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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-
Landini dr. Massimiliano
Tel. mob. (+39) 347 140 11 94
Tel./Fax. (+39) 051 762 196
e-mail: numero (dot) primo (at) tele2 (dot) it
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