spaMM::fitme() - a glmm for longitudinal data that accounts for spatial autocorrelation

Le 13/07/2020 ? 22:42, Sarah Chisholm a ?crit?:
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 at umontpellier.fr 
<mailto:francois.rousset at 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 ? 23:25, Sarah Chisholm a ?crit?:
    > 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 at r-project.org
    <mailto:R-sig-mixed-models at 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>

spaMM::fitme() - a glmm for longitudinal data that accounts for spatial autocorrelation

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