Contour plot of the alpha multivariate normal in S^2 {Compositional} R Documentation

## Contour plot of the α multivariate normal in S^2

### Description

Contour plot of the α multivariate normal in S^2.

### Usage

```alfa.contour(m, s, a, n = 100, x = NULL, cont.line = FALSE)
```

### Arguments

 `m` The mean vector of the α multivariate normal model. `s` The covariance matrix of the α multivariate normal model. `a` The value of a for the α-transformation. `n` The number of grid points to consider over which the density is calculated. `x` This is either NULL (no data) or contains a 3 column matrix with compositional data. `cont.line` Do you want the contour lines to appear? If yes, set this TRUE.

### Details

The α-transformation is applied to the compositional data and then for a grid of points within the 2-dimensional simplex, the density of the α multivariate normal is calculated and the contours are plotted.

### Value

The contour plot of the α multivariate normal appears.

### References

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

```fold.contour, norm.contour, diri.contour, mixnorm.contour, bivt.contour, skewnorm.contour ```

### Examples

```x <- as.matrix(iris[, 1:3])
x <- x / rowSums(x)
a <- a.est(x)\$best
m <- colMeans(alfa(x, a)\$aff)
s <- cov(alfa(x, a)\$aff)
alfa.contour(m, s, a)
```

[Package Compositional version 5.2 Index]