acfM {dse1} | R Documentation

## Calculate Auto-Covariance

### Description

Calculate a matrix with partitions [M0|...|Mi|...|Ml] giving the auto-covariance.

### Usage

        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, ...)

### Arguments

`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.

### Value

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).

### Examples

        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

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