[R-sig-ME] Comparing variance components of crossed effects models fit with lme4 and nlme

[Ben Bolker]

  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> 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>
> 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). 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>:
>>
>>> 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/
>>>
>>>
>>> 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 mailing list
>>> 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]]
>
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