`MVNORM.Rd`

This sampler function for BAMLSS uses estimated `parameters`

and the Hessian
information to create samples from a multivariate normal distribution. Note that smoothing
variance uncertainty is not accounted for, therefore, the resulting credible intervals
are most likely too narrow.

sam_MVNORM(x, y = NULL, family = NULL, start = NULL, n.samples = 500, hessian = NULL, ...) MVNORM(x, y = NULL, family = NULL, start = NULL, n.samples = 500, hessian = NULL, ...)

x | The |
---|---|

y | The model response, as returned from function |

family | A bamlss family object, see |

start | A named numeric vector containing possible starting values, the names are based on
function |

n.samples | Sets the number of samples that should be generated. |

hessian | The Hessian matrix that should be used. Note that the row and column names
must be the same as the names of the |

… | Arguments passed to function |

Function `MVNORM()`

returns samples of parameters. The samples are provided as a
`mcmc`

matrix.

## Simulated data example illustrating ## how to call the sampler function. ## This is done internally within ## the setup of function bamlss(). d <- GAMart() f <- num ~ s(x1, bs = "ps") bf <- bamlss.frame(f, data = d, family = "gaussian") ## First, find starting values with optimizer. o <- with(bf, opt_bfit(x, y, family))#> AICc 433.9735 logPost -207.648 logLik -210.132 edf 6.7482 eps 3.8020 iteration 1 #> AICc 430.5003 logPost -196.349 logLik -210.132 edf 5.0558 eps 0.0000 iteration 2 #> AICc 430.5003 logPost -196.349 logLik -210.132 edf 5.0558 eps 0.0000 iteration 2 #> elapsed time: 0.29sec#>plot(samps)