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Re: st: Mixed effects model for asymmetric data
From
"JVerkuilen (Gmail)" <[email protected]>
To
[email protected]
Subject
Re: st: Mixed effects model for asymmetric data
Date
Wed, 26 Sep 2012 15:41:08 -0400
This is really nice. Thanks for the link. I have used Stata's Poisson
and nbreg commands with non-discrete variables.
Jay
On Wed, Sep 26, 2012 at 2:46 PM, Richard Goldstein
<[email protected]> wrote:
> 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) <[email protected]>:
>>> On Wed, Sep 26, 2012 at 1:13 PM, Ana Beatriz FS
>>> <[email protected]> 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
>>> [email protected]
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--
JVVerkuilen, PhD
[email protected]
"Out beyond ideas of wrong-doing and right-doing there is a field.
I'll meet you there. When the soul lies down in that grass the world
is too full to talk about." ---Rumi
*
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