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Generalized linear mixed model
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<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, a generalized linear mixed model (GLMM) is an extension to the <a href="/facts/Generalized_linear_model/Sf1htm5W">generalized linear model</a> (GLM) in which the linear predictor contains <a href="/facts/Random_effects/FnWmvRsc">random effects</a> in addition to the usual <a href="/facts/Fixed_effects/L5JPDQnB">fixed effects</a>. They also inherit from generalized linear models the idea of extending <a href="/facts/Mixed_model/PovxPrAy">linear mixed models</a> to non-<a href="/facts/Normal_distribution/UapjjPyQ">normal</a> data. </p><p>Generalized linear mixed models provide a broad range of models for the analysis of grouped data, since the differences between groups can be modelled as a random effect. These models are useful in the analysis of many kinds of data, including <a href="/facts/Longitudinal_study/snyWEsXK">longitudinal data</a>. </p>