# NOT RUN {
## Invoke data
data(ADdata)
## Subset
ADclass1 <- ADmetabolites[, sampleInfo$ApoEClass == "Class 1"]
ADclass2 <- ADmetabolites[, sampleInfo$ApoEClass == "Class 2"]
## Transpose data
ADclass1 <- t(ADclass1)
ADclass2 <- t(ADclass2)
## Correlations for subsets
rAD1 <- cor(ADclass1)
rAD2 <- cor(ADclass2)
## Simple precision estimates
P1 <- ridgeP(rAD1, 2)
P2 <- ridgeP(rAD2, 2)
Plist = list(P1 = P1, P2 = P2)
## Threshold matrices
Mats <- sparsify.fused(Plist, threshold = "top", top = 20)
## Prune sparsified partial correlation matrices
## To union of features implied by edge
MatsPrune <- Union(Mats$P1$sparseParCor, Mats$P2$sparseParCor)
# }
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