Based on a posterior similarity matrix of a sample of clusterings minbinder finds the clustering that minimizes the
posterior expectation of Binders loss function, while binder computes the posterior expected loss for several provided clusterings.
minbinder(psm, cls.draw = NULL, method = c("avg", "comp", "draws",
"laugreen","all"), max.k = NULL, include.lg = FALSE,
start.cl = NULL, tol = 0.001)binder(cls,psm)
laugreen(psm, start.cl, tol=0.001)
a posterior similarity matrix, usually obtained from a call to comp.psm.
a matrix in which every row corresponds to a clustering of the ncol(cls) objects.
cls.draw refers to the clusterings that have been used to compute psm, cls.draw has to be provided if
method="draw" or "all".
the maximization method used. Should be one of "avg", "comp", "draws", "laugreen" or "all".
The default is "avg".
integer, if method="avg" or "comp" the maximum number of clusters up to which the hierarchical clustering is cut.
Defaults to ceiling(nrow(psm)/4).
logical, should method "laugreen" be included when method="all"? Defaults to FALSE.
clustering used as starting point for method="laugreen". If NULL start.cl= 1:nrow(psm) is used.
convergence tolerance for method="laugreen".
clustering with minimal value of expected loss. If method="all" a matrix containing the clustering with the smallest value
of the expected loss over all methods in the first row and the clusterings of the individual methods in the next rows.
value of posterior expected loss. A vector corresponding to the rows of cl if method="all".
the maximization method used.
if method="laugreen" the number of iterations the method needed to converge.
The posterior expected loss is the sum of the absolute differences of the indicator function of observation
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 the posterior expected loss is computed for these clusterings.
Method "draws" simply computes the posterior expected loss for each row of cls.draw and takes the minimum.
Method "laugreen" implements the algorithm of Lau and Green (2007), which is based on binary integer programming. Since the method can
take some time to converge it is only used if explicitly demanded with method="laugreen" or method="all" and include.lg=TRUE.
If method="all" all minimization methods except "laugreen" are applied.
Binder, D.A. (1978) Bayesian cluster analysis, Biometrika 65, 31--38.
Fritsch, A. and Ickstadt, K. (2009) An improved criterion for clustering based on the posterior similarity matrix, Bayesian Analysis, accepted.
Lau, J.W. and Green, P.J. (2007) Bayesian model based clustering procedures, Journal of Computational and Graphical Statistics 16, 526--558.
comp.psm for computing posterior similarity matrix, maxpear, medv, relabel
for other possibilities for processing a sample of clusterings. lp for the linear programming.
# NOT RUN {
data(cls.draw2)
# sample of 500 clusterings from a Bayesian cluster model
tru.class <- rep(1:8,each=50)
# the true grouping of the observations
psm2 <- comp.psm(cls.draw2)
mbind2 <- minbinder(psm2)
table(mbind2$cl, tru.class)
# Does hierachical clustering with Ward's method lead
# to a lower value of Binders loss?
hclust.ward <- hclust(as.dist(1-psm2), method="ward")
cls.ward <- t(apply(matrix(1:20),1, function(k) cutree(hclust.ward,k=k)))
ward2 <- binder(cls.ward, psm2)
min(ward2) < mbind2$value
# Method laugreen is applied to 40 randomly selected observations
ind <- sample(1:400, 40)
mbind.lg <- minbinder(psm2[ind, ind],cls.draw2[,ind], method="all",
include.lg=TRUE)
mbind.lg$value
# }
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