Figure 4

Simulation study: Discriminatory power of DIC and $\stackrel{\mathbf{¯}}{\mathit{\text{LS}}}$ for different scenarios (100 runs per scenario). Data generating process: longitudinal, negative binomial counts with subject-specific intercept (balanced design); modeling strategy: Poisson GLMM with random intercept; number of subjects per group: n = 20,50,100; degree of overdispersion: k = 0.5,1,5,10,20,50. As k→∞, the degree of overdispersion decreases and the negative binomial converges to a Poisson distribution. Hence, DIC and $\stackrel{¯}{\mathrm{LS}}$ decline. Note that the range of DIC increases in the case of a larger sample size.