cor.MSAR: Empirical correlation functions comparison .

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

Empirical correlation function of observed data and simulated data are plotted on the same figure. A fluctuation interval of simulations is added to help the comparison.

Usage

cor.MSAR(data,data.sim,lag=NULL,nc=1,alpha=.05,plot=FALSE,
   xlab="Time (days)",dt=1,ylab="Correlation",...)

Arguments

data	observed (or reference) time series, array of dimension T*N.samples*d
data.sim	simulated time series, array of dimension T*N.sim*d. N.sim have to be K*N.samples with K large enough (for instance, K=100)
lag	maximum lag at which to calculate the empirical auto-correlation function. Default floor(T/2) with T the length of each data sample.
nc	number of component for which to calculate the empirical auto-correlation function.
alpha	confidence level for computation of the fluctuation interval. Default= 0.05.
plot	if plot is TRUE plots are drawn (default is FALSE).
xlab	x axis label
dt	default time step is equal to 1
ylab	y axis label
...	for optional plot arguments

Details

The auto-correlation functions are computed from one or several independent realizations of the same length.

Value

A list with the following elements:

C.data	observed data acf
C.sim	simulated data acf
CI.sim	fluctuation interval for each lag
lags	abscissa for acfs

Author(s)

Valerie Monbet, [e-mail]

References

Bessac, J., Ailliot, P., Monbet, V. (2015). Gaussian linear state-space model for wind fields in the North-East Atlantic. Environmetrics, 26(1), 29-38.

See Also

cross.cor.MSAR, cor

Examples

## Not run
#data(Wind)
#T = dim(U)[1]
#N.samples = dim(U)[2]
#Y = array(U[,,1],c(T,N.samples,1))

#theta.init=init.theta.MSAR(Y,M=2,order=1,label="HH")
#res.hh = fit.MSAR(Y,theta.init,verbose=TRUE,MaxIter=10)
#Bsim = 2
#Ksim = Bsim*N.samples
#Y0 = array(Y[1,sample(1:dim(Y)[2],1,replace=T),],c(2,Ksim,1))
#Y.sim = simule.nh.MSAR(res.hh$theta,Y0 = Y0,T,N.samples = Ksim) 
#c = cor.MSAR(Y,Y.sim$Y)
#plot(c$lags/4,c$C.data,typ="l",xlab="Time (days)",ylab="ACF",xlim=c(0,8))
#abline(h=0,lty=3,col="gray")
#lines(c$lags/4,c$C.sim,col="red")
#lines(c$lags/4,c$CI.sim[1,],col="red",lty=2)
#lines(c$lags/4,c$CI.sim[2,],col="red",lty=2)

Example output

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.