CHMM_VEM {CHMM}

Perform variational inference of coupled Hidden Markov Models.

Description

Perform variational inference of coupled Hidden Markov Models.

Usage

CHMM_VEM(X, nb.states, S = NULL, omega = 0.7, meth.init = "mclust",
  var.equal = TRUE, itmax = 500, threshold = 1e-07)

Arguments

X

a data matrix of observations. Columns correspond to individuals.

nb.states

a integer specifying the numbers of states.

S

a matrix of similarity between individuals.

omega

a value of omega.

meth.init

a string specifying the initialization method ("mclust" or "kmeans"). The default method is "mclust".

var.equal

a logical variable indicating whether to treat the variances as being equal.

itmax

an integer specifying the maximal number of iterations for the EM algorithm.

threshold

a value for the threshold used for the stopping criteria.

Value

a list of 9 components

postPr

a list containing for each series the posterior probabilities.

initPr

a numeric specifying the initial state probabilities.

transPr

a matrix of the state transition probabilities.

esAvg

a numeric of the estimated mean for each state.

esVar

a numeric of the estimated variance for each state.

emisPr

a list containing for each series the emission probabilities.

emisPrW

a list containing for each series the emission probabilities taking into account for the dependency structure.

RSS

a numeric corresponding to the Residuals Sum of Squares.

iterstop

an integer corresponding to the total number of iterations.

References

Wang, X., Lebarbier, E., Aubert, J. and Robin, S., Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations.