Gaussian process regression and statistical testing

Plots a gaussian process. Several boolean parameters for modifying the plot. By default plots the data, posterior mean and 95% interval.

1 2 3 4 5 |

`x` |
the gp-object |

`y` |
placeholder variable |

`plotdata` |
plot the data (default) |

`plotmean` |
plot the GP mean (default) |

`plotcov` |
plot the GP covariances (default) |

`plotnoise` |
plot the observational noise (default) |

`samples` |
plot N samples from the GP |

`sigma` |
variance level to plot |

`title` |
plot title |

`legend` |
plot legend |

`plotgradient` |
use gradient graphics |

`plotls` |
plot lengthscale function |

`...` |
... |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# read toy data
data(toydata)
## Not run: can take several minutes
# perform one-sample regression
res = gpr2sample(toydata$ctrl$x, toydata$ctrl$y, seq(0,22,0.1))
# pre-computed model for toydata
data(toygps)
res = toygps$ctrlmodel
# basic plot with data, estimated mean and 95\%
plot(res)
# don't plot the data, plot some samples drawn from the learned gp
plot(res, plotdata=FALSE, samples=3)
## End(Not run)
``` |

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