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Re: st: Mixed effects model for asymmetric data


From   Richard Goldstein <richgold@ix.netcom.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Mixed effects model for asymmetric data
Date   Wed, 26 Sep 2012 14:46:20 -0400

Hi,

re: use of poisson, I suggest you look at the following blog:

http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell-a-friend/

Rich

On 9/26/12 2:15 PM, Ana Beatriz FS wrote:
> Thanks,  JVVerkuilen,
> 
> Unfortunately my variable is not a count one, I work with levels of hormones.
> 
> Following your suggestion, I assessed the quality of the model by the
> residuals and it's really really bad.
> 
> With respect to the transformations, they produce quite different
> distributions. I haven't find one that would fit all my points in
> time, even if not perfectly. I do think I have a problem here!
> 
> Best regards,
> 
> Ana Beatriz
> 
> 
> 2012/9/26 JVerkuilen (Gmail) <jvverkuilen@gmail.com>:
>> On Wed, Sep 26, 2012 at 1:13 PM, Ana Beatriz FS
>> <nutrianabeatriz@gmail.com> wrote:
>>> Dear Statalisters,
>>>
>>> I was performing multilevel mixed-effects linear regression but I
>>> realized my data is not normally distributed. Is there an equivalent
>>> model for asymmetric data in stata?
>>>
>>> I've tried to transform my data but I could not accomplish because my
>>> data require different transformations in each point of follow-up. My
>>> sample is composed by pregnant women in the three trimesters of
>>> pregnancy, but values of my dependent variable in first trimester
>>> require log transformation for normalization, in the second, sqrt
>>> transformation and so on.
>>
>> What are the measures? For instance, if they are counts, you might be
>> better off with -xtmepoisson-, which assumes that you have multilevel
>> Poisson distributed data. If the transformations are all fairly close,
>> you can probably get away with choosing one, so if the right answer is
>> sqrt and you log instead it won't be *that* far off. Also keep in mind
>> that the marginal distribution before conditioning on regressors can
>> be rather far from normal, so it's not really clear you need to do any
>> transformation. What do the residuals look like?
>>
>>
>> --
>> JVVerkuilen, PhD
>> jvverkuilen@gmail.com
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