# Run the whole archetypoid analysis with the functional multivariate Frobenius norm This function executes the entire procedure involved in the functional archetypoid analysis. Firstly, the initial vector of archetypoids is obtained using the functional archetypal algorithm and finally, the optimal vector of archetypoids is returned. ```r do_fada_multiv(subset, numArchoid, numRep, huge, compare = FALSE, PM, method = "adjbox", prob) ``` ## Arguments - `subset`: Data to obtain archetypes. In fadalara this is a subset of the entire data frame. - `numArchoid`: Number of archetypes/archetypoids. - `numRep`: For each `numArch`, run the archetype algorithm `numRep` times. - `huge`: Penalization added to solve the convex least squares problems. - `compare`: Boolean argument to compute the robust residual sum of squares to compare these results with the ones provided by `do_fada_robust`. - `PM`: Penalty matrix obtained with `eval.penalty`. - `method`: Method to compute the outliers. So far the only option allowed is 'adjbox' for using adjusted boxplots for skewed distributions. The use of tolerance intervals might also be explored in the future for the multivariate case. - `prob`: If `compare=TRUE`, probability with values in [0,1]. ## Returns A list with the following elements: * cases: Final vector of archetypoids. * alphas: Alpha coefficients for the final vector of archetypoids. * rss: Residual sum of squares corresponding to the final vector of archetypoids. * rss_rob: If `compare_robust=TRUE`, this is the residual sum of squares using the robust Frobenius norm. Otherwise, NULL. * resid: Vector of residuals. * outliers: Outliers. ## Examples ```r ## Not run: library(fda) ?growth str(growth) hgtm <- growth$hgtm hgtf <- growth$hgtf[,1:39] # Create array: nvars <- 2 data.array <- array(0, dim = c(dim(hgtm), nvars)) data.array[,,1] <- as.matrix(hgtm) data.array[,,2] <- as.matrix(hgtf) rownames(data.array) <- 1:nrow(hgtm) colnames(data.array) <- colnames(hgtm) str(data.array) # Create basis: nbasis <- 10 basis_fd <- create.bspline.basis(c(1,nrow(hgtm)), nbasis) PM <- eval.penalty(basis_fd) # Make fd object: temp_points <- 1:nrow(hgtm) temp_fd <- Data2fd(argvals = temp_points, y = data.array, basisobj = basis_fd) X <- array(0, dim = c(dim(t(temp_fd$coefs[,,1])), nvars)) X[,,1] <- t(temp_fd$coef[,,1]) X[,,2] <- t(temp_fd$coef[,,2]) # Standardize the variables: Xs <- X Xs[,,1] <- scale(X[,,1]) Xs[,,2] <- scale(X[,,2]) suppressWarnings(RNGversion("3.5.0")) set.seed(2018) res_fada <- do_fada_multiv(subset = Xs, numArchoid = 3, numRep = 5, huge = 200, compare = FALSE, PM = PM, method = "adjbox") str(res_fada) ## End(Not run) ``` ## References Epifanio, I., Functional archetype and archetypoid analysis, 2016. **Computational Statistics and Data Analysis** 104 , 24-34, [https://doi.org/10.1016/j.csda.2016.06.007](https://doi.org/10.1016/j.csda.2016.06.007) ## See Also `stepArchetypesRawData_funct_multiv`, `archetypoids_funct_multiv` ## Author(s) Guillermo Vinue, Irene Epifanio

do_fada_multiv function