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