ELoss of a partition point estimate compared to a gold standard

Evaluate the loss of a point estimate of the partition compared to a gold standard according to a given loss function

Usage

evalClustLoss(c, gs, lossFn = "F-measure", a = 1, b = 1)

Arguments

c: vector of length n containing the estimated partition of the n observations.
gs: vector of length n containing the gold standard partition of the n observations.
lossFn: character string specifying the loss function to be used. Either "F-measure" or "Binder" (see Details). Default is "F-measure".
a: only relevant if lossFn is "Binder". Penalty for wrong co-clustering in c compared to gs. Defaults is 1.
b: only relevant if lossFn is "Binder". Penalty for missed co-clustering in c compared to gs. Defaults is 1.

Value

the cost of the point estimate c in regard of the gold standard gs for a given loss function.

Details

The cost of a point estimate partition is calculated using either a pairwise coincidence loss function (Binder), or 1-Fmeasure (F-measure).

References

J.W. Lau & P.J. Green. Bayesian Model-Based Clustering Procedures, Journal of Computational and Graphical Statistics, 16(3): 526-558, 2007.

D. B. Dahl. Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, in Bayesian Inference for Gene Expression and Proteomics, K.-A. Do, P. Muller, M. Vannucci (Eds.), Cambridge University Press, 2006.

Author

Boris Hejblum