Greetings, Carla:
While it is possible to map any proper density into a normal through their
CDFs, that may not be useful in your case.
I suggest that you first plot your data.
?qqnorm
(Type ?qqnorm on the R command line and hit Enter.)
Are your data continuous, or do they occur in groups? Do the data curve?
Do they look like two (or more) distinct lines?
If your data have only one mode and if they are smooth then the Box-Cox
transform should provide a symmetrical result. Not all symmetrical
densities are normal, of course. And if your data are discrete then using a
continuous density like the normal (or Johnson family) is inappropriate.
The purpose of fitting a distribution to data is usually to permit some
probability statement, like Prob(x X) = alpha. Why do you want to use the
Johnson family? I am not aware of convenient methods for making such
probability statements for them.
Best wishes.
Charles Annis, P.E.
[EMAIL PROTECTED]
phone: 561-352-9699
eFax: 614-455-3265
http://www.StatisticalEngineering.com
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
[EMAIL PROTECTED]
Sent: Thursday, January 20, 2005 10:16 AM
To: r-help
Subject: [R] Johnson transformation
Hello,
I'm Carla, an italian student, I'm looking for a package to transform non
normal data to normality. I tried to use Box Cox, but it's not ok. There is
a package to use Johnson families' transormation? Can you give me any
suggestions to find free software as R that use this trasform?
Thank yuo very much
Carla
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