Get a point estimate of the partition using the Binder loss function.
cluster_est_binder(c, logposterior)
a list
:
c_est
:a vector of length n
. Point estimate of the partition
cost
:a vector of length N
. cost[j]
is the cost
associated to partition c[[j]]
similarity
:matrix of size n x n
. Similarity matrix
(see similarityMat
)
opt_ind
:the index of the optimal partition among the MCMC iterations.
a list of vector of length n
. c[[j]][i]
is
the cluster allocation of observation i=1...n
at iteration
j=1...N
.
vector of logposterior corresponding to each
partition from c
used to break ties when minimizing the cost function
Francois Caron, Boris Hejblum
F Caron, YW Teh, TB Murphy, Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes, Annals of Applied Statistics, 8(2):1145-1181, 2014.
DB Dahl, Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, Bayesian Inference for Gene Expression and Proteomics, K-A Do, P Muller, M Vannucci (Eds.), Cambridge University Press, 2006.
similarityMat
similarityMatC