Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/MultivariateFuncs.R

The mean of the distribution may be a d-dimensional vector. The covariance matrix should be given as a d x d non-negative definite matrix if supplied with the parameter `cov`

. It can also be given, if cov is missing, by a vector `sd`

for the marginal standard deviations and a scalar `rho`

implying a constant correlation between all the marginals. If the covariance structure of the marginals is supplied in this way, `sd`

should be a d-dimensional vector,
and `rho`

should be scalar. The dimension `d`

may be inferred from other arguments.
The code of this function is a mere wrapper for the function with the same name from the library `mvtnorm`

. It was written to provide compatibility with S-Plus, hence the long list of parameters

1 2 3 4 5 |

`n` |
Number of samples generated by |

`x` |
n x d numeric matrix, each row giving a point at which the density is computed |

`mean` |
d- dimensional vector giving the mean of the distribution |

`cov` |
d x d matrix giving the covariance matrix of the distribution |

`sd` |
d- vector of the marginal standard deviations |

`rho` |
number giving the constant correlation when the covariance matrix is given by its diagonal and the parameter |

`d` |
dimension of the distribution |

`sigma` |
used for compatibility with S-Plus |

`log` |
boolean for logarithmic scale |

`method` |
String giving the method SVD or Choleski used |

`dmvnorm`

,compute multivariate normal density.
`pmvnorm`

compute multivariate normal c.d.f.
`rmvnorm`

generate random samples from the multivariate normal distribution.

A list of the elements

`$x` |
n x d Matrix giving the values where the density is computed |

`$y` |
Vector of length |

Rene Carmona

library `Rmetrics`

and `S-Plus`

manual

1 2 3 4 5 |

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