archetypoids_funct: Archetypoid algorithm with the functional Frobenius norm
In adamethods: Archetypoid Algorithms and Anomaly Detection

Description Usage Arguments Value Author(s) References See Also Examples

Archetypoid algorithm with the functional Frobenius norm to be used with functional data.

1	archetypoids_funct(numArchoid, data, huge = 200, ArchObj, PM)

`numArchoid`	Number of archetypoids.
`data`	Data matrix. Each row corresponds to an observation and each column corresponds to a variable. All variables are numeric.
`huge`	Penalization added to solve the convex least squares problems.
`ArchObj`	The list object returned by the `stepArchetypesRawData_funct` function.
`PM`	Penalty matrix obtained with `eval.penalty`.

A list with the following elements:

cases: Final vector of archetypoids.
rss: Residual sum of squares corresponding to the final vector of archetypoids.
archet_ini: Vector of initial archetypoids.
alphas: Alpha coefficients for the final vector of archetypoids.
resid: Matrix with the residuals.

Irene Epifanio

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

archetypoids

## 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)

af <- archetypoids_funct(3, data_archs, huge = 200, ArchObj = lass, PM) 
str(af)                                

## End(Not run)