Multivariate kernel density estimation for compositional data | R Documentation |

Multivariate kernel density estimation for compositional data.

comp.kern(x, type= "alr", h = NULL, thumb = "silverman")

`x` |
A matrix with Euclidean (continuous) data. |

`type` |
The type of trasformation used, either the additive log-ratio ("alr"), the isometric log-ratio ("ilr") or the pivot coordinate ("pivot") transformation. |

`h` |
The bandwidh value. It can be a single value, which is turned into a vector and then into a diagonal matrix, or a vector which is turned into a diagonal matrix. If it is NULL, then you need to specify the "thumb" argument below. |

`thumb` |
Do you want to use a rule of thumb for the bandwidth parameter? If no, leave the "h" NULL and put "estim" for maximum likelihood cross-validation, "scott" or "silverman" for Scott's and Silverman's rules of thumb respectively. |

The multivariate kernel density estimate is calculated with a (not necssarily given) bandwidth value.

A vector with the density estimates calculated for every vector.

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives.

The kedd R package.

M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.

B.W. Silverman (1986). Density estimation for statistics and data analysis, pages 76-78.

```
comp.kerncontour, mkde
```

x <- as.matrix(iris[, 1:3]) x <- x / rowSums(x) f <- comp.kern(x)

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