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 <alex@nr.no>
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