Description Usage Arguments Value Examples
Fits, by using an ECM algorithm, parsimonious hidden Markov models to the given four-way data. Parallel computing is implemented and highly recommended for a faster model fitting. The Bayesian information criterion (BIC) is used to select the best fitting model.
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X |
An array of dimension |
k |
An integer or a vector indicating the number of states of the models. |
init.par |
The initial values for starting the algorithms, as produced by the |
mod.row |
A character vector indicating the parsimonious structure of the row covariance matrix. Possible values are: "EII", "VII", "EEI", "VEI", "EVI", "VVI", "EEE", "VEE", "EVE", "EEV", "VVE", "VEV", "EVV", "VVV" or "all". When "all" is used, all of the 14 row parsimonious structures are considered. |
mod.col |
A character vector indicating the parsimonious structure of the column covariance matrix. Possible values are: "II", "EI", "VI", "EE", "VE", "EV", "VV", or "all". When "all" is used, all of the 7 column parsimonious structures are considered. |
ncores |
A positive integer indicating the number of cores used for running in parallel. |
verbose |
A logical indicating whether the running output should be displayed. |
ret.all |
A logical indicating whether to report the results of all the models or only those of the best model according to the BIC. |
A list with the following elements:
all.models |
The results related to the all the fitted models (only when |
BicWin |
The best fitting model according to the BIC. |
Summary |
A quick table showing summary results for the best fitting model according to the BIC. |
c.time |
Provides information on the computational times required to fit all the models for each state. |
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