krige: Kriging the sample means of statistics

In qle: Simulation-Based Quasi-Likelihood Estimation

Description

predictKM,

wrapper for kriging the sample means of statistics

varKM,

calculate the kriging prediction variances

extract,

extract the results of kriging

Usage

predictKM(models, ...)

varKM(models, ...)

extract(X, type = c("mean", "sigma2", "weights"))

Arguments

models	list of covariance models, see setCovModel
...	further arguments passed to function estim
X	kriging result
type	return type of results, see details below

Details

For a list of fitted covariance models the function predictKM predicts the sample means of statistics at (unsampled) points, calculates the prediction variances, if applicable, at these points and extracts the results. Note that, since we aim on predicting the "error free" value of the sample means, we use the smoothing kriging predictor as described in [2, Sec. 3.7.1].

The function extract either returns the predicted values, the prediction variances or the kriging weights for each point.

Value

predictKM	list of kriging predicted values
varKM	list of kriging prediction variances
extract	matrix of corresponding values (see details)

Examples

data(normal)
X <- as.matrix(qsd$qldata[,1:2])
p <- c("mu"=2,"sd"=1)

# get simulated statistics at design X
Tstat <- qsd$qldata[grep("^mean[.]",names(qsd$qldata))]

# predict and extract 
predictKM(qsd$covT,p,X,Tstat)

# prediction variances
varKM(qsd$covT,p,X,Tstat)

qle documentation built on May 2, 2019, 9:55 a.m.