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

Mollie Brooks mo|||eebrook@ @end|ng |rom gm@||@com
Tue Jul 14 12:25:11 CEST 2020

Hi Sarah,

Sorry my reply is a bit late, but I think you could also fit matern and
other spatial correlation structures via glmmTMB. They are documented in
this vignette

I think the code might be something like

df2 <- transform(df,
    pos = numFactor(lat, long),
    group = factor(1)

M1 <- glmmTMB(y ~ year + class + (1|biome) + (1|continent) + (1|ID) +
matern(pos+0 | group), dispformula=~0, data = df2, REML=TRUE)

I don't have the most experience with this type of model, so maybe someone
else has more advice to give.


On Mon, Jul 13, 2020 at 8:31 PM Sarah Chisholm <schis023 using uottawa.ca> wrote:

> 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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