Dear Thierry,
please provide a reproducible example so that we know what you have
actually done.
Best,
F.
Le 14/07/2020 ? 20:00, Thierry Onkelinx a ?crit :
Dear Fran?ois 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
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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ensure that a reasonable answer can be extracted from a given body of data.
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Op di 14 jul. 2020 om 18:22 schreef Francois Rousset <
francois.rousset at umontpellier.fr>:
Dear Sarah,
Le 14/07/2020 ? 16:55, Sarah Chisholm a ?crit :
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.
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