Based on Booth and Hobert (JRSS B, 1999), this function evaluates the maximum likelihood estimate of a simulated probit model with random effects. The random effects are simulated by a MCMC algorithm.
randogit(Tem = 10^3, Tmc = 10^2)
starting number of MCEM iterations
number of Monte Carlo points in the likelihood approximations
The function returns two plots, one of (beta,sigma) and one of the true likelihood L(beta,sigma,u0), where u0 is the true vector of random effects.
Christian P. Robert and George Casella
From Chapter 2 of EnteR Monte Carlo Statistical Methods
## Not run: randogit(20,10) #very small values to let the example run
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.