computeQuickKrigcov <- function(model,
integration.points,
X.new,
precalc.data,
F.newdata,
c.newdata){
integration.points <- t(as.matrix(integration.points))
colnames(integration.points) <- colnames(model@X)
c.xnew.integpoints <- DiceKriging::covMat1Mat2(X1=integration.points,X2=X.new, object=model@covariance, nugget.flag=model@covariance@nugget.flag)
second.member <- t(F.newdata - crossprod(c.newdata,precalc.data$Kinv.F))
# second.member ~ erreur due a l'estimation de la tendance sur X.new
# second.member = F.newdata - t(c.newdata) %*% Kinv.F = F.newdata - t(c.newdata) %*% K^(-1) %*% F
# first.member ~ la moitiee de la variance d'erreur d'estimation de la tendance, que sur les n anciennes donnees pour integration.points
# first.member = (f.integration.points - t(c.olddata) %*% K^(-1) %*% F) %*% norm.const^(-1)
# cov.F est la covariance d'erreur due a l'estimation de la tendance
cov.F <- precalc.data$first.member%*%second.member
# c.newdata est la cov entre learn.db et les points a predire
# crossprod(precalc.data$Kinv.c.olddata,c.newdata) = t(c.newdata) %*% K^(-1) %*% c.newdata
# cov.std = c.xnew.integpoints - t(c.newdata) %*% K^(-1) %*% c.newdata
kn <- c.xnew.integpoints - crossprod(precalc.data$Kinv.c.olddata,c.newdata) + cov.F
return(kn)
}
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