svdGP  R Documentation 
This function fits a full SVDbased GP model with test set X0
,
design set design
and response matrix resp
.
svdGP(design,resp,X0=design,nstarts=5,gstart=0.0001, frac=.95,centralize=FALSE,nthread=1,clutype="PSOCK")
design 
An N by d matrix of N training/design inputs. 
resp 
An L by N response matrix of 
X0 
An M by d matrix of M test inputs. The
default value of 
nstarts 
The number of starting points used in the numerical maximization of
the posterior density function. The larger 
gstart 
The starting number and upper bound for estimating the nugget
parameter. If 
frac 
The threshold in the cumulative percentage criterion to select the number of SVD bases. The default value is 0.95. 
centralize 
If 
nthread 
The number of threads (processes) used in parallel execution of this
function. 
clutype 
The type of cluster in the R package "parallel" to perform
parallelization. The default value is "PSOCK". Required only if

pmean 
An L by M matrix of posterior predicted mean for the response at
the test set 
ps2 
An L by M matrix of posterior predicted variance for the response at
the test set 
Ru Zhang heavenmarshal@gmail.com,
C. Devon Lin devon.lin@queensu.ca,
Pritam Ranjan pritamr@iimidr.ac.in
knnsvdGP
, lasvdGP
.
library("lhs") forretal < function(x,t,shift=1) { par1 < x[1]*6+4 par2 < x[2]*16+4 par3 < x[3]*6+1 t < t+shift y < (par1*t2)^2*sin(par2*tpar3) } timepoints < seq(0,1,len=200) design < lhs::randomLHS(50,3) test < lhs::randomLHS(50,3) ## evaluate the response matrix on the design matrix resp < apply(design,1,forretal,timepoints) ## fit full SVDbased GP model ret < svdGP(design,resp,test,frac=.95,nstarts=1, centralize=TRUE,nthread=2)
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