mixed effect model linear mixed model LMM Hanna Cwiek Philippe Rocca-Serra adapted from Wikipedia: https://en.wikipedia.org/wiki/Mixed_model A lnear mixed model is a mixed model containing both fixed effects and random effects and in which factors and covariates are assumed to have a linear relationship to the dependent variable. These models are useful in a wide variety of disciplines in the physical, biological and social sciences. They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units. Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA. Fixed-effects factors are generally considered to be the variables whose values of interest are all represented in the data file. Random-effects factors are variables whose values correspond to unwanted variation. They are useful when trying to understand variability in the dependent variable which was not anticipated and exceeds what was expected. Linear mixed models also allow to specify specific interactions between factors, and allow the evaluation of the various linear effect that a particular combination of factor levels may have on a response variable. Finally, linear mixed models allow to specify variance components in order to describe the relation between various random effects levels. Pawel Krajewski metadata complete