Point estimate of the partition for the Binder loss function

Get a point estimate of the partition using the Binder loss function.

Usage

cluster_est_binder(c, logposterior)

Arguments

c

a list of vector of length n. c[[j]][i] is the cluster allocation of observation i=1...n at iteration j=1...N.

logposterior

vector of logposterior corresponding to each partition from c used to break ties when minimizing the cost function

Value

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.

References

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.

See also

similarityMat similarityMatC

Author

Francois Caron, Boris Hejblum