Description Usage Arguments Value Author(s) References See Also Examples
View source: R/simule.nh.MSAR.VM.R
simule.nh.MSAR.VM simulates realisations of (non) homogeneous Markov Switching autoregressive models with von Mises innovations
1 | simule.nh.MSAR.VM(theta, Y0, T, N.samples = 1, covar.emis = NULL, covar.trans = NULL)
|
theta |
list of class MSAR including model parameters and a description of the model. See init.theta.MSAR.VM for more details. |
Y0 |
Initial value. Array of dimension order*N.samples*d with order the AR order, N.samples the number of samples to be simulated and d the dimension of the considered data. |
T |
Length of each realisation to be simulated |
N.samples |
number of samples to be simulated |
covar.emis |
emission covariate or lag for non homogeneous models. Lag is used if the covariate is the lagged time series. |
covar.trans |
transition covariate or lag for non homogeneous models. Lag is used if the covariate is the lagged time series. |
List including
..$Y |
simulated observation time series |
..$S |
simulated Markov chain |
Val\'erie Monbet, valerie.monbet@univ-rennes1.fr
Ailliot P., Bessac J., Monbet V., P\'ene F., (2014) Non-homogeneous hidden Markov-switching models for wind time series. JSPI.
fit.MSAR.VM, init.theta.MSAR.VM
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ##Not run
#data(WindDir)
#T = dim(WindDir)[1]
#N.samples = dim(WindDir)[2]
#Y = array(WindDir,c(T,N.samples,1))
# von Mises homogeneous MSAR
#M = 2
#order = 1
#theta.init = init.theta.MSAR.VM(Y,M=M,order=order,label="HH")
#polar.hh = fit.MSAR.VM(Y,theta.init,MaxIter=50,verbose=TRUE,eps=1e-8)
#K.sim = 1
#Y0 = array(Y[1:2,sample(1:N.samples,K.sim,replace=T),],c(2,K.sim,1))
#sim.dir = simule.nh.MSAR.VM(polar.hh$theta,Y0=Y0,T,N.samples=K.sim)
## Not run
#theta.init$mu = polar.hh$theta$mu
# theta.init$kappa = polar.hh$theta$kappa+1i*0 # kappa complex
# theta.init$prior = polar.hh$theta$prior
# theta.init$transmat = polar.hh$theta$transmat
# polar.hh.c = fit.MSAR.VM(Y,theta.init,MaxIter=50,verbose=TRUE,eps=1e-8)
# theta.init = init.theta.MSAR.VM(Y,M=M,order=order,label="NH",ncov=1,nh.transitions="VM")
# theta.init$mu = polar.hh.c$theta$mu
# theta.init$kappa = polar.hh.c$theta$kappa # kappa complex
# theta.init$prior = polar.hh.c$theta$prior
# theta.init$transmat = polar.hh.c$theta$transmat
# theta.init$par.trans = matrix(c(polar.hh.c$theta$mu,.1*matrix(1,M,1)),M,2)+1i
#Y.tmp = array(Y[2:T,,],c(T-1,N.samples,1))
#Z = array(Y[1:(T-1),,],c(T-1,N.samples,1))
# polar.nh.c = fit.MSAR.VM(Y.tmp,theta.init,MaxIter=1,verbose=T,eps=1e-8,covar.trans=Z)
#K.sim = 100
#Y0 = array(Y[1:2,sample(1:N.samples,K.sim,replace=T),],c(2,K.sim,1))
#sim.dir = simule.nh.MSAR.VM(polar.nh.c$theta,Y0=Y0,T,N.samples=K.sim,covar.trans=1)
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