randogit: MCEM resolution for a probit maximum likelihood

Description Usage Arguments Value Author(s) References Examples

View source: R/randogit.R

Description

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.

Usage

1
randogit(Tem = 10^3, Tmc = 10^2)

Arguments

Tem

starting number of MCEM iterations

Tmc

number of Monte Carlo points in the likelihood approximations

Value

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.

Author(s)

Christian P. Robert and George Casella

References

From Chapter 2 of EnteR Monte Carlo Statistical Methods

Examples

1
2
## Not run: randogit(20,10)
#very small values to let the example run

mcsm documentation built on May 30, 2017, 1:33 a.m.

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