[R-sig-ME] Most principled reporting of mixed-effect model regression coefficients
th|erry@onke||nx @end|ng |rom |nbo@be
Fri Feb 14 10:47:30 CET 2020
IMHO the estimate and its CI works best. They instantly provide the range
of uncertainty around the estimate without the reader having to do the
math. CI also work with skewed distributions. p-values don't offer much
added value over a CI.
Below are a few examples of four estimates and their uncertainties. The
first line displays the estimate and its SE. The second line displays the
estimate, SE and p-values. The third displays the estimate and a relative
error. While the last one displays the estimate and 95% CI.
Keep in mind that readers are more likely to understand CI rather than SE.
"1.2 ± 0.3" "10.5 ± 4.5" "0.0 ± 0.3" "0.0 ± 5.0"
"1.2 ± 0.3 (p = 0.0001)" "10.5 ± 4.5 (p = 0.0196)" "0.0 ± 0.3 (p =
1.0000)" "0.0 ± 5.0 (p = 1.0000)"
"1.2 ± 25.0%" "10.5 ± 42.9%" "0.0 ± Inf%" "0.0 ± Inf%"
"1.2 (0.6; 1.8)" "10.5 (1.7; 19.3)" "0.0 (-0.6; 0.6)" "0.0 (-9.8; 9.8)"
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
Op vr 14 feb. 2020 om 09:31 schreef Ades, James <jades using health.ucsd.edu>:
> Hi all,
> It’s been surprisingly difficult to find the most principled reporting of
> mixed-effect model regression coefficients (for individual fixed-effects).
> One stack overflow article lead me to this paper—a systematic review of the
> incorporating and reporting of GLMMs (
> which references a paper by Ben Bolker (
> Oddly, I don’t really find an answer to this in either of those. I’ve heard
> mixed things regarding fixed effect coefficients in LMM (that LMM/and GLMMs
> are more about the predictive power of an entire model than the individual
> predictors themselves), but overall, my understanding is that it’s kosher
> (and informative) to look at effect sizes of regression (fixed effect)
> coefficients—only that lme4 doesn’t currently provide p values (though
> Lmertest does).
> It seems like reporting effect size of regression coefficients and their
> SEs should suffice; though sometimes people report CI with those as well
> (but isn’t that a little redundant). My PI is telling me to include
> p-values. So many different things, so little agreement.
> I figured I’d turn here for something of a “definitive” answer.
> Ben, I definitely need to go back and read through your paper more
> thoroughly for a deeper understanding of the nuances of GLMMs. Currently
> watching—and reading—McElreath’s Statistical Rethinking, but I’m not quite
> at the level of implementing MCMCs.
> Much thanks,
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> R-sig-mixed-models using r-project.org mailing list
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