[R-sig-ME] Help: Specifying truncation point in glmmTMB package

[Ben Bolker]

     I'm not 100% clear on your question, but: glmmTMB *only* does 
zero-truncation, not k-truncation with k>0, i.e. you can only specify 
the model

   Prob(x==0) = 0
   Prob(x>0) = Prob(NBinom(x))/Prob(NBinom(x>0))

(terrible notation, but hopefully you get the idea)



On 1/17/21 8:26 AM, Andre Syvertsen wrote:
> Hi,
> I am working with a large dataset that contains longitudinal data on gambling behavior of 184,113 participants. The data is based on complete tracking of electronic gambling behavior within a gambling operator. Gambling behavior data is aggregated on a monthly level, a total of 70 months. I have an ID variable separating participants, a time variable (months), as well as numerous gambling behavior variables such as active days played for given month, bets placed for given month, total losses for given month, etc. Participants vary in when they have been active gambling. One participant may have gambled at month 2, 3, 4, and 7, another participant at 3, 5, and 7, and a third at 23, 24, 48, 65 etc.
> I am attempting to run a negative binomial 2 truncated model in glmmTMB and I am wondering how the package handles lack of 0. I have longitudinal data on gambling behavior, days played for each month (for a total of 70 months). The variable can take values between 1-31 (depending on month), there are no 0. Participants� months with 0 are absent from dataset. Example of how data are structured with just two participants:
> # Example variables and data frame in long form
>    # Includes id variable, time variable and example variable
> id <- c(1, 1, 1, 1, 2, 2, 2)
> time <- c(2, 3, 4, 7, 3, 5, 7)
> daysPlayed <- c(2, 2, 3, 3, 2, 2, 2)
> dfLong <- data.frame(id = id, time = time, daysPlayed = daysPlayed)
> 
> My question: How do I specify where the truncation happens in glmmTMB? Does it default to 0? I want to truncate 0 and have run the following code (I am going to compare models, the first one is a simple unconditional one):
> DaysPlayedUnconditional <- glmmTMB(daysPlayed ~ 1 + (1 | id), dfLong, family = truncated_nbinom2)
> Will it do the trick?
> 
> 
> Kind regards,
> Andr� Syvertsen
> PhD student, University of Bergen
> 
> 
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