if (FALSE) {
data(metMUD2)
# Using clusterCrit
res1 <- hcaSpectra(metMUD2) # default clustering and distance methods
res2 <- hcaSpectra(metMUD2, d.method = "cosine")
# The return value from hcaSpectra is a list with hclust as the first element.
crit1 <- evalClusters(metMUD2, pkg = "clusterCrit", hclst = res1[[1]], k = 2)
crit2 <- evalClusters(metMUD2, pkg = "clusterCrit", hclst = res2[[1]], k = 2)
# crit1 and crit2 can now be compared.
# Using NbClust
res3 <- evalClusters(metMUD2, min.nc = 2, max.nc = 5, method = "average", index = "kl")
}
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