View source: R/gp.functions6.R

gprPredict | R Documentation |

Prediction of GPR model

gprPredict( train = NULL, inputNew = NULL, noiseFreePred = F, hyper = NULL, input = NULL, Y = NULL, mSR = NULL, Cov = NULL, gamma = NULL, nu = NULL, meanModel = 0, mu = 0 )

`train` |
A 'gpr' object obtained from 'gpr' function. Default to NULL. If NULL, learning is done based on the other given arguments; otherwise, prediction is made based on the trained model of class gpr'. |

`inputNew` |
Test input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate. |

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

`hyper` |
The hyperparameters. Default to NULL. If not NULL, then it must be a list with appropriate names. |

`input` |
Input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate. |

`Y` |
Training response. It should be a matrix, where each column is a realisation. It can be a vector if there is only one realisation. |

`mSR` |
Subset size m if Subset of Regressors method is used for prediction. It must be smaller than the total sample size. |

`Cov` |
Covariance function(s) to use. Options are: 'linear', 'pow.ex', 'rat.qu', and 'matern'. Default to 'power.ex'. |

`gamma` |
Power parameter used in powered exponential kernel function. It must be 0<gamma<=2. |

`nu` |
Smoothness parameter of the Matern class. It must be a positive value. |

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

`mu` |
Mean function specified by the user. It must be a vector. Its length must be the same as the sample size, that is, nrow(response). |

A list containing

- pred.mean
Mean of predictions

- pred.sd
Standard deviation of predictions

- newdata
Test input data

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

- ...
Objects of 'gpr' class.

## See examples in vignettes: # vignette("gpr_ex1", package = "GPFDA") # vignette("gpr_ex2", package = "GPFDA") # vignette("co2", package = "GPFDA")

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