Robust and classical variation matrix
Estimates the variation matrix with robust methods.
variation(x, method = "robustPivot")
x |
data frame or matrix with positive entries |
method |
method used for estimating covariances. See details. |
The variation matrix is estimated for a given compositional data set.
Instead of using the classical standard deviations the miniminm covariance estimator
is used (covMcd) is used when parameter robust is set to TRUE.
For method robustPivot forumala 5.8. of the book (see second reference) is used. Here
robust (mcd-based) covariance estimation is done on pivot coordinates.
Method robustPairwise uses a mcd covariance estimation on pairwise log-ratios.
Methods Pivot (see second reference) and Pairwise (see first reference)
are the non-robust counterparts.
Naturally, Pivot and Pairwise gives the same results, but
the computational time is much less for method Pairwise.
The (robust) variation matrix.
Karel Hron, Matthias Templ
Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman \& Hall Ltd., London (UK). 416p.
#' Filzmoser, P., Hron, K., Templ, M. (2018) Applied Compositional Data Analysis. Springer, Cham.
data(expenditures) variation(expenditures) # default is method "robustPivot" variation(expenditures, method = "Pivot") variation(expenditures, method = "robustPairwise") variation(expenditures, method = "Pairwise") # same results as Pivot
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.