Description Usage Arguments Value References Examples

View source: R/ellipse-functions.R

Draws a contour of constant density at the (1-`alpha`

)100% level for a
bivariate normal distribution using the eigendecomposition of the covariance
matrix. This is likely more interesting for learning about the bivariate
normal distribution than as a practical tool, for which other functions
already exist (e.g. `link[graphics]{contour}`

).

1 2 3 4 5 6 7 8 9 10 11 12 |

`mu` |
a vector giving the mean of the bivariate normal distribution. This is the center of the ellipse. |

`Sigma` |
a matrix giving the covariance matrix of the bivariate normal
distribution. Either |

`eig` |
the eigenvalues and eigenvectors of the covariance matrix. This
should be of the same form as the output of |

`xl` |
a vector giving the lower and upper limits of the x-axis for
plotting. If |

`yl` |
a vector giving the lower and upper limits of the y-axis for
plotting. If |

`axes` |
logical. If |

`center` |
logical. If |

`lim.adj` |
a value giving an adjustment to the x-axis and y-axis limits
computed if either |

`alpha` |
a value giving the value of alpha to be used when computing the
contour. Contours are drawn at the |

`...` |
other arguments to be passed to the graphing functions. |

None

Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed). Pearson Prentice Hall.

1 2 3 4 | ```
mu <- c(-1,8)
Sigma <- matrix(c(3,2,2,4), ncol = 2)
# Draw a 90% contour
bvNormalContour(mu = mu, Sigma = Sigma, alpha = 0.10)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.