View source: R/comp.kerncontour.R

Contour plot of the kernel density estimate in S^2 | R Documentation |

Contour plot of the kernel density estimate in *S^2*.

comp.kerncontour(x, type = "alr", n = 50, 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, mix.compnorm.contour, bivt.contour, compnorm.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)

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