BookmarkSubscribeRSS Feed
deleted_user
Not applicable
Hi everyone,

I'm currently working with mixed models and I would require to apply a variance stabilizing transformation to my data. I tried applying the simple log transformation, but the result wasn't convincing. I was told that this transformation was part of a family called "Box-Cox transformations" (or simply power transformations). Applying such a transformation requires us specifying a value for a parameter called "lambda". This parameter can be chosen as to maximize the likelihood of the transformed data being as closely normally distributed as possible (with the parameters of this normal distribution being derived from the chosen model).

Does SAS offer a quick and efficient way to find the optimal value of this "lambda" parameter when the selected model is mixed?

Thanks for your help!
2 REPLIES 2
deleted_user
Not applicable
try proc nlmixed
Olivier
Pyrite | Level 9
I think that proc Transreg also provides support for determining optimal Box-Cox transformation.
See http://support.sas.com/documentation/cdl/en/statug/59654/HTML/default/statug_transreg_sect059.htm for details, even though statistical graphics & plots are not available prior to version 9.2.

Regards,
Olivier

SAS Innovate 2025: Call for Content

Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!

Submit your idea!

What is Bayesian Analysis?

Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.

Find more tutorials on the SAS Users YouTube channel.

Click image to register for webinarClick image to register for webinar

Classroom Training Available!

Select SAS Training centers are offering in-person courses. View upcoming courses for:

View all other training opportunities.

Discussion stats
  • 2 replies
  • 1242 views
  • 0 likes
  • 2 in conversation