em.bic {MFDA}R Documentation

BIC for Functional Mixture Gaussian Models

Description

Compute the BIC (Bayesian Information Criterion) for functional mixture Gaussian models given the negative loglikelihood, the dimension of the data, and the trace of the smoothing matrix.

Usage

em.bic(likelihood, trc, n, m)

Arguments

likelihood The negative loglikelihood for a data set with respect to the functional mixture model.
trc The trace of the smoothing matrix.
n The number of the functional data use to compute loglik.
m The number of the repeated measurements in the data used to compute loglik.

Value

The BIC or Bayesian Information Criterion for the given input arguments.

References

Ma, P., Castillo-Davis, C., Zhong, W., and Liu, J. S. (2006) A data-driven clustering method for time course gene expression data, Nucleic Acids Research, 34 (4), 1261-1269.

See Also

em.clust.


[Package MFDA version 1.1-1 Index]