[R-sig-ME] ar1 structure too big, alternate formulation?

Mollie Brooks mo|||eebrook@ @end|ng |rom gm@||@com
Thu Mar 10 15:08:24 CET 2022


I’m working on a data set where individually tagged cod were observed on ship wrecks (present or absent). We aggregated everything to an hourly scale. We include an AR1 random effect to account for the idea that being present at a particular wreck in one hour makes an individual fish more likely to be present in the next hour. The variable `ID_Wreck` is the combination of fish tag ID and wreck where they were observed; we dropped combinations that were never observed.

dat = na.omit(transform(dPresence1, 
	time = factor(DateTimeCEST_Hourly),
	ID_Wreck = factor(paste(Tag_ID, Wreck, sep="_")))[,c("Presence","Speed_10m","Direction_10m","SeaLevel", "Temp_10m","Diel","time","ID_Wreck")])

There are 506 levels of `ID_Wreck`, each with up to 3550 hourly observations (min 214, median 3233).

I think I want to fit a model like

M0 <- glmmTMB(Presence ~ environ + ar1(time +0| ID_Wreck), family=binomial, data=dat)

where environ is some combination of fixed effects.

Even without any fixed effects, the AR1 random effect seems to cause a problem with the size of the structure.

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 't': missing value where TRUE/FALSE needed
In addition: Warning message:
In attributes(.Data) <- c(attributes(.Data), attrib) :
  NAs introduced by coercion to integer range

>From what I’ve read, the last part is related to trying to allocate a data frame that is bigger than R can handle (I’m allocating 100GB on a cluster).
https://stackoverflow.com/questions/55183203/how-to-create-data-frame-for-super-large-vectors <https://stackoverflow.com/questions/55183203/how-to-create-data-frame-for-super-large-vectors>

Is there a way to reformulate this problem so that the structures are small enough for R to handle?

What if I instead include a fixed effect of presence/absence in the previous hour? Is there a reference for how that is related to the random effect model (I know it’s easy, but references help)?


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