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?