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From |
"JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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 <richgold@ix.netcom.com> 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) <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 > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- JVVerkuilen, PhD jvverkuilen@gmail.com "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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Mixed effects model for asymmetric data***From:*Ana Beatriz FS <nutrianabeatriz@gmail.com>

**Re: st: Mixed effects model for asymmetric data***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**Re: st: Mixed effects model for asymmetric data***From:*Ana Beatriz FS <nutrianabeatriz@gmail.com>

**Re: st: Mixed effects model for asymmetric data***From:*Richard Goldstein <richgold@ix.netcom.com>

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