Archetypoid algorithm with the functional robust Frobenius norm
Archetypoid algorithm with the functional robust Frobenius norm to be used with functional data.
archetypoids_funct_robust(numArchoid, data, huge = 200, ArchObj, PM, prob)
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
|
PM |
Penalty matrix obtained with |
prob |
Probability with values in [0,1]. |
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
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
## 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_robust(data = data_archs, numArch = 3,
numRep = 5, verbose = FALSE,
saveHistory = FALSE, PM, prob = 0.8)
afr <- archetypoids_funct_robust(3, data_archs, huge = 200, ArchObj = lass, PM, 0.8)
str(afr)
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