plot.gp | R Documentation |

Cross-Validated Diagnostic Plots for Gaussian Processes

## S3 method for class 'gp' plot(x, type = 0, params = NULL, sds = 1, CI.at.point = FALSE, ...)

`x` |
an object of class |

`type` |
the type of graph to plot, 0 by default (see Details) |

`params` |
for graph types 2 and 3, a vector of parameter names (or parameter indices) to plot against. By default, all parameters are looked at |

`sds` |
the number of standard deviations to use for confidence bands/intervals, for graph types 0-3 |

`CI.at.point` |
if TRUE, will plot confidence intervals at each predicted point, rather than bands, which is the default |

`...` |
additional arguments to plot, but cannot overwrite xlab or ylab |

All plots involve cross-validated predictions and/or cross-validated standardized residuals. The cross-validation is in the sense that for predictions made at design point x, all observations at design point x are removed from the training set.

Where relevant, open circles correspond to Gaussian process predictions, black lines correspond to the observations, and red lines correspond to confidence bands. The argument `type`

determines the type of graph displayed, and is one of the following integers:

0 for observed vs. predicted AND observed vs. standardized residual (default), | |

1 for observed vs. predicted only, | |

2 for parameter vs. predicted for all parameters, | |

3 for parameter vs. standardized residual for all parameters, | |

4 for normal quantile plot and histogram of standardized residuals |

Garrett M. Dancik dancikg@easternct.edu

https://github.com/gdancik/mlegp/

`CV`

for cross-validation, `plot.gp.list`

for plotting `gp.list`

objects

## fit the gp ## x = seq(-5,5,by=.5) y = sin(x) + rnorm(length(x), sd=.1) fit = mlegp(x,y) ## plot diagnostics ## plot(fit) plot(fit, type = 2)

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