Box-Cox Analysis for a Time Series
An AR(p) model is selected using AIC and then the best Box-Cox transformation is determined. Requires package FitAR.
## S3 method for class 'numeric' BoxCox(object, interval = c(-1, 1), IIDQ = FALSE, ...)
object |
a vector of time series values |
interval |
interval to be searched |
IIDQ |
If true, IID is assumed, ie. p=0. If FALSE, AR(p) is fit with p determined using AIC. |
... |
optional arguments |
For lambda!=0, the Box-Cox transformation is of x is (x^lambda-1)/lambda.
If the minimum data value is <= 0, a small positive constant, equal to the negative of the minimum plus 0.25, is added to all the data values. If length(object) < 20, no AR model is used, that is, p=0.
No value returned. Graphical output produced as side-effect. The plot shows relative likelihood funciton as well as the MLE and a confidence interval.
The MASS package has a similar function boxcox but this is implemented only for regression and analysis of variance.
A.I. McLeod and Y. Zhang
Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. Journal of Royal Statistical Society, Series B, vol. 26, pp. 211-246.
## Not run: #takes a few seconds #annual sunspot series BoxCox(sunspot.year, IIDQ=FALSE) # #non-time series example, lengths of rivers BoxCox(rivers) ## End(Not run)
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