View source: R/mgp.functions.R

mgprPredict | R Documentation |

Prediction of MGPR model

mgprPredict( train, DataObs = NULL, DataNew, noiseFreePred = F, meanModel = NULL, mu = 0 )

`train` |
A 'mgpr' object obtained from 'mgpr' function. If NULL, predictions are made based on DataObs informed by the user. |

`DataObs` |
List of observed data. Default to NULL. If NULL, predictions are made based on the trained data (included in the object of class 'mgpr') used for learning. |

`DataNew` |
List of test input data. |

`noiseFreePred` |
Logical. If TRUE, predictions will be noise-free. |

`meanModel` |
Type of mean function applied to all outputs. It can be - 0
Zero mean function for each output. - 1
Constant mean function to be estimated for each output. - 't'
Linear model for the mean function of each output. - 'avg'
The average across replications is used as the mean function of each output. This can only be used if there are more than two realisations observed at the same input values.
Default to 0. If argument 'mu' is specified, then 'meanModel' will be set to 'userDefined'. |

`mu` |
Vector of concatenated mean function values defined by the user. Default to NULL. |

A list containing

- pred.mean
Mean of predictions for the test set.

- pred.sd
Standard deviation of predictions for the test set.

- noiseFreePred
Logical. If TRUE, predictions are noise-free.

## See examples in vignette: # vignette("mgpr", package = "GPFDA")

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