Class providing object with methods for fitting a GP model
Class providing object with methods for fitting a GP model
Object of R6Class with methods for fitting GP model.
R6Class object.
new(X, Z, corr="Gauss", verbose=0, separable=T, useC=F,useGrad=T,
parallel=T, nug.est=T, ...)This method is used to create object of this class with X and Z as the data.
update(Xnew=NULL, Znew=NULL, Xall=NULL, Zall=NULL,
restarts = 5,
param_update = T, nug.update = self$nug.est)This method updates the model, adding new data if given, then running optimization again.
XDesign matrix
ZResponses
NNumber of data points
DDimension of data
corrType of correlation function
nug.minMinimum value of nugget
nugValue of the nugget, is estimated unless told otherwise
separableAre the dimensions separable?
verbose0 means nothing printed, 1 prints some, 2 prints most.
useGradShould grad be used?
useCShould C code be used?
parallelShould the code be run in parallel?
parallel_coresHow many cores are there? It will self detect, do not set yourself.
corrType of correlation function
separableAre the dimensions separable?
corr_func()GauPro_base$corr_func(...)
new()GauPro_base$new( X, Z, verbose = 0, useC = F, useGrad = T, parallel = FALSE, nug = 1e-06, nug.min = 1e-08, nug.est = T, param.est = TRUE, ... )
initialize_GauPr()GauPro_base$initialize_GauPr()
fit()GauPro_base$fit(X, Z)
update_K_and_estimates()GauPro_base$update_K_and_estimates()
predict()GauPro_base$predict(XX, se.fit = F, covmat = F, split_speed = T)
pred()GauPro_base$pred(XX, se.fit = F, covmat = F, split_speed = T)
pred_one_matrix()GauPro_base$pred_one_matrix(XX, se.fit = F, covmat = F)
pred_mean()GauPro_base$pred_mean(XX, kx.xx)
pred_meanC()GauPro_base$pred_meanC(XX, kx.xx)
pred_var()GauPro_base$pred_var(XX, kxx, kx.xx, covmat = F)
pred_LOO()GauPro_base$pred_LOO(se.fit = FALSE)
cool1Dplot()GauPro_base$cool1Dplot( n2 = 20, nn = 201, col2 = "gray", xlab = "x", ylab = "y", xmin = NULL, xmax = NULL, ymin = NULL, ymax = NULL )
plot1D()GauPro_base$plot1D( n2 = 20, nn = 201, col2 = 2, xlab = "x", ylab = "y", xmin = NULL, xmax = NULL, ymin = NULL, ymax = NULL )
plot2D()GauPro_base$plot2D()
loglikelihood()GauPro_base$loglikelihood(mu = self$mu_hat, s2 = self$s2_hat)
optim()GauPro_base$optim( restarts = 5, param_update = T, nug.update = self$nug.est, parallel = self$parallel, parallel_cores = self$parallel_cores )
optimRestart()GauPro_base$optimRestart( start.par, start.par0, param_update, nug.update, optim.func, optim.grad, optim.fngr, lower, upper, jit = T )
update()GauPro_base$update( Xnew = NULL, Znew = NULL, Xall = NULL, Zall = NULL, restarts = 5, param_update = self$param.est, nug.update = self$nug.est, no_update = FALSE )
update_data()GauPro_base$update_data(Xnew = NULL, Znew = NULL, Xall = NULL, Zall = NULL)
update_corrparams()GauPro_base$update_corrparams(...)
update_nugget()GauPro_base$update_nugget(...)
deviance_searchnug()GauPro_base$deviance_searchnug()
nugget_update()GauPro_base$nugget_update()
grad_norm()GauPro_base$grad_norm(XX)
sample()GauPro_base$sample(XX, n = 1)
print()GauPro_base$print()
clone()The objects of this class are cloneable with this method.
GauPro_base$clone(deep = FALSE)
deepWhether to make a deep clone.
# NOT RUN {
#n <- 12
#x <- matrix(seq(0,1,length.out = n), ncol=1)
#y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#gp <- GauPro(X=x, Z=y, parallel=FALSE)
# }
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