stepArchetypesRawData_funct_multiv: 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(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 <- 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(data = Xs, 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.