mcemGLMMext: Extending the Iterations of a Model Fitted with mcemGLMM

In mcemGLM: Maximum Likelihood Estimation for Generalized Linear Mixed Models

Extending the Iterations of a Model Fitted with mcemGLMM

Description

Given a model fitted with the function mcemGLMM this function will add iterations and update the model estimates for more accurate results.

This is recommended if the initial fitting seems to have a large Monte Carlo error. This function will use the previous maximum likelihood estimate as its initial point and will also start with a Monte Carlo sample size equal to the sample size used in the last iteration of the previous fitting.

Usage

mcemGLMMext(object, it = 20, controlEM)

Arguments

object	an model fitted with mcemGLMM
it	the maximum number of iterations to be performed.
controlEM	a list. New set of options for the EM algorithm. Can be missing

Value

An updated object of class mcemGLMM.

Note

If controlEM is supplied it is important that the value for MCit is at least equal to number of Monte Carlo iterations used in the last EM step to fit object since providing a lower number will increase the Monte Carlo error.

Author(s)

Felipe Acosta Archila <[e-mail]>

See Also

mcemGLMM

Examples

set.seed(72327)
data(exdata)
fit1 <- mcemGLMM(obs ~ z2 + x, random = ~ 0 + z1, 
                 data = exdata, 
                 family = "bernoulli", vcDist = "normal", 
                 controlEM = list(verb = FALSE, EMit = 5, MCit = 8000), 
                 initial = c(-0.13, -0.15, -0.21, 1.59, 0.002))
                 
# Now we extend the algorithm to do at least another 10 iterations
fit2 <- mcemGLMMext(fit1, it = 10)

mcemGLM documentation built on April 3, 2023, 5:43 p.m.