>>>>> "EC" == Emmanuel Charpentier <charp...@bacbuc.dyndns.org>
>>>>> on Sun, 18 Apr 2010 11:29:29 +0200 writes:
EC> Le vendredi 16 avril 2010 à 00:15 -0800, Kay Cichini a
EC> écrit :
>> thanks thierry,
>>
>> i considered this transformations already, but variance
>> is not stabilized and/or normality is neither achieved.
>> i guess i'll have to look out for non-parametrics?
EC> Or (maybe) a model based on a non-Gaussian likelihood ?
EC> A beta distribution comes to mind, either fitted by
EC> maximum likelihood or (if relevant prior information is
EC> available) in a Bayesian framework ?
EC> But beware : you have a not-so-small problem ...
EC> Your data have zeroes and ones, which, if you have no
EC> information on a "sample size", are "sharp" zeroes and
EC> ones, and there therefore theoretically bound to
EC> infinite linear predictors (in plain English : bloody
EC> unlikely). These values make a "fixed effect" analysis
EC> impossible : these points "at infinite" will make
EC> regression essentially impossible. Consider :
>> logit<-function(x)log(x/(1-x))
>> ilogit<-function(x)1/(1+exp(-x))
Hmmm, and some CRAN packages even define these ..
Now, please, the help page ?Logistic
has contained for a long time now
>> Note:
>>
>> ‘qlogis(p)’ is the same as the well known ‘_logit_’ function,
>> logit(p) = log(p/(1-p), and ‘plogis(x)’ has consequently been
>> called the ‘inverse logit’.
So please "note", and do use qlogis() and plogis() instead of
logit() and ilogit() ...
or if you really really must (e.g. for didactical reasons), use
logit <- qlogis
Using the logistic functions directly may also remind you or
your user that sometimes it will be advantageous to use
'log.p=TRUE' or 'lower.tail=FALSE' ``coordinate systems"
Martin
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