plot: Plots object from 'deepgp' package

In deepgp: Bayesian Deep Gaussian Processes using MCMC

Plots object from deepgp package

Description

Acts 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.

Usage

## 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, ...)

Arguments

x	object of class gp, gpvec, dgp2, dgp2vec, dgp3, or dgp3vec
trace	logical indicating whether to generate trace plots (default is TRUE if the object has not been through predict)
predict	logical indicating whether to generate posterior predictive plot (default is TRUE if the object has been through predict)
...	N/A
hidden	logical indicating whether to generate plots of hidden layers (two or three layer only, default is FALSE)

Details

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.

Examples

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

deepgp documentation built on Sept. 11, 2024, 8:30 p.m.