maxpear {mcclust} | R Documentation |
Based on a posterior similarity matrix of a sample of clusterings maxpear
finds the clustering that maximizes the
posterior expected Rand adjusted index (PEAR) with the true clustering, while pear
computes PEAR for several provided clusterings.
maxpear(psm, cls.draw = NULL, method = c("avg", "comp", "draws",
"all"), max.k = NULL)
pear(cls,psm)
psm |
a posterior similarity matrix, usually obtained from a call to |
cls , cls.draw |
a matrix in which every row corresponds to a clustering of the |
method |
the maximization method used. Should be one of |
max.k |
integer, if |
For method="avg"
and "comp"
1-psm
is used as a distance matrix for hierarchical clustering with average/complete linkage.
The hierachical clustering is cut for the cluster sizes 1:max.k
and PEAR computed for these clusterings.
Method "draws"
simply computes PEAR for each row of cls.draw
and takes the maximum.
If method="all"
all maximization methods are applied.
cl |
clustering with maximal value of PEAR. If |
value |
value of PEAR. A vector corresponding to the rows of |
method |
the maximization method used. |
Arno Fritsch, arno.fritsch@tu-dortmund.de
Fritsch, A. and Ickstadt, K. (2009) An improved criterion for clustering based on the posterior similarity matrix, Bayesian Analysis, accepted.
comp.psm
for computing posterior similarity matrix, minbinder
, medv
, relabel
for other possibilities for processing a sample of clusterings.
data(cls.draw1.5)
# sample of 500 clusterings from a Bayesian cluster model
tru.class <- rep(1:8,each=50)
# the true grouping of the observations
psm1.5 <- comp.psm(cls.draw1.5)
mpear1.5 <- maxpear(psm1.5)
table(mpear1.5$cl, tru.class)
# Does hierachical clustering with Ward's method lead
# to a better value of PEAR?
hclust.ward <- hclust(as.dist(1-psm1.5), method="ward")
cls.ward <- t(apply(matrix(1:20),1, function(k) cutree(hclust.ward,k=k)))
ward1.5 <- pear(cls.ward, psm1.5)
max(ward1.5) > mpear1.5$value