View source: R/computeQuickKrigcov.R
computeQuickKrigcov | R Documentation |
Computes kriging covariances between some new points and many integration points, using precomputed data.
computeQuickKrigcov(model,integration.points,X.new, precalc.data, F.newdata , c.newdata)
model |
A Kriging model of |
integration.points |
p*d matrix of fixed integration points in the X space. |
X.new |
q*d matrix of new points. The calculated covariances are the covariances between these new point and the integration points. |
precalc.data |
List containing precalculated data. This list is generated using the function |
F.newdata |
The value of the kriging trend basis function at point X.new. |
c.newdata |
The (unconditional) covariance between X.new and the design points. |
This function requires to use another function in order to generate the proper arguments.
The argument precalc.data
can be generated using precomputeUpdateData
.
The arguments F.newdata
and c.newdata
can be obtained using predict_nobias_km
.
Matrix of size p*q containing kriging covariances
Clement Chevalier (University of Neuchatel, Switzerland)
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2014), Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, vol. 56(4), pp 455-465
Chevalier C., Ginsbourger D. (2014), Corrected Kriging update formulae for batch-sequential data assimilation, in Pardo-Iguzquiza, E., et al. (Eds.) Mathematics of Planet Earth, pp 119-122
precomputeUpdateData
, predict_nobias_km
#computeQuickKrigcov set.seed(9) N <- 20 #number of observations testfun <- branin #a 20 points initial design design <- data.frame( matrix(runif(2*N),ncol=2) ) response <- testfun(design) #km object with matern3_2 covariance #params estimated by ML from the observations model <- km(formula=~., design = design, response = response,covtype="matern3_2") #the integration.points are the points where we want to #compute predictions/covariances if a point new.x is added #to the DOE x.grid <- seq(0,1,length=20) integration.points <- expand.grid(x.grid,x.grid) integration.points <- as.matrix(integration.points) #precalculation precalc.data <- precomputeUpdateData(model=model, integration.points=integration.points) #now we can compute quickly kriging covariances #between these data and any other points. #example if 5 new points are added: X.new <- matrix(runif(10),ncol=2) pred <- predict_nobias_km(object=model, newdata=X.new,type="UK",se.compute=TRUE) kn <- computeQuickKrigcov(model=model, integration.points=integration.points,X.new=X.new, precalc.data=precalc.data, F.newdata=pred$F.newdata, c.newdata=pred$c)
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