plot.gp: Plot a gaussian process

In nsgp: Non-Stationary Gaussian Process Regression

Description

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

Usage

## S3 method for class 'gp'
plot(x, y = NULL, plotdata = TRUE, plotmean = TRUE,
  plotcov = TRUE, plotnoise = FALSE, samples = 0, sigma = 2,
  title = NULL, legend = FALSE, plotgradient = TRUE, plotls = FALSE,
  ...)

Arguments

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
...	...

Examples

# 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)

nsgp documentation built on May 2, 2019, 9:19 a.m.