Fitting Linear Mixed-Effects Models using lme4
Fitting Linear Mixed-Effects Models using lme4Douglas BatesUniversity of Wisconsin-MadisonMartin M chlerETH ZurichBenjamin M. BolkerMcMaster UniversitySteven C. WalkerMcMaster UniversityAbstractMaximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in Linear Mixed-Effects Models can be determined using thelmerfunction in thelme4package forR. As for most model - Fitting functions inR, the model is described inanlmercall by a formula, in this case including both fixed- and random -effects formula and data together determine a numerical representation of the model fromwhich the profiled deviance or the profiled REML criterion can be evaluated as a functionof some of the model parameters.
1.1. Linear mixed models Just as a linear model is described by the distribution of a vector-valued random response variable, Y, whose observed value is y obs, a linear mixed model is described by the distribution of two vector-valued random variables: Y, the response, and B, the vector of random effects.
Download Fitting Linear Mixed-Effects Models using lme4
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Using lme4: Mixed, Effects, Linear mixed, Models, Mixed, Random, Random effects, Effects models, Using STATA for mixed-effects models, Mixed models, Basic tutorial for performing linear mixed effects, Linear mixed effects, Introduction to Generalized Linear Mixed Models, Linear mixed models, Linear models, Linear Mixed Models with Random Effects, Effects linear, Linear, Mixed Effects, Effects models mixed