[R-sig-ME] Most principled reporting of mixed-effect model regression coefficients
th|erry@onke||nx @end|ng |rom |nbo@be
Sat Feb 15 10:37:56 CET 2020
I tend to use qnorm(c(0.025, 0.975), mean = estimate, sd = SE)
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 za 15 feb. 2020 om 01:29 schreef Ades, James <jades using health.ucsd.edu>:
> Thanks, Thierry. This is what I was looking for!
> When I try confint(lme4_model) I get the following warning:
> Computing profile confidence intervals ...Error in zeta(shiftpar, start = opt[seqpar1][-w]) :
> profiling detected new, lower deviance
> Is there an easier way of extracting confidence intervals for fixed
> effects in lme4 than calculating them using the point estimate +/- Z * SE ?
> *From:* Thierry Onkelinx <thierry.onkelinx using inbo.be>
> *Sent:* Friday, February 14, 2020 1:47 AM
> *To:* Ades, James <jades using health.ucsd.edu>
> *Cc:* r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
> *Subject:* Re: [R-sig-ME] Most principled reporting of mixed-effect model
> regression coefficients
> Dear James,
> 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)"
> Best regards,
> 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,
> [[alternative HTML version deleted]]
> R-sig-mixed-models using r-project.org mailing list
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models