computeQuickKrigcov {KrigInv} | R Documentation |
Quick computation of kriging covariances
Description
Computes kriging covariances between some new points and many integration points, using precomputed data.
Usage
computeQuickKrigcov(model,integration.points,X.new,
precalc.data, F.newdata , c.newdata)
Arguments
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. |
Details
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
.
Value
Matrix of size p*q containing kriging covariances
Author(s)
Clement Chevalier (University of Neuchatel, Switzerland)
References
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
See Also
precomputeUpdateData
, predict_nobias_km
Examples
#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)