Description Usage Arguments Value Author(s)

This uses a conditional Bayes factor (CBF) to update a model in a linear system given a current model and other information in a spatial GEV model. Note that it is agnostic to which part of the framework (location, precision, scale) you are updating.

1 | ```
gev.update.M(Y, X, M, alpha, lambda, D, beta.0, Omega.0)
``` |

`Y` |
The current dependent variable, calculated relative to the linear plus random effect terms of the given component. |

`X` |
The matrix of covariates |

`M` |
The current model. A subset of (1, ..., p) where p is the number of columns in X |

`alpha` |
The precision term of the Gaussian process for this component of the model |

`lambda` |
The length term of the Gaussian process for this component of the model |

`D` |
The distance matrix used in the Gaussian process |

`beta.0` |
The prior mean on the linear regression terms |

`Omega.0` |
The prior covariance on the linear regression terms |

This returns an updated model, which is a vector that is a subset of (1, ..., p).

Alex Lenkoski <[email protected]>

spatial.gev.bma documentation built on May 29, 2017, 9:10 a.m.

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