mix.fitted object is a
mix object with three
additional slots, here is the complete list:
List of list of
Array of dimension sum(ntimes)*nresp*nstates providing the densities of the observed responses for each state.
Array of dimension
the current predictions for the initial state probabilities.
A vector containing the lengths of independent time series; if data is provided, sum(ntimes) must be equal to nrow(data).
The number of states of the model.
The number of independent responses.
The total number of parameters of the model. This is not the degrees of freedom, ie there are redundancies in the parameters, in particular in the multinomial models for the transitions and prior.
This provides some information on convergence, either from the EM algorithm or from Rdonlp2.
The linear constraint matrix, which has zero rows if there were no constraints.
The lower bounds on the linear constraints.
The upper bounds on the linear constraints.
Posterior (Viterbi) state sequence.
The print function shows some convergence information, and the summary method shows the parameter estimates.
mix.fitted, the only difference being that the model is fitted
by maximising the classification likelihood.
Ingmar Visser & Maarten Speekenbrink
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