1d:T789,# Alphas and RSS of every set of archetypoids 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. ```r do_alphas_rss(data, subset, huge, k_subset, rand_obs, alphas_subset, type_alg = "ada", PM, prob) ``` ## Arguments - `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'`. ## Returns 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. ## Examples ```r 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) ``` ## See Also `archetypoids_norm_frob` ## Author(s) Guillermo Vinue

do_alphas_rss function