R: ELoss of a partition point estimate compared to a gold...

evalClustLoss {NPflow} | R Documentation

## ELoss of a partition point estimate compared to a gold standard

### Description

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 codegs. Defaults is 1.
`b` | only relevant if `lossFn` is "Binder". Penalty for missed co-clustering in `c` compared to codegs. Defaults is 1.

### Details

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

### Value

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

### Author(s)

Boris Hejblum

### 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.

### See Also

`similarityMat`, `cluster_est_binder`

[Package NPflow version 0.13.3 Index]