Description Usage Arguments Value Author(s) References

The returned design is (a low-rank approximation to) the matrix square root
of the implied covariance of the centered MRF. The function stops if
'islands', i.e. regions without any neighbors are found. Regions without
observations have to be removed from the neighborhood matrix and there is
currently no `predict`

-functionality for regions without observations in
the original data.

1 2 |

`x` |
a factor: which observation belongs to which region |

`N` |
the neighborhood (adjacency) matrix: a symmetric matrix with one
column/row for every level of |

`decomposition` |
use a (truncated) spectral decomposition of the implied
prior covariance of |

`tol` |
count singular/eigenvalues smaller than this as zero |

`rankZ` |
how many eigenvectors to retain from the eigen decomposition: either a number > 3 or the proportion of the sum of eigenvalues the retained eigenvectors must represent at least. Defaults to .999. |

a transformed design matrix for the Markov Random Field

Fabian Scheipl

Fahrmeir, L., Lang, S. (2001) Bayesian inference for generalized
additive mixed models based on Markov random field priors. *Applied
Statistics*, **50**(2):201–220.

spikeSlabGAM documentation built on Sept. 21, 2018, 6:37 p.m.

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