Contour plot of the alpha multivariate normal in S^2 | R Documentation |
Contour plot of the α multivariate normal in S^2.
alfa.contour(m, s, a, n = 100, x = NULL, cont.line = FALSE)
m |
The mean vector of the α multivariate normal model. |
s |
The covariance matrix of the α multivariate normal model. |
a |
The value of a for the α-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. |
The α-transformation is applied to the compositional data and then for a grid of points within the 2-dimensional simplex, the density of the α multivariate normal is calculated and the contours are plotted.
The contour plot of the α multivariate normal appears.
Michail Tsagris and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Christos Adam pada4m4@gmail.com.
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
folded.contour, compnorm.contour, diri.contour, mix.compnorm.contour, bivt.contour, skewnorm.contour
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)
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