Contour plot of the alpha multivariate normal in S^2: 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.
Value
The contour plot of the \(\alpha\) multivariate normal appears.
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.
References
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
# NOT RUN {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)
# }