contourmcd | R Documentation |

Draws the contour plot of the probability density of the multivariate Cauchy distribution with 2 variables
with location parameter `mu`

and scatter matrix `Sigma`

.

```
contourmcd(mu, Sigma,
xlim = c(mu[1] + c(-10, 10)*Sigma[1, 1]),
ylim = c(mu[2] + c(-10, 10)*Sigma[2, 2]),
zlim = NULL, npt = 30, nx = npt, ny = npt,
main = "Multivariate Cauchy density",
sub = NULL, nlevels = 10,
levels = pretty(zlim, nlevels), tol = 1e-6, ...)
```

`mu` |
length 2 numeric vector. |

`Sigma` |
symmetric, positive-definite square matrix of order 2. The scatter matrix. |

`xlim` , `ylim` |
x-and y- limits. |

`zlim` |
z- limits. If NULL, it is the range of the values of the density on the x and y values within |

`npt` |
number of points for the discretisation. |

`nx` , `ny` |
number of points for the discretisation among the x- and y- axes. |

`main` , `sub` |
main and sub title, as for |

`nlevels` , `levels` |
arguments to be passed to the |

`tol` |
tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma, for the estimation of the density. see |

`...` |
additional arguments to |

Returns invisibly the probability density function.

Pierre Santagostini, Nizar Bouhlel

N. Bouhlel, D. Rousseau, A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions. Entropy, 24, 838, July 2022. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/e24060838")}

`dmcd`

: probability density of a multivariate Cauchy density

`plotmcd`

: 3D plot of a bivariate Cauchy density.

```
mu <- c(1, 4)
Sigma <- matrix(c(0.8, 0.2, 0.2, 0.2), nrow = 2)
contourmcd(mu, Sigma)
```

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