[R-sig-ME] Question on random effect

Thierry Onkelinx thierry.onkelinx at inbo.be
Wed Jul 12 10:02:50 CEST 2017


Dear Joaquin and Ben,

AFAIK have most random effects a term which is i.i.d. The random effects in
nlme and lme4 directly use the i.i.d. term: x_i ~ N(0, \sigma), hence the
random effect itself is i.i.d. INLA has some other constructs available.
e.g. a first order random walk where x_i - x_{i-1} ~ N(0, \sigma). Here the
difference between to consecutive random effects is i.i.d. but the random
effect itself isn't. The available options are listed at
http://www.r-inla.org/models/latent-models

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

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

2017-07-11 13:22 GMT+02:00 Ben Bolker <bbolker op gmail.com>:

> Yes, the assumption is that the random effects are (conditionally)
> independent.  It can help to specify covariates  (such as
> latitude/longitude or eastings/northings, or environmental conditions
> [temperature, elevation, etc.]) for sites to mop up some of the
> independence. It is theoretically possible, although I don't know of
> an easy off-the-shelf way to do it, to impose (e.g.) spatial
> correlation structures at the level of the random effects ...  or you
> could examine the spatial dependence of the conditional modes/random
> effects and try to convince yourself it was weak ...
>
> On Tue, Jul 11, 2017 at 7:14 AM, Joaquín Aldabe
> <joaquin.aldabe op gmail.com> wrote:
> > Hi all, when working with mixed models, do the levels of the random
> effect
> > have to be independent? For example, if my random effect is the identity
> of
> > sites and it is associated to the intercept, do sites have to be
> > independent?
> >
> > I appreciate comments and bibliographic references.
> >
> > Thank you very much in advanced,
> >
> > Joaquin
> >
> > --
> > *Joaquín Aldabe*
> >
> > *Grupo Biodiversidad, Ambiente y Sociedad*
> > Centro Universitario de la Región Este, Universidad de la República
> > Ruta 15 (y Ruta 9), Km 28.500, Departamento de Rocha
> >
> > *Departamento de Conservación*
> > Aves Uruguay
> > BirdLife International
> > Canelones 1164, Montevideo
> >
> > https://sites.google.com/site/joaquin.aldabe
> > <https://sites.google.com/site/perfilprofesionaljoaquinaldabe>
> >
> >
> >
> >
> > <https://www.avast.com/sig-email?utm_medium=email&utm_
> source=link&utm_campaign=sig-email&utm_content=webmail>
> > Libre
> > de virus. www.avast.com
> > <https://www.avast.com/sig-email?utm_medium=email&utm_
> source=link&utm_campaign=sig-email&utm_content=webmail>
> > <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models op r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> _______________________________________________
> R-sig-mixed-models op r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]



More information about the R-sig-mixed-models mailing list