frobenius_norm_funct_robust: Functional robust Frobenius norm
In adamethods: Archetypoid Algorithms and Anomaly Detection

Description Usage Arguments Details Value Author(s) References Examples

View source: R/frobenius_norm_funct_robust.R

Computes the functional robust Frobenius norm.

1	frobenius_norm_funct_robust(m, PM, prob)

`m`	Data matrix with the residuals. This matrix has the same dimensions as the original data matrix.
`PM`	Penalty matrix obtained with `eval.penalty`.
`prob`	Probability with values in [0,1].

Residuals are vectors. If there are p variables (columns), for every observation there is a residual that there is a p-dimensional vector. If there are n observations, the residuals are an n times p matrix.

Real number.

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

library(fda)
mat <- matrix(1:9, nrow = 3)
fbasis <- create.fourier.basis(rangeval = c(1, 32), nbasis = 3)
PM <- eval.penalty(fbasis)
frobenius_norm_funct_robust(mat, PM, 0.8)