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Contour plot of the kernel density estimate in
comp.kerncontour(x, type = "alr", n = 100)
A matrix with the compositional data. It has to be a 3 column matrix.
This is either "alr" or "ilr", corresponding to the additive and the isometric log-ratio transformation respectively.
The number of grid points to consider, over which the density is calculated.
A ternary diagram with the points and the kernel contour lines.
The alr or the ilr transformation are applied to the compositional data. Then, the optimal bandwidth using maximum likelihood cross-validation is chosen. The multivariate normal kernel density is calculated for a grid of points. Those points are the points on the 2-dimensional simplex. Finally the contours are plotted.
M.P. Wand and M.C. Jones (1995). Kernel smoothing, CrC Press.
Aitchison J. (1986). The statistical analysis of compositional data. Chapman \& Hall.
# NOT RUN {
x <- as.matrix(iris[, 1:3])
x <- x / rowSums(x)
comp.kerncontour(x, type = "alr", n = 20)
comp.kerncontour(x, type = "ilr", n = 20)
# }
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