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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

In a linear mixed-e ects model the conditional distribution, YjB, and the marginal distribution, B, are independent, multivariate normal (or \Gaussian") distributions,

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