Archetypoid algorithm with the robust Frobenius norm
Robust version of the archetypoid algorithm with the Frobenius form.
archetypoids_robust(numArchoid, data, huge = 200, ArchObj, 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
|
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
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$cases
res$rssPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.