acfM {dse1} | R Documentation |
Calculate a matrix with partitions [M0|...|Mi|...|Ml] giving the auto-covariance.
acfM(obj, ...) ## S3 method for class 'TSdata': acfM(obj, lag=round(6*log(periods(obj))), type ="covariance", sub.mean=TRUE, ...) ## S3 method for class 'TSmodel': acfM(obj, lag=NULL, type ="covariance", Psi=NULL, ...) ## S3 method for class 'TSestModel': acfM(obj, ...)
obj |
An object of class TSdata or TSmodel. |
lag |
Number of lags for which to calculate the autocorrelations. |
type |
With the defaults the blocks are auto-covariances. If type == 'correlation' the result is scaled to give autocorrelations. |
sub.mean |
Only valid if object is of class TSdata. If FALSE then means are not subtracted. |
Psi |
A matrix of innovation covariance. Only valid if object is of class TSmodel. |
... |
arguments passed to other methods. |
A matrix with partitions [M0|...|Mi|...|Ml] giving the covariance or correlation, including the that between the output and input series (as in the first block row of a Hankel matrix).
if(is.R()) data("eg1.DSE.data.diff", package="dse1") z <- acfM(eg1.DSE.data.diff) model <- TSmodel(toSS(estVARXls(eg1.DSE.data.diff))) # z <- acfM(model) not working