[R] Box-Cox / data transformation question

[Spencer Graves]

      What R commands were used to produce this estimate of the required 
transformation?  Did the command used produce a confidence interval for 
the power transformation, as, e.g., "boxcox" in library(MASS) described 
by Venables and Ripley (2002) Modern Applied Statistics with S, 4th ed. 
(Springer, sec. 6.8, p. 172)? 

      In particular, did the confidence interval include more common 
numbers like 0 or 0.5 or 1?  If you describe the application a bit (but 
still briefly), that information along with the confidence interval 
might elicit other useful comments. 

      hope this helps.  spencer graves
p.s  PLEASE do read the posting guide! 
http://www.R-project.org/posting-guide.html.  It  might help you 
formulate your question in a way that might elicit more useful answers. 

Landini Massimiliano wrote:

>On Sun, 30 Jan 2005 17:47:31 -0500, you wrote:
>
><<<<<-----------------SNIP
>|=[:o)  >
>|=[:o)  >  
>|=[:o)  >
>|=[:o)  Why are you using a double square root transformation? Is the 
>|=[:o)  transformation for the response variable? Transfromation is one way to 
>|=[:o)  help insure that the error distribution is at least approximately 
>|=[:o)  normal. So if this is the reason, it certainly could make sense. 
>
>Are you sure that (data^0.25) had sense??? Coud you explain me which is the
>sense??
>I know sense of boxcox exponents near zero when data are positively skewed and
>log(data) make it  normally distributed, or all those case where variances grow
>proportionally to means or when i know that there are interaction effects that
>not follow additive model (AnOVa assumption);
>
>I know 0.5 exponent (square root) [ as sqr(data) if all data differ from zero
>else sqr(data+.5) else Asconbe propose sqr(data +3/8) else Tukey & Freeman
>propose sqr(data)+sqr(data+1) particularly suitable when data domain is  (0,2) ]
>for right skewed data, frequently  applied to count-data or
>count-of-something-over -a -surface (bacteria, virus, nematode, lions) due to
>n*p*q (variance)  is almost proportional to its mean (n*p)  so AnOVa fundamental
>assumption is basically violated....
>
>I know 1/3 exponent  applied to count-of-something-in-a -volume...and so on...
>
>What is worth is that i'm trying to ask to Christoph to sit down and think: what
>kind of number are these??
>E.coli/mL?? ...so...i try cuberoot transformation and/or log transformation
>Timing of a slug vs snail speed race?? ...so..i think that inverse
>transformation it best.
>
>BoxCox procedure have produced a fantastic implement that can help many people
>but (IMHO) none procedure can be superior than Ripley + Bates + other gurus
>experience. If you ask to those great statisticians how do you manage
>electrophoresis velocity they could respond with "data^-1 why......blah blah
>blah"
>If you push data in BoxCox algorithm it will respond with "-0.97847164..."
>Which answer had more sense???
>I prefer -1
>
> 
>|=[:o) There  is no unique scale for making measurements. We choose a scale that helps 
>|=[:o)  us analyze the data appropriately.
>|=[:o)  
>|=[:o)  Rick B.
>|=[:o)  
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