Using lme4: Mixed-Effects Modeling in R

SimpleLongitudinalInteractionsTheoryGLMM Item ResponseNLMMU sing lme4 : Mixed-Effects Modeling in RDouglas BatesUniversity of Wisconsin - Madisonand R Development Core 11, 2008 SimpleLongitudinalInteractionsTheoryGLMM Item ResponseNLMMO utlineOrganizing and plotting data; simple, scalar random effectsModels for longitudinal dataInteractions of grouping factors and other covariatesEvaluating the log-likelihoodGeneralized Linear mixed ModelsItem Response Models as GLMMsNonlinear mixed ModelsSimpleLongitudinalInteractionsTheo ryGLMMItem ResponseNLMMO utlineOrganizing and plotting data; simple, scalar random effectsModels for longitudinal dataInteractions of grouping factors and other covariatesEvaluating the log-likelihoodGeneralized Linear mixed ModelsItem Response Models as GLMMsNonlinear mixed ModelsSimpleLongitudinalInteractionsTheo ryGLMMItem ResponseNLMMO utlineOrganizing and plotting data; simple, scalar random effectsModels for longitudinal dataInteractions of grouping factors and other covariatesEvaluating the log-likelihoodGeneralized Linear mixed ModelsItem Response Models as GLMMsNonlinear mixed ModelsSimpleLongitudinalInteractionsTheo ryGLMMItem ResponseNLMMO utlineOrganizing and plotting data; simple, scalar random effectsModels for longitudinal dataInteractions of grouping factors and other covariatesEvaluating the log-likelihoodG

e ects because they are not parameters. One answer to the question, \so what are those numbers anyway?" is that they are BLUPs (Best Linear Unbiased Predictors) but that answer is not informative and the concept does not generalize. A better answer is that those values are the conditional means, E[BjY = y], evaluated at the estimated parameters.

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