CGGPfit: Update CGGP model given data

In CollinErickson/CGGP: Composite Grid Gaussian Processes

Description

This function will update the GP parameters for a CGGP design.

Usage

CGGPfit(
  CGGP,
  Y,
  Xs = NULL,
  Ys = NULL,
  theta0 = pmax(pmin(CGGP$thetaMAP, 0.8), -0.8),
  HandlingSuppData = CGGP$HandlingSuppData,
  separateoutputparameterdimensions = is.matrix(CGGP$thetaMAP),
  set_thetaMAP_to,
  corr,
  Ynew
)

Arguments

CGGP	Sparse grid objects
Y	Output values calculated at CGGP$design
Xs	Supplemental X matrix
Ys	Supplemental Y values
theta0	Initial theta
HandlingSuppData	How should supplementary data be handled? * Correct: full likelihood with grid and supplemental data * Only: only use supplemental data * Ignore: ignore supplemental data
separateoutputparameterdimensions	If multiple output dimensions, should separate parameters be fit to each dimension?
set_thetaMAP_to	Value for thetaMAP to be set to
corr	Will update correlation function, if left missing it will be same as last time.
Ynew	Values of 'CGGP$design_unevaluated'

Value

Updated CGGP object fit to data given

See Also

Other CGGP core functions: CGGPappend(), CGGPcreate(), predict.CGGP()

Examples

cg <- CGGPcreate(d=3, batchsize=100)
y <- apply(cg$design, 1, function(x){x[1]+x[2]^2})
cg <- CGGPfit(CGGP=cg, Y=y)

CollinErickson/CGGP documentation built on May 14, 2021, 4:33 a.m.