Statistical model containing both fixed effects and random effects
Not to be confused with mixture model.
Part of a series on
Regression analysis
Models
Linear regression
Simple regression
Polynomial regression
General linear model
Generalized linear model
Vector generalized linear model
Discrete choice
Binomial regression
Binary regression
Logistic regression
Multinomial logistic regression
Mixed logit
Probit
Multinomial probit
Ordered logit
Ordered probit
Poisson
Multilevel model
Fixed effects
Random effects
Linear mixed-effects model
Nonlinear mixed-effects model
Nonlinear regression
Nonparametric
Semiparametric
Robust
Quantile
Isotonic
Principal components
Least angle
Local
Segmented
Errors-in-variables
Estimation
Least squares
Linear
Non-linear
Ordinary
Weighted
Generalized
Generalized estimating equation
Partial
Total
Non-negative
Ridge regression
Regularized
Least absolute deviations
Iteratively reweighted
Bayesian
Bayesian multivariate
Least-squares spectral analysis
Background
Regression validation
Mean and predicted response
Errors and residuals
Goodness of fit
Studentized residual
Gauss–Markov theorem
Mathematics portal
v
t
e
A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects.[1][2] 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.[2] Mixed models are often preferred over traditional analysis of variance regression models because of their flexibility in dealing with missing values and uneven spacing of repeated measurements.[3] The Mixed model analysis allows measurements to be explicitly modeled in a wider variety of correlation and variance-covariance structures.
This page will discuss mainly linear mixed-effects models rather than generalized linear mixed models or nonlinear mixed-effects models.[4]
^Baltagi, Badi H. (2008). Econometric Analysis of Panel Data (Fourth ed.). New York: Wiley. pp. 54–55. ISBN 978-0-470-51886-1.
^ abGomes, Dylan G.E. (20 January 2022). "Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model?". PeerJ. 10: e12794. doi:10.7717/peerj.12794. PMC 8784019. PMID 35116198.
^Yang, Jian; Zaitlen, NA; Goddard, ME; Visscher, PM; Prince, AL (29 January 2014). "Advantages and pitfalls in the application of mixed-model association methods". Nat Genet. 46 (2): 100–106. doi:10.1038/ng.2876. PMC 3989144. PMID 24473328.
^Seltman, Howard (2016). Experimental Design and Analysis. Vol. 1. pp. 357–378.
mixedmodel, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. These models...
In statistics, a generalized linear mixedmodel (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random...
environment. Goleman's original model may now be considered a mixedmodel that combines what has since been modeled separately as ability EI and trait...
differences relate to that hierarchy. A random effects model is a special case of a mixedmodel. Contrast this to the biostatistics definitions, as biostatisticians...
model is a statistical model in which the model parameters are fixed or non-random quantities. This is in contrast to random effects models and mixed...
Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixedmodels, nested data models, random coefficient, random-effects...
include Bolivia, Costa Rica, Panama, Nicaragua and Venezuela. In the mixed-model there is an independent board to determine policy, but implementation...
multilevel models and as mixedmodel. In general, fitting GLMMs is more computationally complex and intensive than fitting GEEs. Generalized additive models (GAMs)...
general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that...
relationships between individuals based on their genotypes and use a linear mixedmodel to estimate the variance explained by the genetic markers. This gives...
generalization of simple differences) differ from the fixed-effects model. A mixed-effects model (class III) contains experimental factors of both fixed and random-effects...
establish mixed-model production lines. A production schedule based on MRP will tend to cope with high product mix by allocating each model to a multiple...
uction-Distribution+Planning.pdf OptimJ used in an optimization model for mixed-model assembly lines, University of Münster http://www.aaai.org/ocs/index...
control group). A mixed multivariate model can contain both discrete and continuous variables. For instance, a simple mixed multivariate model could have a...
Mixed logit is a fully general statistical model for examining discrete choices. It overcomes three important limitations of the standard logit model...
philosophies that support mixed economies include Keynesianism, social liberalism, state capitalism, fascism, social democracy, the Nordic model, and China's socialist...
statistics, best linear unbiased prediction (BLUP) is used in linear mixedmodels for the estimation of random effects. BLUP was derived by Charles Roy...
distribution Mixed logit Misconceptions about the normal distribution Misuse of statistics Mixed data sampling Mixed-design analysis of variance Mixedmodel Mixing...
ordinary differential equations Mixedmodel, also called linear mixed effects models (LMEMs), or shortened to linear mixedmodels (LMM) This disambiguation...
inverse of takt time. Besides volume, heijunka also means attaining mixed-model production, which however may only be feasible through set-up reduction...
contrasts, according to the model for the complete data set. In particular, REML is used as a method for fitting linear mixedmodels. In contrast to the earlier...
are a popular alternative to the likelihood–based generalized linear mixedmodel which is more at risk for consistency loss at variance structure specification...
negligence, with some states using a mixedmodel of comparative and contributory negligence. A state with a mixedmodel may, for example, prevent a plaintiff...
In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear response variable depends linearly on unknown smooth...
regression models a particular conditional quantile, for example the conditional median, as a linear function βTx of the predictors. Mixedmodels are widely...
longitudinal studies, potentially biasing the results. In these cases mixed effects models would be preferable as they can deal with missing values. Mean regression...