Contour plot of the alpha multivariate normal in S^2 {Compositional}

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 \alpha multivariate normal model.

s

The covariance matrix of the \alpha multivariate normal model.

a

The value of a for the \alpha-transformation.

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 [e-mail] and Christos Adam [e-mail].

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)