plot.gp.list: Diagnostics Plots for Gaussian Process Lists

In mlegp: Maximum Likelihood Estimates of Gaussian Processes

Diagnostics Plots for Gaussian Process Lists

Description

Cross-validated Diagnostic Plots For Gaussian Process Lists

Usage

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

Arguments

x	an object of class gp.list
sds	the number of standard deviations to use for confidence bands / intervals
CI.at.point	if TRUE, will plot confidence intervals around each predicted point, rather than bands, which is the default
...	not used; for compatibility with plot.gp

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 output, black lines correspond to the observations, and red lines correspond to confidence bands.

For each Gaussian process in x, plot.gp is called using graph type 1, which plots cross-validated predictions vs. observed values.

Author(s)

Garrett M. Dancik [e-mail]

References

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

See Also

plot.gp, CV

Examples

## create data for multiple responses ##
x = seq(-5,5)
z1 = 10 - 5*x + rnorm(length(x))
z2 = 4 - 5*x + rnorm(length(x))
z3 = 7*sin(x) + rnorm(length(x))

## fit multiple Gaussian processes ##
fitMulti = mlegp(x, cbind(z1,z2,z3))

## plot diagnostics ##
plot(fitMulti)

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