CGGPfit {CGGP} | R Documentation |
This function will update the GP parameters for a CGGP design.
CGGPfit(CGGP, Y, Xs = NULL, Ys = NULL, theta0 = pmax(pmin(CGGP$thetaMAP, 0.8), -0.8), HandlingSuppData = CGGP$HandlingSuppData, separateoutputparameterdimensions = is.matrix(CGGP$thetaMAP), set_thetaMAP_to, corr, Ynew)
CGGP |
Sparse grid objects |
Y |
Output values calculated at CGGP$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$design_unevaluated' |
Updated CGGP object fit to data given
Other CGGP core functions: CGGPappend
,
CGGPcreate
, predict.CGGP
cg <- CGGPcreate(d=3, batchsize=100) y <- apply(cg$design, 1, function(x){x[1]+x[2]^2}) cg <- CGGPfit(CGGP=cg, Y=y)