DownloadThis package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". If the potential levels of the covariate are fixed and reproducible, e.g. the levels for Sex could be "F" and "M", they are modeled with fixed-effects parameters. If the levels constitute a sample from a population, e.g. the Subject or the Item at a particular observation, they are modeled as random effects.
Features
- A mixed-effects model contains both fixed-effects and random-effects terms
- With fixed-effects it is the coefficients themselves or combinations of coefficients that are of interest
- For random effects it is the variability of the effects over the population that is of interest
- For Windows, Linux, and macOS
- A mixed-effects model is a statistical model for a response variable as a function of one or more covariates
- Typical distribution forms are Bernoulli for binary data or Poisson for count data
Project Samples
