glmmTMB has the capability to simulate from a fitted model. These simulations resample random effects from their estimated distribution. In future versions of
glmmTMB, it may be possible to condition on estimated random effects.
library(glmmTMB) library(ggplot2); theme_set(theme_bw())
Fit a typical model:
data(Owls) owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1, ziformula = ~1, data=Owls)
Then we can simulate from the fitted model with the
simulate.glmmTMB function. It produces a list of simulated observation vectors, each of which is the same size as the original vector of observations. The default is to only simulate one vector (
nsim=1) but we still return a list for consistency.
simo=simulate(owls_nb1, seed=1) Simdat=Owls Simdat$SiblingNegotiation=simo[] Simdat=transform(Simdat, NegPerChick = SiblingNegotiation/BroodSize, type="simulated") Owls$type = "observed" Dat=rbind(Owls, Simdat)
Then we can plot the simulated data against the observed data to check if they are similar.
ggplot(Dat, aes(NegPerChick, colour=type))+geom_density()+facet_grid(FoodTreatment~SexParent)