The alpha-transformation {Compositional} | R Documentation |
The \alpha
-transformation
Description
The \alpha
-transformation.
Usage
alfa(x, a, h = TRUE)
alef(x, a)
Arguments
x |
A matrix with the compositional data. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to
be greater than 0. If |
h |
A boolean variable. If is TRUE (default value) the multiplication with the Helmert sub-matrix will take place.
When |
Details
The \alpha
-transformation is applied to the compositional data. The command "alef" is the same as
"alfa(x, a, h = FALSE)", but reurns a different element as well and is necessary for the functions a.est
, a.mle
and alpha.mle
.
Value
A list including:
sa |
The logarithm of the Jacobian determinant of the |
sk |
If the "alef" was called, this will return the sum of the |
aff |
The |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>.
References
Tsagris M. and Stewart C. (2022). A Review of Flexible Transformations for Modeling Compositional Data. In Advances and Innovations in Statistics and Data Science, pp. 225–234. https://link.springer.com/chapter/10.1007/978-3-031-08329-7_10
Tsagris Michail and Stewart Connie (2020). A folded model for compositional data analysis. Australian and New Zealand Journal of Statistics, 62(2): 249-277. https://arxiv.org/pdf/1802.07330.pdf
Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
See Also
alfainv, pivot, alfa.profile, alfa.tune
a.est, alpha.mle, alr, bc, fp, green
Examples
library(MASS)
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
y1 <- alfa(x, 0.2)$aff
y2 <- alfa(x, 1)$aff
rbind( colMeans(y1), colMeans(y2) )
y3 <- alfa(x, 0.2)$aff
dim(y1) ; dim(y3)
rowSums(y1)
rowSums(y3)