set.seed(1001)
N <- 1000
mean_pars <- c(1,2)
disp_pars <- c(1,2)
dd <- data.frame(x=rnorm(N))
m <- plogis(mean_pars[1]+mean_pars[2]*dd$x)
d <- exp(disp_pars[1]+disp_pars[2]*dd$x)
dd$y <- rbeta(N,shape1=m*d,shape2=(1-m)*d)Fit models:
## location only
m1 <- glmmTMB(y~x,
family=beta_family(),
data=dd)
## add model for dispersion
m2 <- update(m1,dispformula=~x)Fixed effects look close to theoretical values:
fixef(m2)##
## Conditional model:
## (Intercept) x
## 1.005 2.013
##
## Dispersion model:
## (Intercept) x
## 1.064 1.962AIC is insanely much better for the model with dispersion varying:
bbmle::AICtab(m1,m2)## dAIC df
## m2 0.0 4
## m1 1491.6 3
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