Description Usage Arguments Value Author(s) References See Also
View source: R/Estep.MSAR.VM.R
Forward-backward algorithm called in fit.MSAR.
1 2 |
data |
array of univariate or multivariate series with dimension T*N.samples*d. T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension. |
theta |
model's parameter; object of class MSAR. See also init.theta.MSAR. |
smth |
If smth=FALSE, only the forward step is computed for forecasting probabilities. If smth=TRUE, the smoothing probabilities are computed too. |
verbose |
|
covar.emis |
covariables for emission probabilities. |
covar.trans |
covariables for transition probabilities |
list including
loglik |
log likelihood |
probS |
smoothing probabilities: P(S_t=s|y_0,\cdots,y_T) |
probSS |
one step smoothing probabilities: P(S_t=s,S_{t+1}|y_0,\cdots,y_T) |
Valerie Monbet, valerie.monbet@univ-rennes1.fr
Ailliot P., Bessac J., Monbet V., Pene F., (2014) Non-homogeneous hidden Markov-switching models for wind time series. JSPI.
fit.MSAR.VM, Mstep.hh.MSAR.VM,Estep.MSAR
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