Description Usage Arguments Details Value Note References See Also Examples

Calculates the negative log-likelihood (excluding all the constant terms) as described in `reference 1`

.

1 2 | ```
neg_log_l(hyperparam, p_quant, p_qual, lvs_qual, n_lvs_qual, dim_z,
X_quant, X_qual, Y, min_eig, k, M)
``` |

`hyperparam` |
Hyperparameters of the LVGP model |

`p_quant` |
Number of quantative variables |

`p_qual` |
Number of qualitative variables |

`lvs_qual` |
Levels of each qualitative variable |

`n_lvs_qual` |
Number of levels of each qualitative variable |

`dim_z` |
Dimensionality of latent variables, usually 1 or 2 |

`X_quant` |
Input data of quantative variables |

`X_qual` |
Input data of qualitative variables |

`Y` |
Vector containing the outputs of data points |

`min_eig` |
The smallest eigen value that the correlation matrix is allowed to have, which determines the nugget added to the correlation matrix. |

`k` |
Number of data points, |

`M` |
Vector of ones with length |

`LVGP_fit`

calls this function as its optimization objective function.

The negative log-likelihood (excluding all the constant terms) value.

This function is **NOT** exported once the package is loaded.

"A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors", Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (arXiv)

`LVGP_fit`

to see how a GP model can be fitted to a training dataset.

`LVGP_predict`

to use the fitted LVGP model for prediction.

`LVGP_plot`

to plot the features of the fitted model.

1 | ```
# see the examples in the documentation of the function LVGP_fit.
``` |

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