[R] powerTransform warning message?

John Fox jfox at mcmaster.ca
Fri Jul 17 02:16:11 CEST 2015


Dear Brittany,

On Thu, 16 Jul 2015 17:35:38 -0600
 Brittany Demmitt <demmitba at gmail.com> wrote:
> Hello,
> 
> I have a series of 40 variables that I am trying to transform via the boxcox method using the powerTransfrom function in R.  I have no zero values in any of my variables.  When I run the powerTransform function on the full data set I get the following warning. 
> 
> Warning message:
> In sqrt(diag(solve(res$hessian))) : NaNs produced
> 
> However, when I analyze the variables in groups, rather than all 40 at a time I do not get this warning message.  Why would this be? And does this mean this warning is safe to ignore?
> 

No, it is not safe to ignore the warning, and the problem has nothing to do with non-positive values in the data -- when you say that there are no 0s in the data, I assume that you mean that the data values are all positive. The square-roots of the diagonal entries of the Hessian at the (pseudo-) ML estimates are the SEs of the estimated transformation parameters. If the Hessian can't be inverted, that usually implies that the maximum of the (pseudo-) likelihood isn't well defined. 

This isn't surprising when you're trying to transform as many as 40 variables at a time to multivariate normality. It's my general experience that people often throw their data into the Box-Cox black box and hope for the best without first examining the data, and, e.g., insuring a reasonable ratio of maximum/minimum values for each variable, checking for extreme outliers, etc. Of course, I don't know that you did that, and it's perfectly possible that you were careful.

> I would like to add that all of my lambda values are in the -5 to 5 range.  I also get different lambda values when I analyze the variables together versus in groups.  Is this to be expected?
> 

Yes. It's very unlikely that both are right. If, e.g., the variables are multivariate normal within groups then their marginal distribution is a mixture of multivariate normals, which almost surely isn't itself normal.

I hope this helps,
 John

------------------------------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
	
	
> Thank you so much!
> 
> Brittany
> 	[[alternative HTML version deleted]]
> 
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