[R-sig-ME] spaMM::fitme() - a glmm for longitudinal data that accounts for spatial autocorrelation

Thierry Onkelinx th|erry@onke||nx @end|ng |rom |nbo@be
Tue Jul 14 20:00:09 CEST 2020

Dear François and Sarah,

INLA seems more efficient. I ran a model with Mattern correlation structure
on 13K locations (1 observation per location) in under 10 minutes on a
laptop with 16GB RAM.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

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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


Op di 14 jul. 2020 om 18:22 schreef Francois Rousset <
francois.rousset using umontpellier.fr>:

> Dear Sarah,
> Le 14/07/2020 à 16:55, Sarah Chisholm a écrit :
> > Hi Mollie, thank you for your suggestion. glmmTMB seems like a good
> > option for my needs as well. In your sample code above, can you
> > explain what the term 'group' does in matern(pos+0|group)? Does this
> > allow the spatial correlation structure to be applied to specific
> > groupings in the data (in my case, for example, by 'continent')?
> >
> > Francois, thank you for this very clear answer. This is a very
> > convenient feature of the function! May I ask you a couple of other
> > questions about some issues that I've had with spaMM::fitme()?
> >
> > In particular, when I try fitting this model to a large data set (~14
> > 000 rows x 7 columns, ~2 MB), the model will run for an extended
> > period of time, to the point where I've had to terminate the
> > computation. I've tried applying the suggestions that are mentioned in
> > the user guide, i.e. setting init=list(lambda=0.1)
> > and init=list(lambda=NaN). Implementing init=list(lambda=0.1) returned
> > an error suggesting that there was a lack of memory, while running the
> > model with init=list(lambda=NaN) also ran for an extended period of
> > time without completing. Is there something else I can do to speed up
> > the fit of these models?
> >
> > I've had a similar problem with an even larger data set (~185 000 rows
> > x 8 columns, ~21 MB), where, when I try running the model, this error
> > is returned immediately:
> >
> > ErrorinZA %*%xmatrix :Cholmoderror 'problem too large'at file
> > ../Core/cholmod_dense.c,line 105
> >
> > I've tried running this model on two devices, both with a 64-bit OS
> > with Windows 10, one with 32 GB of RAM and the other with 64 GB. I've
> > gotten the same error from both devices. Is there a way that fitme()
> > can accommodate these large data sets?
> spaMM can handle large data sets, but the first issue to consider here
> is the number of distinct locations for the spatial random effect. The
> large correlation matrices of geostatistical models will always be a
> problem, both in terms of memory requirements and of potentially huge
> computation times. My guess from past experiments is that one should
> still be able to fit models with ~ 10K locations within a few days on a
> computer with <60 Gb of RAM (given perhaps some tinkering of the
> arguments), so at least the data set of 14 000 rows should be feasible,
> particularly if the number of locations is smaller.
> Anyone planning to analyze large spatial data sets should anticipate
> these problems and check by themselves whether there is any practical
> alternative suitable for their particular problem. The discussion in
> section 6.2 of the "gentle introduction" to spaMM may then be useful.
> Best,
> F.
> >
> > Thank you,
> >
> > Sarah
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