Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/cross.cor1.MSAR.R View source: R/cross.cor.MSAR.R

cross.cor.MSAR computes the cross-correlation between two components. The cross-corelation can be estimted for the whole time series or regime by regime.

1 2 3 |

`data` |
observed (or reference) time series, array of dimension T*N.samples*d |

`X` |
time series of regimes associated to data |

`nc1` |
first component to be considered |

`nc2` |
second component to be considered |

`lag` |
maximum lag (default=10). The cross-correlation is estimated for lags -lag:lag. |

`regime` |
has to be an integer between 0 and M, with M the number of regimes. If regime=0, the cross correlaiton is computed for the whole time series. If regime=m>0, the corss corelation is computed considereing only the sub-sequences in regime m. |

`CI` |
If CI=TRUE fluctuation intervals are computed, default is FALSE |

`Bsim` |
useful for computation of confidence intervals. When observed and simulated data are compared, one expects that the number of simulated time series is Bsim*N.samples |

`N.samples` |
useful for computation of confidence intervals. N.sample describes the number of independant time series in the observed (or reference) data |

`dt` |
default time step is equal to 1 |

`add` |
if add=TRUE the empirical cross-correlation is added to the current plot. |

`col` |
color of the line |

`names` |
list with the names of components of data |

`alpha` |
level for the computation of the fluctuation intervals. default=0.1 |

`ylab` |
legend for y axis |

`ylim` |
limit for y axis |

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

returns a list including:

`..$ccf` |
empirical cross-correlation |

`..$lag` |
abscissa for the cross-correlation |

`..$CI` |
fluctuation intervals |

Valerie Monbet, valerie.monbet@univ-rennes1.fr

Bessac, J., Ailliot, P., & Monbet, V. (2013). Gaussian linear state-space model for wind fields in the North-East Atlantic. arXiv preprint arXiv:1312.5530.

cor.MSAR, cor, valid_all

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data(Wind)
T = dim(U)[1]
c = cross.cor.MSAR(U,nc1=1,nc2=18,names=1:18)
## Not run
#Y = U[,,c(1,18)]
#theta.init=init.theta.MSAR(Y,M=2,order=2,label="HH")
#res.hh = fit.MSAR(Y,theta.init,verbose=TRUE,MaxIter=200)
#Bsim = 20
#N.samples = dim(U)[2]
#Ksim = Bsim*N.samples
#Y0 = Y0
#Y.sim = simule.nh.MSAR(res.hh$theta,Y0 = Y0,T,N.samples = Ksim)
#c.sim = cross.cor.MSAR(Y.sim$Y,nc1=1,nc2=2,names=c(1,18),
# CI=TRUE,Bsim=Bsim,N.samples=N.samples,add=TRUE,col="red")
``` |

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