archetypoids_funct_robust {adamethods}  R Documentation 
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, 195208. 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) ## End(Not run)