Description Usage Arguments Value Author(s) See Also Examples
View source: R/simule.nh.MSAR.R
simule.nh.MSAR simulates realisations of (non) homogeneous Markov Switching autoregressive models with Gaussian innovations
1 2 | simule.nh.MSAR(theta, Y0, T, N.samples = 1, covar.emis = NULL, covar.trans = NULL,
link.ct = NULL,nc = 1,S0 = NULL)
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theta |
list of class MSAR including model parameters and a description of the model. See init.theta.MSAR 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. |
link.ct |
allows to specify a link function for non homogeneous transitions. |
nc |
allows to specify the components of the vector to be considered as covariates in the non homogeneous transitions (default is the first component). |
S0 |
initial state of the Markov chain if not null |
List including
..$Y |
simulated observation time series |
..$S |
simulated Markov chain |
Val\'erie Monbet, valerie.monbet@univ-rennes1.fr
fit.MSAR, init.theta.MSAR,valid_all
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 | data(meteo.data)
data = array(meteo.data$temperature,c(31,41,1))
k = 40
plot(data[,k,1],typ="l",xlab=("time (days)"),ylab=("temperature (Celsius degrees)"))
T = dim(data)[1]
N.samples = dim(data)[2]
d = dim(data)[3]
# Fit Homogeneous MS-AR models
M = 2
order = 2
theta.init = init.theta.MSAR(data,M=M,order=order,label="HH")
mod.hh = fit.MSAR(data,theta.init,verbose=TRUE,MaxIter=20)
# Simulation
yT = 31
Bsim = 1
Ksim = Bsim*N.samples
Y0 = array(data[1:2,sample(1:dim(data)[2],Ksim,replace=T),],c(2,Ksim,1))
Y.sim = simule.nh.MSAR(mod.hh$theta,Y0 = Y0,T,N.samples = Ksim)
# Validation
# valid_all(data,Y.sim$Y,id=1,alpha=.05)
## Not run
#data(lynx)
#lyt <- log10(lynx)
#T = length(lynx)
#Y = array(lyt,c(T,1,1))
#theta = init.theta.MSAR(Y,M=2,order=2,label='NH',nh.transitions="logistic",ncov.trans=1)
#Z = array(lyt[1:(T-2)],c(T-2,1,1))
#res=fit.MSAR(lyt[3:T],theta,covar.trans=Z,verbose=TRUE)
#Y0 = lyt[1:2]
#Bsim = 20
#Y0 = array(data[1:2,sample(1:dim(data)[2],Bsim,replace=TRUE),],c(2,Bsim,1))
#Y.sim = simule.nh.MSAR(res$theta,Y0 = Y0,T,N.samples = Bsim,covar.trans=2)
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