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

[Sarah Chisholm]

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) to avoid calculating
distances of zero between the same site from different years. 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> 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 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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