[R-sig-ME] lmer formula specification
Gjalt-Jorn Peters
gjalt-jorn at behaviorchange.eu
Mon Nov 5 18:04:01 CET 2012
Dear Alan,
this is great, thank you!!! I will add a comment to Nieuwenhuis' blog
linking to that page to help future searchers. Thank you very much again!
Kind regards,
Gjalt-Jorn Peters
On 05-11-2012 16:50, Alan Haynes wrote:
> Gjalt-Jorn,
>
> You might want to check out the Model specification section of
> http://glmm.wikidot.com/faq
>
> Between this and the Nieuwenhuis link you probably have everything you
> need...
>
> HTH
>
> Alan
>
> --------------------------------------------------
> Email: aghaynes at gmail.com <mailto:aghaynes at gmail.com>
> Mobile: +41794385586
> Skype: aghaynes
>
>
> On 5 November 2012 16:03, Gjalt-Jorn Peters
> <gjalt-jorn at behaviorchange.eu <mailto:gjalt-jorn at behaviorchange.eu>>
> wrote:
>
> Dear all,
>
> I posted a question last week, but I haven't received any replies
> - I'm not sure why (because there were no replies :-)), but hereby
> I try again, this time taking it one question at a time.
>
> I don't manage to find a clear explanation of the lmer model
> specification syntax. I haven't been able to find a webpage
> explaining this. I did find some archived posts of this list, and
> a variety of webpages, but some resources seem outdated, and
> explanations often seem to assume proficiency with mixed models
> and/or prior knowledge of some parts of the specification. Which
> is a bit of an obstacle when you're new to both R and lmer :-)
>
> Did I just overlook a resource, or does nothing exist yet and is
> this knowledge indeed kind of fragmented over the internet? I've
> been struggling with this for a few weeks now (not full-time
> though :-)), but perhaps I'm missing crucial search terms or
> something.
>
> If no source like this exists yet, could somebody perhaps correct
> my inferences? If one or more of you are willing to provide some
> feedback, I can hopefully write a tutorial/webpage thing (similar
> to, e.g.
> http://www.rensenieuwenhuis.nl/r-sessions-16-multilevel-model-specification-lme4/,
> but a bit more complete, and similar to Douglas Bates' article at
> http://cran.r-project.org/doc/Rnews/Rnews_2005-1.pdf, but a bit
> more geared towards relative lay people). From a variety of
> sources, I pieced together the following.
>
> The basic form is 'criterion ~ formula', where formula specifies
> the model you use to predict the criterion. This model consists of
> one or more terms separated by plusses (+). A term can be:
> -- 1 -> specifies that the intercept should be estimated. Is in
> fact optional as the intercept is always estimated;
> -- a variable name >- specifies that coefficient of that variable
> should be estimated (i.e. its slope);
> -- an interaction term, consisting of two or more variable names
> separated by colons -> specifies that the interactions between all
> those variables should be estimated, as well as their regular
> coefficients;
> -- a specification of random effects, which can take a number of
> forms:
> ---- (1 | variable name) -> for each level of variable name,
> random intercepts are estimated
> ---- (variable name 1 | variable name 2) -> for each level of
> variable name 2, random slopes are estimated for variable name 1,
> and random intercepts are estimated (note: '1 + ' implicit, see
> first bullet);
> ---- (0 + variable name 1 | variable name 2) -> for each level of
> variable name 2, random slopes are estimated for variable name 1,
> but only one (fixed) intercept is estimated;
> ---- (variable name 1 + variable 2 | variable name 3) -> for each
> level of variable name 3, random slopes are estimated for variable
> name 1 and variable name 2, and random intercepts are estimated;
> ---- (variable name 1 | variable name 3 : variable name 2) -> for
> each unique combination of levels of variable name 2 and variable
> name 3 (where variable name 3 is the higher level), random slopes
> are estimated for variable name 1;
> ---- (variable name 1 | variable name 2 / variable name 3) -> for
> each level of variable name 2, which is nested within variable
> name 3, random slopes are estimated for variable name 1;
>
> The next step would be to generate a series of potential scenarios
> and providing syntax for each scenario, like Rense Nieuwenhuis
> does at
> http://www.rensenieuwenhuis.nl/r-sessions-16-multilevel-model-specification-lme4/.
>
> I hope somebody either knows a resource that roughly does this, or
> thinks it may be nice to help make something like this!
>
> Kind regards, and thank you in advance,
>
> Gjalt-Jorn Peters
>
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