Contour plot of the kernel density estimate in S^2 {Compositional} | R Documentation |
Contour plot of the kernel density estimate in S^2.
comp.kerncontour(x, type = "alr", n = 100, cont.line = FALSE)
x |
A matrix with the compositional data. It has to be a 3 column matrix. |
type |
This is either "alr" or "ilr", corresponding to the additive and the isometric log-ratio transformation respectively. |
n |
The number of grid points to consider, over which the density is calculated. |
cont.line |
Do you want the contour lines to appear? If yes, set this TRUE. |
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.
A ternary diagram with the points and the kernel contour lines.
Michail Tsagris and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Christos Adam pada4m4@gmail.com.
M.P. Wand and M.C. Jones (1995). Kernel smoothing, CrC Press.
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
diri.contour, mixnorm.contour, bivt.contour, norm.contour
x <- as.matrix(iris[, 1:3]) x <- x / rowSums(x) comp.kerncontour(x, type = "alr", n = 20) comp.kerncontour(x, type = "ilr", n = 20)