Description Usage Arguments Value Author(s) References See Also
View source: R/Estep.MSAR.VM.R
Forwardbackward 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=sy_0,\cdots,y_T) 
probSS 
one step smoothing probabilities: P(S_t=s,S_{t+1}y_0,\cdots,y_T) 
Valerie Monbet, [email protected]
Ailliot P., Bessac J., Monbet V., Pene F., (2014) Nonhomogeneous hidden Markovswitching models for wind time series. JSPI.
fit.MSAR.VM, Mstep.hh.MSAR.VM,Estep.MSAR
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.