em.bic {MFDA} | R Documentation |
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.
em.bic(likelihood, trc, n, m)
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 .
|
The BIC or Bayesian Information Criterion for the given input arguments.
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.