smvrnorm | R Documentation |

`smvrnorm()`

simulates data from a multivariate normal distribution.

smvrnorm( n = 1, mu, sigma, tol = 1e-06, empirical = FALSE, eispack = FALSE, seed )

`n` |
the number of observations to simulate |

`mu` |
a vector of means |

`sigma` |
a positive-definite symmetric matrix specifying the covariance matrix of the variables. |

`tol` |
tolerance (relative to largest variance) for numerical lack of positive-definiteness in |

`empirical` |
logical. If true, |

`eispack` |
logical. values other than FALSE result in an error |

`seed` |
set an optional seed |

This is a simple port and rename of `mvrnorm()`

from the MASS package. I elect
to plagiarize/port it because the MASS package conflicts with a lot of things in my workflow,
especially `select()`

. This is useful for "informal Bayes" approaches to generating quantities
of interest from a regression model.

The function returns simulated data from a multivariate normal distribution.

B. D. Ripley (1987) *Stochastic Simulation.* Wiley. Page 98.

M1 <- lm(mpg ~ disp + cyl, mtcars) smvrnorm(100, coef(M1), vcov(M1))

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