do_alphas_rss: Alphas and RSS of every set of archetypoids
In adamethods: Archetypoid Algorithms and Anomaly Detection

Description Usage Arguments Value Author(s) See Also Examples

In the ADALARA algorithm, every time that a set of archetypoids is computed using a sample of the data, the alpha coefficients and the associated residual sum of squares (RSS) for the entire data set must be computed.

1 2	do_alphas_rss(data, subset, huge, k_subset, rand_obs, alphas_subset, type_alg = "ada", PM, prob)

`data`	Data matrix with all the observations.
`subset`	Data matrix with a sample of the `data` observations.
`huge`	Penalization added to solve the convex least squares problems.
`k_subset`	Archetypoids obtained from `subset`.
`rand_obs`	Sample observations that form `subset`.
`alphas_subset`	Alpha coefficients related to `k_subset`.
`type_alg`	String. Options are 'ada' for the non-robust multivariate adalara algorithm, 'ada_rob' for the robust multivariate adalara algorithm, 'fada' for the non-robust fda fadalara algorithm and 'fada_rob' for the robust fda fadalara algorithm.
`PM`	Penalty matrix obtained with `eval.penalty`. Needed when `type_alg = 'fada'` or `type_alg = 'fada_rob'`.
`prob`	Probability with values in [0,1]. Needed when `type_alg = 'ada_rob'` or `type_alg = 'fada_rob'`.

A list with the following elements:

rss Real number of the residual sum of squares.
resid_rss Matrix with the residuals.
alphas Matrix with the alpha values.

Guillermo Vinue

archetypoids_norm_frob

data(mtcars)
data <- mtcars
n <- nrow(data)
m <- 10

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

suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
rand_obs_si <- sample(1:n, size = m) 

si <- data[rand_obs_si,]
ada_si <- do_ada(si, k, numRep, huge, FALSE) 

k_si <- ada_si$cases
alphas_si <- ada_si$alphas
colnames(alphas_si) <- rownames(si)     

rss_si <- do_alphas_rss(data, si, huge, k_si, rand_obs_si, alphas_si, "ada")
str(rss_si)