CGGPfit {CGGP}

Update CGGP model given data

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)