct_multiv_robust(data, numArch, numRep = 3, 
                            verbose = TRUE, saveHistory = FALSE, PM, prob, nbasis, nvars)

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.
prob	Probability with values in [0,1].
nbasis	Number of basis.
nvars	Number of variables.

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

Moliner, J. and Epifanio, I., Robust multivariate and functional archetypal analysis with application to financial time series analysis, 2019. Physica A: Statistical Mechanics and its Applications 519, 195-208. https://doi.org/10.1016/j.physa.2018.12.036

Examples

## Not run: 
library(fda)
?growth
str(growth)
hgtm <- growth$hgtm
hgtf <- growth$hgtf[,1:39]

# Create array:
nvars <- 2
data.array <- array(0, dim = c(dim(hgtm), nvars))
data.array[,,1] <- as.matrix(hgtm)
data.array[,,2] <- as.matrix(hgtf)
rownames(data.array) <- 1:nrow(hgtm)
colnames(data.array) <- colnames(hgtm)
str(data.array)

# Create basis:
nbasis <- 10
basis_fd <- create.bspline.basis(c(1,nrow(hgtm)), nbasis)
PM <- eval.penalty(basis_fd)
# Make fd object:
temp_points <- 1:nrow(hgtm)
temp_fd <- Data2fd(argvals = temp_points, y = data.array, basisobj = basis_fd)

X <- array(0, dim = c(dim(t(temp_fd$coefs[,,1])), nvars))
X[,,1] <- t(temp_fd$coef[,,1]) 
X[,,2] <- t(temp_fd$coef[,,2])

# Standardize the variables:
Xs <- X
Xs[,,1] <- scale(X[,,1])
Xs[,,2] <- scale(X[,,2])

lass <- stepArchetypesRawData_funct_multiv_robust(data = Xs, numArch = 3, 
                                                  numRep = 5, verbose = FALSE, 
                                                  saveHistory = FALSE, PM, prob = 0.8, 
                                                  nbasis, nvars)
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.