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

Francois Rousset |r@nco|@@rou@@et @end|ng |rom umontpe|||er@|r
Mon Jul 13 22:01:05 CEST 2020

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?



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
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