View source: R/evalClustLoss.R
evalClustLoss | R Documentation |
Evaluate the loss of a point estimate of the partition compared to a gold standard according to a given loss function
evalClustLoss(c, gs, lossFn = "F-measure", a = 1, b = 1)
c |
vector of length |
gs |
vector of length |
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 |
b |
only relevant if |
The cost of a point estimate partition is calculated using either a pairwise coincidence loss function (Binder), or 1-Fmeasure (F-measure).
the cost of the point estimate c
in regard of the
gold standard gs
for a given loss function.
Boris Hejblum
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.
similarityMat
, cluster_est_binder
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