Booth2: A Logit-Normal GLMM Dataset

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

A Logit-Normal GLMM Dataset

Description

This data set contains simulated data from the paper of Booth and Hobert (referenced below) as well as another vector.

Usage

data(Booth2)

Format

A data frame with 3 columns:

y

Response vector.

x1

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

z1

A categorical vector to be used for part of the random effect model matrix.

z2

A categorical vector to be used for part of the random effect model matrix.

Details

The original data set was generated by Booth and Hobert using a single variance component, a single fixed effect, no intercept, and a logit link. This data set has the z2 vector added purely to illustrate an example with multiple variance components.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1467-9868.00176")}.

Examples

data(Booth2)

glmm documentation built on Sept. 30, 2024, 9:44 a.m.