do_fada_multiv {adamethods}

Run the whole archetypoid analysis with the functional multivariate Frobenius norm

Description

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.

Usage

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].

Value

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.

Author(s)

Guillermo Vinue, Irene Epifanio

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

See Also

stepArchetypesRawData_funct_multiv, archetypoids_funct_multiv

Examples

## 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)