[R-sig-ME] Comparing variance components of crossed effects models fit with lme4 and nlme
Ben Bolker
bbolker at gmail.com
Wed Aug 16 20:10:52 CEST 2017
Most Bayesian approaches use the mean of the marginal posterior
distribution as the point estimate (although the median would be a
defensible choice as well). (I assume brms is using the mean but haven't
looked at the code/docs to confirm.) The maximum of the marginal
posterior distribution would also be fairly easy to compute, but doesn't
have as much meaning in the Bayesian case. The maximum (mode) of the
*multivariate* posterior distribution is the closest analogue of the
maximum likelihood estimate (with a completely uninformative prior it
would be identical), but is hard to compute.
On 17-08-16 01:21 PM, Joshua Rosenberg wrote:
> Thank you for helping me build a "model" around these ideas and
> distinctions.
>
> Regarding point 2 (below), for software such as brms, I notice that some
> kind of (sorry if my language is too loose here) estimate is produced
> for fixed effects parameters, as well as their standard errors. Based on
> brm's use (I think) of stochastic optimization and inferences based on
> means and distributions, do these estimates correspond to something like
> the maximum point - or rather to the mean of the posterior distribution?
>
> thank you very much again
> Josh
>
> On Mon, Aug 14, 2017 at 6:54 PM, Ben Bolker <bbolker at gmail.com
> <mailto:bbolker at gmail.com>> wrote:
>
> A couple of thoughts:
>
> (1) INLA *is* explicitly Bayesian, although I don't know what it
> specifies (implicitly or explicitly) for priors or whether it allows
> them to be user-adjusted (I'm too lazy to go look at the documentation
> or Google "INLA priors" right now ...)
> (2) it's worth making a distinction between
> (a) stochastic optimization (as in Bayesian MCMC, or frequentist
> Monte Carlo expectation-maximization (MCEM) approaches) and
> hill-climbing/deterministic optimization (as in INLA, or lme4, or
> glmmTMB -- anything that says it uses the Laplace approximation, or
> Gaussian quadrature ...)
> (b) inference based on a maximum point (MLE, or maximum a
> posteriori [MAP] estimates in the Bayesian world) and inferences based
> on means and distributions (MCMC). Typically the former goes with
> deterministic optimization and the latter goes with stochastic
> optimization
> (3) in addition to INLA, there are a variety of existing Bayesian
> machines in R (blme, MCMCglmm, brms, rstanarm ...) -- I think MCMCglmm
> and brms implement some flavours of (temporal) autoregression ...
>
> Depending on the kind of autoregressive structure you want, glmmTMB
> is also a possibility.
>
> On Mon, Aug 14, 2017 at 12:23 PM, Joshua Rosenberg
> <jmichaelrosenberg at gmail.com <mailto:jmichaelrosenberg at gmail.com>>
> wrote:
> > Thank you, I will explore use of INLA (or potentially the brms package
> > because of my familiarity with the [lme4-like] syntax).
> >
> > I'm curious whether you (or anyone else) has thoughts / advice on using a
> > package that uses a Bayesian approach for carrying out mixed effects
> > modeling. In my field / area of research, mixed effects models are new! And
> > so a Bayesian approach to them would be *very *new. Even though if I
> > understand (very preliminarily), with some (uniform) prior
> specification,
> > results can be comparable to models specified with a maximum
> likelihood
> > approach, when possible.
> >
> > Thank you again!
> > Josh
> >
> > On Fri, Aug 11, 2017 at 8:38 AM, Thierry Onkelinx
> <thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be>>
> > wrote:
> >
> >> Dear Joshua,
> >>
> >> Crossed random effects are difficult to specify in nlme. I think
> that you
> >> have to use pdBlocked() in the specification.
> >>
> >> When I need correlation I often use INLA (r-inla.org
> <http://r-inla.org>). It allows for
> >> correlated random effects. Crossed random effects are no problem.
> >>
> >> 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
> >>
> >> 2017-08-10 23:05 GMT+02:00 Joshua Rosenberg
> <jmichaelrosenberg at gmail.com <mailto:jmichaelrosenberg at gmail.com>>:
> >>
> >>> Hi all,
> >>>
> >>> I'm trying to fit models with a) crossed random effects and b) a
> specific
> >>> residual structure (auto-correlation). Based on my understanding
> of what
> >>> nlme and lme4 do well, I would normally turn to lme4 to fit a
> model with
> >>> crossed random effects, but because I'm trying to structure the
> residuals,
> >>> I am trying nlme.
> >>>
> >>> In trying to fit and compare the same variance components (no fixed
> >>> effects) model using lme4 and nlme, I found the output is
> similar but a
> >>> bit
> >>> different. Specifically, the standard deviations of the random
> effects and
> >>> the log-likelihood statistics are different. Would you expect
> the output
> >>> to
> >>> be a bit different?
> >>>
> >>> The models I fit to compare the output are here, though the
> output is also
> >>> here:
> >>>
> https://bookdown.org/jmichaelrosenberg/comparing_crossed_effects_models/
> <https://bookdown.org/jmichaelrosenberg/comparing_crossed_effects_models/>
> >>>
> >>>
> >>> library(lme4)
> >>> library(nlme)
> >>>
> >>> m_lme4 <- lmer(diameter ~ 1 + (1 | plate) + (1 | sample), data =
> >>> Penicillin)
> >>> m_lme4
> >>>
> >>> m_nlme <- lme(diameter ~ 1, random = list(plate = ~ 1, sample =
> ~ 1), data
> >>> = Penicillin)
> >>> m_nlme
> >>>
> >>>
> >>> Thank you for considering this question,
> >>> Josh
> >>>
> >>> --
> >>> Joshua Rosenberg, Ph.D. Candidate
> >>> Educational Psychology
> >>> &
> >>> Educational Technology
> >>> Michigan State University
> >>> http://jmichaelrosenberg.com
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
> >>> R-sig-mixed-models at r-project.org
> <mailto:R-sig-mixed-models at r-project.org> mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> <https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
> >>
> >>
> >>
> >
> >
> > --
> > Joshua Rosenberg, Ph.D. Candidate
> > Educational Psychology
> > &
> > Educational Technology
> > Michigan State University
> > http://jmichaelrosenberg.com
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org
> <mailto:R-sig-mixed-models at r-project.org> mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> <https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>
>
>
>
> --
> Joshua Rosenberg, Ph.D. Candidate
> Educational Psychology
> &
> Educational Technology
> Michigan State University
> http://jmichaelrosenberg.com <http://jmichaelrosenberg.com/>
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