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What is lme4 R?

Author

Charlotte Adams

Published Mar 17, 2026

What is lme4 R?

lme4-package. Linear, generalized linear, and nonlinear mixed models. Description lme4 provides functions for fitting and analyzing mixed models: linear (lmer), generalized linear (glmer) and nonlinear (nlmer.) Differences between nlme and lme4.

Moreover, what is LMER for?

LMER. Labor and Management Employee Relations.

Additionally, what library is LMER in R? The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

lme4: Linear Mixed-Effects Models using 'Eigen' and S4.

Version:1.1-27.1
Depends:R (≥ 3.2.0), Matrix (≥ 1.2-1), methods, stats

Also to know, what is the LMER function in R?

Like most model-fitting functions in R, lmer takes as its first two arguments a formula spec- ifying the model and the data with which to evaluate the formula. This second argument, data, is optional but recommended and is usually the name of an R data frame.

What is Theta LMER?

a named list of starting values for the parameters in the model. For lmer this can be a numeric vector or a list with one component named "theta" . This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included.

What package is LMER in?

lme4 uses modern, efficient linear algebra methods as implemented in the Eigen package, and uses reference classes to avoid undue copying of large objects; it is therefore likely to be faster and more memory-efficient than nlme.

Should I use ml or REML?

Recap that, ML estimates for variance has a term 1/n, but the unbiased estimate should be 1/(n−p), where n is the sample size, p is the number of mean parameters. So REML should be used when you are interested in variance estimates and n is not big enough as compared to p.Aug 1, 2017

What is the difference between LMER and Glmer?

The lmer() function is for linear mixed models and the glmer() function is for generalized mixed models. For generalized mixed models the familyName sets the link and variance function for the model.

What is random effect in LMER?

If an individual has a positive random effect, then they increase more quickly with practice than the average, while a negative random effect indicates they learn less quickly with practice than the average, or possibly get worse with practice, depending on the variance of the random effect (this is assuming the fixed

What is Reml criterion?

In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that

What is a linear mixed model analysis?

Linear mixed models are an extension of simple linear models to allow both fixed and random effects, and are particularly used when there is non independence in the data, such as arises from a hierarchical structure. For example, students could be sampled from within classrooms, or patients from within doctors.

How do you cite lme4 packages?

lme4 citation info. Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.†Journal of Statistical Software, 67(1), 1–48. doi: 10.18637/jss.

What is random effect model in statistics?

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. In econometrics, random effects models are used in panel analysis of hierarchical or panel data when one assumes no fixed effects (it allows for individual effects).

How do you report a linear mixed model?

It is not complicated at all:
  1. Don't report p-values. They are crap!
  2. Report the fixed effects estimates. These represent the best-guess average effects in the population.
  3. Report the confidence limits.
  4. Report how variable the effect is between individuals by the random effects standard deviations:

Why is Reml false?

If your random effects are nested, or you have only one random effect, and if your data are balanced (i.e., similar sample sizes in each factor group) set REML to FALSE, because you can use maximum likelihood.Apr 8, 2017

What does nAGQ 0 mean?

Without being too specific (and therefore perhaps too technical), nAGQ = 0 means that the random effects only influence the estimates of the fixed effects through their estimated conditional modes -- therefore, nAGQ = 0 does not completely account for the randomness of the random effects.Dec 16, 2013

What is Theta in random effects?

The parameter vector θ is defined as "random-effects parameters estimates; these are parameterized as the relative Cholesky factors of each random effect term".Jun 4, 2015

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What is lme4 R?