an object of class graphlist as obtained from estimateGraph
delta
numeric threshold, between 0 and 1 if scaled = TRUE
scaled
optional boolean, if TRUE, indices are normalized by the overall variance before for threshold cut, defaults to TRUE
robust
optional boolean, if TRUE, upper confidence intervals limits are used for the threshold cut instead of indices themselves, confidence intervals must be provided in graphlist, defaults to FALSE
Value
an object of class graphlist where the indices are thresholded the clique structure is updated respectively, see estimateGraph for a detailed description
Warning
The threshold cut is by default performed on scaled indices. For a cut on the original unscaled indices set scaled = FALSE.
Author(s)
J. Fruth, T. Muehlenstaedt, O. Roustant
References
Muehlenstaedt, T.; Roustant, O.; Carraro, L.; Kuhnt, S. (2011) Data-driven Kriging models based on FANOVA-decomposition, Statistics and Computing.
Examples
# Kriging model prediction
x <- matrix(runif(100*3,-pi,pi),100,3)
KM <- km(~1, design = data.frame(x), response = ishigami.fun(x))
krigingMean <- function(Xnew) predict(object = KM, newdata = Xnew,
type = "UK", se.compute = FALSE, checkNames = FALSE)$mean
# full graph estimation
g <- estimateGraph(krigingMean, d=3, n.tot=10000, q.arg=list(min=-pi, max=pi))
print(g[c(2,6)])
# threshold graph
g.cut <- threshold(g, delta = 0.1)
print(g.cut[c(2,6)])