BoothHobert: A Logit-Normal GLMM Dataset from Booth and Hobert

In glmm: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation

A Logit-Normal GLMM Dataset from Booth and Hobert

Description

This data set contains simulated data from the paper of Booth and Hobert referenced below.

Usage

data(BoothHobert)

Format

A data frame with 3 columns:

y

Response vector.

x1

Fixed effect model matrix. The matrix has just one column vector.

z1

Random effect model matrix. The matrix has just one column vector.

Details

This data set was generated by Booth and Hobert using a single variance component, a single fixed effect, no intercept, and a logit link.

References

Booth, J. G. and Hobert, J. P. (1999) Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B, 61, 265–285. doi: 10.1111/1467-9868.00176.

Examples

data(BoothHobert)

glmm documentation built on Oct. 10, 2022, 1:06 a.m.