plot | R Documentation |

`deepgp`

packageActs on a `gp`

, `gpvec`

, `dgp2`

, `dgp2vec`

,
`dgp3`

, or `dgp3vec`

object.
Generates trace plots for outer log likelihood, length scale,
and nugget hyperparameters.
Generates plots of hidden layers for one-dimensional inputs or monotonic
warpings. Generates
plots of the posterior mean and estimated 90% prediction intervals for
one-dimensional inputs; generates heat maps of the posterior mean and
point-wise variance for two-dimensional inputs.

```
## S3 method for class 'gp'
plot(x, trace = NULL, predict = NULL, ...)
## S3 method for class 'gpvec'
plot(x, trace = NULL, predict = NULL, ...)
## S3 method for class 'dgp2'
plot(x, trace = NULL, hidden = NULL, predict = NULL, ...)
## S3 method for class 'dgp2vec'
plot(x, trace = NULL, hidden = NULL, predict = NULL, ...)
## S3 method for class 'dgp3'
plot(x, trace = NULL, hidden = NULL, predict = NULL, ...)
## S3 method for class 'dgp3vec'
plot(x, trace = NULL, hidden = NULL, predict = NULL, ...)
```

`x` |
object of class |

`trace` |
logical indicating whether to generate trace plots (default is
TRUE if the object has not been through |

`predict` |
logical indicating whether to generate posterior predictive
plot (default is TRUE if the object has been through |

`...` |
N/A |

logical indicating whether to generate plots of hidden layers (two or three layer only, default is FALSE) |

Trace plots are useful in assessing burn-in. If there are too
many hyperparameters to plot them all, then it is most useful to
visualize the log likelihood (e.g., `plot(fit$ll, type = "l")`

).

Hidden layer plots are colored on a gradient - red lines represent earlier iterations and yellow lines represent later iterations - to help assess burn-in of the hidden layers. Only every 100th sample is plotted.

```
# See ?fit_one_layer, ?fit_two_layer, or ?fit_three_layer
# for examples
```

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