mcemGLM-package: Generalized Linear Mixed Model Estimation via Monte Carlo EM

In mcemGLM: Maximum Likelihood Estimation for Generalized Linear Mixed Models

Generalized Linear Mixed Model Estimation via Monte Carlo EM

Description

mcemGLM performs maximum likelihood estimation for logistic, Poisson, and negative binomial regression when random effects are present. The package uses an MCEM algorithm to estimate the model's fixed parameters and variance components with their respective standard errors.

A Wald test based anova is available to test significance of multi-leveled variables and for multiple contrast testing.

Details

Package:	mcemGLM
Type:	Package
Version:	1.1.2
Date:	2023-01-12
License:	GPL (>= 2)

Author(s)

Felipe Acosta Archila

Maintainer: Felipe Acosta Archila <[e-mail]>

Examples

set.seed(123)
x <- rnorm(30, 10, 1)
z <- factor(rep(1:6, each = 5))
obs <- sample(0:1, 30, TRUE)
fit <- mcemGLMM(obs ~ x, random = ~ 0 + z, family = "bernoulli",
vcDist = "normal", controlEM = list(EMit = 15, MCit = 10000), 
initial = c(3.30, -0.35, 0.005))
summary(fit)
anova(fit)

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