Introduction Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects.

Estimation is more difficult in the mixed model than in the general linear model. Not only do you have as in the general linear model, but you have unknown parameters in , G, and R as well.

Scientists may wish to analyze correlated outcome data with constraints among the responses. For example, piecewise linear regression in a longitudinal data analysis can require use of a general ...

Smithsonian Libraries and Archives Object Details Author Zuur, Alain F Contents Limitations of linear regression applied on ecological data -- Things are not always linear : additive modeling -- ...

In recent years, generalized linear and nonlinear mixed-effects models have proved to be powerful tools for the analysis of unbalanced longitudinal data. To date, much of the work has focused on ...