# Archetype algorithm to raw data with the functional multivariate robust Frobenius norm This is a slight modification of `stepArchetypesRawData` to use the functional archetype algorithm with the multivariate Frobenius norm. ```r stepArchetypesRawData_funct_multiv_robust(data, numArch, numRep = 3, verbose = TRUE, saveHistory = FALSE, PM, prob, nbasis, nvars) ``` ## Arguments - `data`: Data to obtain archetypes. - `numArch`: Number of archetypes to compute, from 1 to `numArch`. - `numRep`: For each `numArch`, run the archetype algorithm `numRep` times. - `verbose`: If TRUE, the progress during execution is shown. - `saveHistory`: Save execution steps. - `PM`: Penalty matrix obtained with `eval.penalty`. - `prob`: Probability with values in [0,1]. - `nbasis`: Number of basis. - `nvars`: Number of variables. ## Returns A list with the archetypes. ## 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]) lass <- stepArchetypesRawData_funct_multiv_robust(data = Xs, numArch = 3, numRep = 5, verbose = FALSE, saveHistory = FALSE, PM, prob = 0.8, nbasis, nvars) str(lass) length(lass[[1]]) class(lass[[1]]) class(lass[[1]][[5]]) ## End(Not run) ``` ## References Cutler, A. and Breiman, L., Archetypal Analysis. **Technometrics**, 1994, 36(4) , 338-347, [https://doi.org/10.2307/1269949](https://doi.org/10.2307/1269949) 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) Eugster, M.J.A. and Leisch, F., From Spider-Man to Hero - Archetypal Analysis in R, 2009. **Journal of Statistical Software** 30(8) , 1-23, [https://doi.org/10.18637/jss.v030.i08](https://doi.org/10.18637/jss.v030.i08) 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](https://doi.org/10.1016/j.physa.2018.12.036) ## Author(s) Irene Epifanio

stepArchetypesRawData_funct_multiv_robust function