gp.list: Gaussian Process Lists

In mlegp: Maximum Likelihood Estimates of Gaussian Processes

Gaussian Process Lists

Description

Creates an object of type gp.list, given a list of Gaussian processes fit to separate sets of observations, or a list of Gaussian processes fit to principle component weights to approximate output of high dimension

Usage

gp.list(..., param.names = NULL, UD = NULL, gp.names = NULL)

Arguments

...	either a list object, where each element is a Gaussian process; or several objects of type gp
param.names	optionally, the parameter names corresponding to the columns of the design matrix of all Gaussian processes. By default, this will be equal to the parameter names of the first Gaussian process in ...
UD	the UD matrix, if the Gaussian process is fit to principle component weights
gp.names	optionally, a vector of names for the Gaussian processes, defaulting to ‘gp \#1’, ‘gp \#2’, ...

Value

A gp.list object is a list object, where the first k elements correspond to k Gaussian processes passed in as .... This makes it straightforward to access a single Gaussian process. In addition, gp.list contains components:

params	a vector of parameter names, corresponding to the columns of the design matrix
numGPs	the number of Gaussian processes in the list
numDim	the number of parameters in the design matrix
numObs	the number of observations
names	the names of the Gaussian processes

Note

currently, we require that all Gaussian processes have the same dimension (number of columns in the design matrix) and the same number of observations

this function is called by mlegp and should not be called by the user

Author(s)

Garrett M. Dancik [e-mail]

References

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

See Also

mlegp, mlegp-svd-functions for more details about the UD matrix

Examples

  x = -5:5
  y1 = sin(x) + rnorm(length(x), sd=.1)
  y2 = sin(x) + rnorm(length(x), sd = .5)

  ## create the gp.list object ## 
  fitMulti = mlegp(x, cbind(y1,y2))

  plot(fitMulti)
 
  fitMulti   ## print summary of of the fitted Gaussian process list
  fitMulti[[2]]  ## print summary for the 2nd Gaussian process 

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