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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

most R model- tting functions, this is the rst argument. The model formula consists of two expressions separated by the ˘symbol. The expression on the left, typically the name of a variable, is evaluated as the response. The right-hand side consists of one or more terms separated by ‘+’ symbols.

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