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

[Francois Rousset]

Le 13/07/2020 à 22:42, Sarah Chisholm a écrit :
> Thank you both for your reply.
>
> Thierry, the inlabru package sounds interesting. However, I should 
> have mentioned that I'm not very familiar with Bayesian statistics and 
> would prefer to use other methods if possible.
>
> Francois, I apologize for not contacting you directly first. To 
> clarify my question, when using the nlme::gls() function with 
> longitudinal data, it is necessary to group the data first. I'm 
> *pretty sure* this is to avoid having distances of zero in the 
> corSpatial object, although I'm not entirely sure of the details of 
> fitting this model.
>
> What I'm wondering is, will the fitme() function recognize that there 
> are repeated measurements through time on the same sites (and thus, 
> duplicates of the sites' coordinate points in the data set)
yes it does
> to avoid calculating distances of zero between the same site from 
> different years.

Internally, spaMM avoids zero distances (or rather, the singularities 
that would occur if different rows of a distance matrix represented the 
same location) by handling a distance matrix only among distinct spatial 
locations in the data.  There is no need to declare something like 
nlme::groupedData() to achieve this, and your call to spaMM::fitme() is 
OK. If there are non-zero but very close locations in the data, 
near-singularities may occur but spaMM also tries to deal with them 
automatically.

Best,

F.

> If I were to use lme4::lmer (for a normally distributed response 
> variable) without a spatial random effect, the model would look like this:
>
> M1 <- lmer(y ~ year + class + (1| biome ) + (1| continent ) + (1|ID) , 
> data = df, family = "gaussian" , REML = TRUE)
>
> Thanks so much,
>
> Sarah
>
> On Mon, Jul 13, 2020 at 4:01 PM Francois Rousset 
> <francois.rousset using umontpellier.fr 
> <mailto:francois.rousset using umontpellier.fr>> wrote:
>
>     Dear Sarah,
>
>     perhaps try to contact that package's author directly...
>
>     That being said, I am not quite sure what the question is, maybe
>     because
>     I am not familiar with constraints on the models nlme can fit and
>     with
>     its syntax. What would be the formula you would use with glmer if
>     there
>     were no spatial random effect?
>
>     Best,
>
>     F.
>
>     Le 12/07/2020 à 23:25, Sarah Chisholm a écrit :
>     > Hello,
>     >
>     > I'm trying to fit a GLMM that accounts for spatial
>     autocorrelation (SAC)
>     > using the spaMM::fitme() function in R. I have a longitudinal
>     data set
>     > where observations were collected repeatedly from a number of
>     sites over 13
>     > years. I'm interested in understanding what the effect of time
>     (year) is on
>     > the dependent variable (y), as well as the fixed effect of a
>     categorical
>     > variable (class) while accounting for the random factors biome,
>     continent,
>     > and ID (a unique ID for each site sampled). My full data set
>     contains ~ 180
>     > 000 rows and attached is a subset of these data ('sampleDF'). My
>     current
>     > fitme() model looks like this:
>     >
>     > library(spaMM)
>     >
>     > M1 <- fitme(y ~ year + class + (1|biome) + (1|continent) + (1|ID) +
>     > Matern(1|long + lat), data = df, family = "gaussian", method =
>     "REML")
>     > I have two questions:
>     >
>     > 1) I'm uncertain if this is an appropriate way of applying the
>     > spaMM::fitme() function to longitudinal data. I have some
>     experience with
>     > fitting GLS models that account for SAC to a longitudinal data
>     set where I
>     > had to group the data set by year using the nlme::groupedData()
>     function
>     > before fitting the model. Does a similar method need to be used
>     in the case
>     > of spaMM:fitme() and longitudinal data?
>     >
>     > 2) Is there another R package out there that can create a
>     similar model (a
>     > GLMM that accounts for SAC)?. I've found very few resources
>     explaining the
>     > use of functions in the spaMM package other than the user guide (F.
>     > Rousset, 2020. An introduction to the spaMM package for mixed
>     models) and
>     > I'm not quite getting the help that I need from it. I'm wondering if
>     > there's another approach to modeling these data that has a
>     broader user
>     > base and thus more easily accessible resources / online help
>     (ex. stack
>     > exchange / cross validated Qs and As).
>     >
>     > Thank you!
>     > Sarah
>     > _______________________________________________
>     > R-sig-mixed-models using r-project.org
>     <mailto:R-sig-mixed-models using r-project.org> mailing list
>     > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
>
> -- 
> Sarah Chisholm
> MSc Candidate
> Department of Biology
> University of Ottawa
> Linkedin <http://www.linkedin.com/in/sarah-chisholm-422a5785>

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