archetypoids_robust: Archetypoid algorithm with the robust Frobenius norm

Description

Robust version of the archetypoid algorithm with the Frobenius form.

Usage

archetypoids_robust(numArchoid, data, huge = 200, ArchObj, prob)

Arguments

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_robust function.

prob

Probability with values in [0,1].

Value

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.

References

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

Run this code

# NOT RUN {
data(mtcars)
data <- mtcars

k <- 3
numRep <- 2
huge <- 200

lass <- stepArchetypesRawData_robust(data = data, numArch = k, 
                                     numRep = numRep, verbose = FALSE, 
                                     saveHistory = FALSE, prob = 0.8)

res <- archetypoids_robust(k, data, huge, ArchObj = lass, 0.8)
str(res)    
res$cases
res$rss                                                           
              
# }