MCMC simulation around an evmOpt fit

1 2 |

`o` |
a fit |

`priorParameters` |
A list with two components. The first should
be a vector of means, the second should be a covariance matrix
if the penalty/prior is "gaussian" or "quadratic" and a
diagonal precision matrix if the penalty/prior is "lasso", "L1"
or "Laplace". If |

`prop.dist` |
The proposal distribution to use, either multivariate gaussian or a multivariate Cauchy. |

`jump.const` |
Control parameter for the Metropolis algorithm. |

`jump.cov` |
Covariance matrix for proposal distribution of
Metropolis algorithm. This is scaled by |

`iter` |
Number of simulations to generate |

`start` |
Starting values for the chain; if missing, defaults to
the MAP/ML estimates in |

`thin` |
The degree of thinning of the resulting Markov chains. |

`burn` |
The number of initial steps to be discarded. |

`verbose` |
Whether or not to print progress to screen. Defaults
to |

`trace` |
How frequently to talk to the user |

`theCall` |
(internal use only) |

`...` |
ignored |

an object of class `evmSim`

:

`call` |
The call to |

`threshold` |
The threshold above which the model was fit. |

`map` |
The point estimates found by maximum penalized
likelihood and which were used as the starting point for the Markov
chain. This is of class |

`burn` |
The number of steps of the Markov chain that are to be treated as the burn-in and not used in inferences. |

`thin` |
The degree of thinning used. |

`chains` |
The entire Markov chain generated by the Metropolis algorithm. |

`y` |
The response data above the threshold for fitting. |

`seed` |
The seed used by the random number generator. |

`param` |
The remainder of the chain after deleting the burn-in and applying any thinning. |

it is not expected that the user should call this directly

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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