checkResiduals {dse1} | R Documentation

## Autocorrelations Diagnostics

### Description

Calculate autocorrelation diagnostics of a time series matrix or TSdata or residuals of a TSestModel

### Usage

        checkResiduals(obj, ...)
        ## Default S3 method:
        checkResiduals(obj, ac=TRUE, pac=TRUE, select=seq(nseries(obj)),
                       drop=NULL, plot.=TRUE, graphs.per.page=5, verbose=FALSE, ...)
        ## S3 method for class 'TSdata':
        checkResiduals(obj, ...)
        ## S3 method for class 'TSestModel':
        checkResiduals(obj, ...)

### Arguments

`obj` | An TSestModel or TSdata object.
`ac` | If TRUE the auto-correlation function is plotted.
`pac` | If TRUE the partial auto-correlation function is plotted.
`select` | Is used to indicate a subset of the residual series. By default all residuals are used.
`drop` | Is used to indicate a subset of the residual time periods to drop. All residuals are used with the default (NULL).Typically this can be used to get rid of bad initial conditions (eg. drop=seq(10) ) or outliers.
`plot.` | If FALSE then plots are not produced.
`graphs.per.page` | Integer indicating number of graphs to place on a page.
`verbose` | If TRUE then the auto-correlations and partial auto-correlations are printed if they are calculated.
`...` | arguments passed to other methods.

### Details

This is a generic function. The default method works for a time series matrix which is treated as if it were a matrix of residuals. However, in a Box-Jenkins type of analysis the matrix may be data which is being evaluated to determine a model. The method for a TSestModel evaluates the residuals calculated by subtracting the output data from the model predictions.

### Value

A list with residual diagnostic information: residuals, mean, cov, acf= autocorrelations, pacf= partial autocorrelations.

### Examples

        if(is.R()) data("eg1.DSE.data.diff", package="dse1")
        model <- estVARXls(eg1.DSE.data.diff)
        checkResiduals(model)

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