[R-sig-ME] lmer formula specification

[Gjalt-Jorn Peters]

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     a variety of webpages, but some resources seem outdated, and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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