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Re: st: ln transform and box cox


From   Maarten Buis <maartenlbuis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ln transform and box cox
Date   Wed, 6 Mar 2013 16:58:49 +0100

Yes it does make a big difference. You could look at -xtgee- as a
panale data generalization of -glm-, and -xtgee- is an alowed program
in -fracpoly-. However, whether or not that model estimates what you
want it to estimate becomes very subtle question. See for example
chapter 13 of G.M. Fitzmaurice, N.M. Laird and J.H. Ware (2004)
"Applied Longitudinal Analysis". Hoboken: Wiley. Your data structure
certainly makes Box-Cox regression not an option.

Hope this helps,
Maarten

On Wed, Mar 6, 2013 at 4:43 PM, Thomas Norris wrote:
> Thank you both for your advice, I will get the book. As my models are using serially collected data in the same participant, I was fitting using xtmixed, not regress. Does this change things? Please excuse my ignorance.
>
> Many thanks,
>
> Tom
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of JVerkuilen (Gmail)
> Sent: 06 March 2013 15:36
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: ln transform and box cox
>
> 100% agree with Maarten and it's what I show in class now.
>
> However, if you want to do transformations I highly recommend getting your hands on a copy of A. C. Atkinson's Plots, Transformations & Regression (Oxford, 1986).
>
>
> On Wed, Mar 6, 2013 at 10:16 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> On Wed, Mar 6, 2013 at 4:07 PM, Thomas Norris wrote:
>>> I am running estimated fetal weight growth models. Weight shows increasing variance as gestation proceeds so I was advised to take the natural logarithm of weight to address this issue. However I am having doubts about whether this transformation is appropriate. Should I instead use a box cox transformation of the weight variable?
>>>
>>> If this is the case, how would I proceed with running the box cox? I have used the help and see that the syntax is straightforward, but I don't know how I would include the independent variables (age), as in my case, this is what I am trying to find out, ie which age term(s) best describe the data using the fracpoly command. With natural log weight, the best 2 degree fracpoly was with age terms 1 and 2 (fracpoly command didn't converge on raw scale), but if I put these in the box cox, they are based on natural logged data, which is the thing I am doubting.
>>
>> Generally, the advise on this list is not transform the
>> dependent/explained/response/left-hand-side/y variable but to use a
>> log-link function instead. See:
>> <http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell
>> -a-friend/>
>>
>> Your real problem problem appears to be the combination of -fracpoly-
>> and -boxcox-, which is not allowed. You can use either -glm- or
>> -poisson- with -fracpoly-, thus solving your problem.
>>
>> -- Maarten
>>
>> ---------------------------------
>> Maarten L. Buis
>> WZB
>> Reichpietschufer 50
>> 10785 Berlin
>> Germany
>>
>> http://www.maartenbuis.nl
>> ---------------------------------
>>
>> *
>> *   For searches and help try:
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>
>
>
> --
> JVVerkuilen, PhD
> jvverkuilen@gmail.com
>
> "It is like a finger pointing away to the moon. Do not concentrate on the finger or you will miss all that heavenly glory." --Bruce Lee, Enter the Dragon (1973)
>
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>
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-- 
---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany

http://www.maartenbuis.nl
---------------------------------

*
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