Description Usage Arguments Details Examples

Plots the GP's corresponding to the control and case data, as well as the null model. Visualizes the log likelihood ratios between the null and individual models. Several boolean parameters for modifying the plot. By default plots the data, posterior mean and 95% interval for CASE and CONTROL.

1 2 3 4 5 |

`x` |
the |

`y` |
placeholder variable |

`plotdata` |
plot the data (default) |

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

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

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

`plotnull` |
plots also the null model |

`plotratios` |
plots the ratios, choices are |

`thr` |
ratio threshold |

`samples` |
plot N samples from the GP |

`sigma` |
variance level to plot |

`title` |
plot title |

`legend` |
plot legend |

`plotgradient` |
use gradient graphics |

`...` |
... |

The threshold `thr`

is the logarithmic likelihood ratio between
null and control+case models. The default value 1 hence corresponds to
a likelihood ratio of 2.72.

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 two-sample regression
res = gpr2sample(toydata$ctrl$x, toydata$ctrl$y, toydata$case$x, toydata$case$y, seq(0,22,0.1))
# pre-computed model for toydata
data(toygps)
res = toygps
# basic plot
plot(res)
# plot also the null model, don't plot data, means or noise
plot(res, plotnull=TRUE, plotdata=FALSE, plotmeans=FALSE, plotnoise=FALSE)
## End(Not run)
``` |

nsgp documentation built on May 29, 2017, 11:47 p.m.

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