Description Usage Arguments Value Author(s) References Examples

View source: R/SVC_selection.R

Function to set up control parameters for
`SVC_selection`

. The underlying Gaussian Process-based
SVC model is defined in `SVC_mle`

. `SVC_selection`

then jointly selects fixed and random effects of the GP-based
SVC model using a penalized maximum likelihood estimation (PMLE).
In this function, one can set the parameters for the PMLE and
its optimization procedures (Dambon et al., 2021, <arXiv:2101.01932>).

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`IC.type` |
( |

`method` |
( |

`r.lambda` |
( |

`n.lambda` |
( |

`n.init` |
( |

`n.iter` |
( |

`CD.conv` |
( |

`hessian` |
( |

`adaptive` |
( |

`parallel` |
( |

`optim.args` |
( |

A list of control parameters for SVC selection.

Jakob Dambon

Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J.,
Lang, M. (2017).
*mlrMBO: A Modular Framework for Model-Based Optimization of
Expensive Black-Box Functions*,
ArXiv preprint https://arxiv.org/abs/1703.03373

Dambon, J. A., Sigrist, F., Furrer, R. (2021).
*Joint Variable Selection of both Fixed and Random Effects for
Gaussian Process-based Spatially Varying Coefficient Models*,
ArXiv preprint https://arxiv.org/abs/2101.01932

1 2 3 4 5 6 7 | ```
# Initializing parameters and switching logLik to FALSE
selection_control <- SVC_selection_control(
CD.conv = list(N = 20L, delta = 1e-06, logLik = FALSE)
)
# or
selection_control <- SVC_selection_control()
selection_control$CD.conv$logLik <- FALSE
``` |

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