plot.gp: Diagnostic Plots for Gaussian processes

In mlegp: Maximum Likelihood Estimates of Gaussian Processes

Diagnostic Plots for Gaussian processes

Description

Cross-Validated Diagnostic Plots for Gaussian Processes

Usage

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

Arguments

x	an object of class gp
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

Details

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

Author(s)

Garrett M. Dancik [e-mail]

References

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

See Also

CV for cross-validation, plot.gp.list for plotting gp.list objects

Examples

## 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)

mlegp documentation built on March 18, 2022, 5:29 p.m.