Everything about Mixed model
Statistical model containing both fixed effects and random effects
A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. 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, or where measurements are made on clusters of related statistical units. Mixed models are often preferred over traditional analysis of variance regression models because they don't rely on the independent observations assumption. Further, they have their flexibility in dealing with missing values and uneven spacing of repeated measurements. The Mixed model analysis allows measurements to be explicitly modeled in a wider variety of correlation and variance-covariance avoiding biased estimations structures. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Mixed modelA 5-minute orientation
- Skim a recent paper: glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed modelsInfluential work since 2022
- Pick up a classic book: Mathematical Modeling of Concrete Mixture ProportioningThe most-reprinted book on the subject
- Read the landmark paper: Fitting Linear Mixed-Effects Models Using lme4Cited 80,837 times
- Go sideways: Multilevel modelA closely related topic
- Go sideways: Fay–Herriot modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Fitting Linear Mixed-Effects Models Using lme4
lmerTest Package: Tests in Linear Mixed Effects Models
MrBayes 3: Bayesian phylogenetic inference under mixed models
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
Approximate Inference in Generalized Linear Mixed Models
A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
Generalized linear mixed models: a practical guide for ecology and evolution
🆕 Influential since 2022
glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models
Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research
A mixed traffic car-following behavior model
Model Specification in Mixed-Effects Models
Generalized quasi-linear mixed-effects model
📚 Books worth reading
- 📕Mathematical Modeling of Concrete Mixture ProportioningGanesh Babu Kodeboyina · 2020
- 📕Simulations of diffusion-reaction equations with implications to turbulent combustion modelingSharath S. Girimaji · 1993
Kameleon ManKim Barry Brunhuber · 2003
Marine turbulenceJohn H. Simpson, Jürgen Sündermann · 2005
Applied Turbulence Modelling in Marine WatersHans Burchard · 2002- 📕Fizyczne i matematyczne modelowanie procesów mieszania kąpieli metalowej w reaktorach metalurgicznychJan Falkus · 1998
The one-dimensionality of the upper ocean mixing and the role of advection during the POLE experimentRicky Eugene Shook · 1980- 📕Resource allocation during the transition to a market economyJoshua Aizenman · 1993
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Multilevel model Multilevel models are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains…
- Fay–Herriot model The Fay–Herriot model is a statistical model which includes some distinct variation for each of several subgroups of observations. It is an area-level…
- Generalized linear mixed model In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random…
- Multilevel modeling for repeated measures One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed…
- Linear regression In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory…
- Generalized least squares In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there…
- Two-way analysis of variance In statistics, the two-way analysis of variance (ANOVA) is used to study how two categorical independent variables affect one continuous dependent variable…
- Ordinary least squares In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model by…