do_ada_robust: Run the whole robust archetypoid analysis with the robust...
In adamethods: Archetypoid Algorithms and Anomaly Detection

Description Usage Arguments Value Author(s) References See Also Examples

This function executes the entire procedure involved in the robust archetypoid analysis. Firstly, the initial vector of archetypoids is obtained using the robust archetypal algorithm and finally, the optimal vector of robust archetypoids is returned.

do_ada_robust(subset, numArchoid, numRep, huge, prob, compare = FALSE,
              vect_tol = c(0.95, 0.9, 0.85), alpha = 0.05, 
              outl_degree = c("outl_strong", "outl_semi_strong", "outl_moderate"),
              method = "adjbox")

`subset`	Data to obtain archetypes. In ADALARA 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.
`prob`	Probability with values in [0,1].
`compare`	Boolean argument to compute the non-robust residual sum of squares to compare these results with the ones provided by `do_ada`.
`vect_tol`	Vector the tolerance values. Default c(0.95, 0.9, 0.85). Needed if `method='toler'`.
`alpha`	Significance level. Default 0.05. Needed if `method='toler'`.
`outl_degree`	Type of outlier to identify the degree of outlierness. Default c("outl_strong", "outl_semi_strong", "outl_moderate"). Needed if `method='toler'`.
`method`	Method to compute the outliers. Options allowed are 'adjbox' for using adjusted boxplots for skewed distributions, and 'toler' for using tolerance intervals.

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_non_rob: If compare=TRUE, this is the residual sum of squares using the non-robust Frobenius norm. Otherwise, NULL.
resid Vector of residuals.
outliers: Outliers.

Guillermo Vinue, 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

stepArchetypesRawData_robust, archetypoids_robust

## Not run: 
library(Anthropometry)
data(mtcars)
#data <- as.matrix(mtcars)
data <- mtcars

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

preproc <- preprocessing(data, stand = TRUE, percAccomm = 1)
suppressWarnings(RNGversion("3.5.0"))
set.seed(2018)
res_ada_rob <- do_ada_robust(preproc$data, k, numRep, huge, 0.8,
                             FALSE, method = "adjbox")
str(res_ada_rob)    

res_ada_rob1 <- do_ada_robust(preproc$data, k, numRep, huge, 0.8,
                             FALSE, vect_tol = c(0.95, 0.9, 0.85), alpha = 0.05, 
                             outl_degree = c("outl_strong", "outl_semi_strong", 
                                             "outl_moderate"),
                             method = "toler")
str(res_ada_rob1)  

## End(Not run)