compute.M.all {MFDA}R Documentation

M-step in the EM algorithm for functional mixture models.

Description

Maximization step in the EM algorithm for functional mixture models.

Usage

compute.M.all(x, weight, my.label)

Arguments

x A matrix, or data frame of observations, rows correspond to observations and columns correspond to variables.
weight A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
my.label A list whose kth entry is the indicator value indicating whether the ith functional observation is participant of M-step for cluster k.

Value

A list including the following components:

mu A matrix whose kth column is the mean of the kth component of the mixture model.
zeta A numerical vector specifying the estimate of cluster precision parameters.
varht A numerical vector specifying the estimate of cluster error variance.
mu A matrix specifying the estimate of cluster mean profile.
zeta A numerical vector specifying the estimate of cluster precision parameters.
trc A numerical vector whose kth entry specifying the trace of smoothing matrix of cluster k.

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

Estep.tk


[Package MFDA version 1.1-1 Index]