# comp.kern: Multivariate kernel density estimation for compositional data In Compositional: Compositional Data Analysis

 Multivariate kernel density estimation for compositional data R Documentation

## Multivariate kernel density estimation for compositional data

### Description

Multivariate kernel density estimation for compositional data.

### Usage

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

### Arguments

 `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.

### Details

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

### Value

A vector with the density estimates calculated for every vector.

### Author(s)

Michail Tsagris.

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

### References

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])