A Logit-Normal GLMM Dataset from Booth and Hobert
This data set contains simulated data from the paper of Booth and Hobert referenced below.
A data frame with 3 columns:
Fixed effect model matrix. The matrix has just one column vector.
Random effect model matrix. The matrix has just one column vector.
This data set was generated by Booth and Hobert using a single variance component, a single fixed effect, no intercept, and a logit link.
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 (Statistical Methodology) 61, 265–285.