rmvn | R Documentation |

Generates multivariate normal or t random deviates, and evaluates the corresponding log densities.

rmvn(n,mu,V) r.mvt(n,mu,V,df) dmvn(x,mu,V,R=NULL) d.mvt(x,mu,V,df,R=NULL)

`n` |
number of simulated vectors required. |

`mu` |
the mean of the vectors: either a single vector of length |

`V` |
A positive semi definite covariance matrix. |

`df` |
The degrees of freedom for a t distribution. |

`x` |
A vector or matrix to evaluate the log density of. |

`R` |
An optional Cholesky factor of V (not pivoted). |

Uses a â€˜square rootâ€™ of `V`

to transform standard normal deviates to multivariate normal with the correct covariance matrix.

An `n`

row matrix, with each row being a draw from a multivariate normal or t density with covariance matrix `V`

and mean vector `mu`

. Alternatively each row may have a different mean vector if `mu`

is a vector.

For density functions, a vector of log densities.

Simon N. Wood simon.wood@r-project.org

`ldTweedie`

, `Tweedie`

library(mgcv) V <- matrix(c(2,1,1,2),2,2) mu <- c(1,3) n <- 1000 z <- rmvn(n,mu,V) crossprod(sweep(z,2,colMeans(z)))/n ## observed covariance matrix colMeans(z) ## observed mu dmvn(z,mu,V)

mgcv documentation built on March 7, 2023, 8:29 p.m.

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