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From |
Rebecca Pope <rebecca.a.pope@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: ln transform and box cox |

Date |
Thu, 7 Mar 2013 08:45:22 -0600 |

David's advice about not modeling all non-linear relationships as quadratic is good advice. However, I want to make sure that my point earlier about -fracpoly- was not misunderstood. What I'm not saying: You must use a quadratic function. Rereading my first post, I realize that the throw-away sentence about which is more common may have sounded like advice to default to that. It should not be taken as such. That's what loose language gets you. I'm sorry. What I am saying: You do not need to go mining through your data with a host of functions and see which one happens to fit best. For a variety of reasons I think this is bad practice, but my opinion on that doesn't matter much. More importantly, if you understand the general relationship between your variables, you should have an idea of some functions that are likely candidates. I offered 2 functions that I have seen applied to age. Jay has suggested splines, which I (clearly) didn't think of, but is an excellent suggestion. There may well be others that suit fetal growth, however, it seems like there should be a self-limiting set that are reasonable and easily interpretable. Others may not value that last point. If you have a limited set of functions selected based on theory/prior knowledge/just looked at the data, it is possible to select the best one yourself and hence no need to artificially constrain yourself to Stata functions that will work with -fracpoly-. On Wed, Mar 6, 2013 at 4:31 PM, David Hoaglin <dchoaglin@gmail.com> wrote: > Also, you should examine the choice of functional form for age in the > context of the model that contains all the explanatory variables that > you plan to use. The adjustments for those explanatory variables may > affect the apparent pattern of the relation of the dependent variable > to age. Yes. And this also brings us back to Tom's concern about increasing variance as the subjects aged. This is, I think, motivated by a fear that non-constant Var(weight) indicates that the error variance is not constant over time (i.e. not homoskedastic). My understanding is that this concern, unlike something like the functional form, can't be assessed in the absence of conditioning on the explanatory variables. As Anthony noted, one of the wonderful things about the list as that you get great discussions around a topic, not just focused help-line responses, so I'm going to try to take advantage of that. Given the set up of the study, is age not analagous to time? If so, is it not true then that Var(weight) should be, by definition, increasing with age? So, one couldn't directly draw conclusions about violated assumptions just looking at Var(weight) plotted against age. Specifically, time (=age) needs to be explicitly incorporated into the random part of the model. As a skeleton: xtmixed weight age xvars || subject: age where Tom would add whatever appropriate functional form of age, options, and additional variables (xvars) were needed. Regards, Rebecca On Wed, Mar 6, 2013 at 7:52 PM, JVerkuilen (Gmail) <jvverkuilen@gmail.com> wrote: > On Wed, Mar 6, 2013 at 7:15 PM, Anthony Fulginiti <fulginit@usc.edu> wrote: >> Although I did not post the initial question, I welcome the helpful book reference. I have been using the Singer and Willett book, "Applied Longitudinal Data Analysis" but looked at Fitzmaurice website >> and found it to be another great source. Related to the post: I followed a similar modeling strategy as Tom (specifying the polynomial function of time that best fit the data first and subsequently adding >> variables of central interest and controls) but plan to reexamine the issue with the explanatory vars in the model. > > Regression splines may be a good way to go about getting a flexible > specification for age. If you know that there are a few key ages, put > knots there. > > > -- > 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) > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: ln transform and box cox***From:*Thomas Norris <T.Norris2@lboro.ac.uk>

**Re: st: ln transform and box cox***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: ln transform and box cox***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**RE: st: ln transform and box cox***From:*Thomas Norris <T.Norris2@lboro.ac.uk>

**Re: st: ln transform and box cox***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: ln transform and box cox***From:*Rebecca Pope <rebecca.a.pope@gmail.com>

**RE: st: ln transform and box cox***From:*Thomas Norris <T.Norris2@lboro.ac.uk>

**Re: st: ln transform and box cox***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: ln transform and box cox***From:*Anthony Fulginiti <fulginit@usc.edu>

**Re: st: ln transform and box cox***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

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