Описание тега mixed-models

Overview

"Mixed models" refers to a class of models that are variously known as: mixed-effects models, multilevel models, hierarchical linear models,... This class of models was developed to account for correlation that may occur within nested data. A classic example is the estimation of test scores of students: if test scores are correlated within classes, schools, districts, etc., mixed models allow the modeler to simultaneously estimate the differences between individual students and between the groups to which they belong (with the possibility of including covariates at all levels).

References

StatsExchangers often recommend the following resources for learning more about mixed models:

• Modern Applied Statistics with S by Venables and Ripley (2002)
• "Random Effects Models for Longitudinal Data" (Biometrics 38:963—974) by Laird and Ware (1982)
• Analyzing linguistic data by Baayen (2008)
• Hierarchical Linear Models by Raudenbush and Bryk (2001)
• Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill (2006)
• Applied Longitudinal Data Analysis by Singer and Willett (2003).

Software packages

Mixed models are available in the following statistical packages:

• lme4 and nlme for R
• PROC MIXED and GLIMMIX for SAS
• MLwiN
• xtreg, xtmixed, xtlogit, xtmelogit, xtmepoisson, and other xt* commands; user-contributed package GLLAMM for Stata
• Mplus
• HLM

Tag usage

Questions on tag mixed-models should be about implementation and programming problems, not about the statistical or theoretical properties of the technique. Consider whether your question might be better suited to Cross Validated, the StackExchange site for statistics, machine learning and data analysis.