Contour plot of the alpha multivariate normal in S^2 {Compositional} | R Documentation |
Contour plot of the \alpha
multivariate normal in S^2
Description
Contour plot of the \alpha
multivariate normal in S^2
.
Usage
alfa.contour(m, s, a, n = 100, x = NULL, cont.line = FALSE)
Arguments
m |
The mean vector of the |
s |
The covariance matrix of the |
a |
The value of a for the |
n |
The number of grid points to consider over which the density is calculated. |
x |
This is either NULL (no data) or contains a 3 column matrix with compositional data. |
cont.line |
Do you want the contour lines to appear? If yes, set this TRUE. |
Details
The \alpha
-transformation is applied to the compositional data and then for a grid of points within the 2-dimensional simplex, the density of the \alpha
multivariate normal is calculated and the contours are plotted.
Value
The contour plot of the \alpha
multivariate normal appears.
Author(s)
Michail Tsagris and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Christos Adam pada4m4@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 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
See Also
folded.contour, compnorm.contour, diri.contour, mix.compnorm.contour, bivt.contour, skewnorm.contour
Examples
x <- as.matrix(iris[, 1:3])
x <- x / rowSums(x)
a <- a.est(x)$best
m <- colMeans(alfa(x, a)$aff)
s <- cov(alfa(x, a)$aff)
alfa.contour(m, s, a)