[R] Help! - boxcox transformations

From: Nuno Prista <nmprista_at_fc.ul.pt>
Date: Thu, 13 Dec 2007 20:56:09 -0500


Hope this does not sound too ignorant .  

I am trying to detrend and transform variables to achieve normality and stationarity (for time series use, namely spectral analysis). I am using the boxcox transformations.  

As my dataset contains zeros, I found I need to add a constant to it in order to run "boxcox". I have ran tests adding several types of constants, from .0001 (my unit of measurement) to 10 (still way below my maximum value). Most of my data concentrates in low values. I found the estimate of lambda changes drastically (from positive to negative) with the successive constants. I also found that normality (evaluated by running Shapiro and Jarque Bera test) after performing the suggested transformation obtained from "box-cox" is maximized for constant=0.0001(pvalues>0.7, lambda=0.2) and not for constant=1 (p-values <0.01, lambda=-0.94). Curiously, running the Shapiro tests with constant=1 and across lambda values, the highest p-value was obtained with lambda=0.2 (and not -0.94!)  

Why does box-cox() return lambda values that are so far from "creating" normality in the data? What type of best estimates are they? How should I choose the constant?  

I have skimmed (I am not a statistician.) through the Box-Cox(1964) paper and found no reference to this.  

So, any suggestion will be precious,  


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