A mix.fitted object is a mix object with three
additional slots, here is the complete list:
response:
List of list of response objects.
prior:
transInit object.
dens:
Array of dimension sum(ntimes)*nresp*nstates
providing the densities of the observed responses for each state.
init:
Array of dimension length(ntimes)*nstates with
the current predictions for the initial state probabilities.
ntimes:
A vector containing the lengths of independent time
series; if data is provided, sum(ntimes) must be equal to
nrow(data).
nstates:
The number of states of the model.
nresp:
The number of independent responses.
npars:
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.
message:
This provides some information on convergence,
either from the EM algorithm or from Rdonlp2.
conMat:
The linear constraint matrix, which has zero rows
if there were no constraints.
lin.lower
The lower bounds on the linear constraints.
lin.upper
The upper bounds on the linear constraints.
posterior:
Posterior (Viterbi) state sequence.
Details
The print function shows some convergence information, and the summary
method shows the parameter estimates.
Extends
Class "mix" directly. mix.fitted.classLik is
similar to mix.fitted, the only difference being that the model is fitted
by maximising the classification likelihood.