emmeans v1.4.4


Monthly downloads



Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.


R package emmeans: Estimated marginal means

Note: emmeans is a continuation of the package lsmeans. The latter will eventually be retired.


Estimated marginal means (EMMs, previously known as least-squares means in the context of traditional regression models) are derived by using a model to make predictions over a regular grid of predictor combinations (called a reference grid). These predictions may possibly be averaged (typically with equal weights) over one or more of the predictors. Such marginally-averaged predictions are useful for describing the results of fitting a model, particularly in presenting the effects of factors. The emmeans package can easily produce these results, as well as various graphs of them (interaction-style plots and side-by-side intervals).

  • Estimation and testing of pairwise comparisons of EMMs, and several other types of contrasts, are provided. There is also a cld method for display of grouping symbols.

  • Two-way support of the glht function in the multcomp package.

  • For models where continuous predictors interact with factors, the package's emtrends function works in terms of a reference grid of predicted slopes of trend lines for each factor combination.

  • Vignettes are provided on various aspects of EMMs and using the package. See the CRAN page

Model support

  • The package incorporates support for many types of models, including standard models fitted using lm, glm, and relatives, various mixed models, GEEs, survival models, count models, ordinal responses, zero-inflated models, and others. Provisions for some models include special modes for accessing different types of predictions; for example, with zero-inflated models, one may opt for the estimated response including zeros, just the linear predictor, or the zero model. For details, see vignette("models", package = "emmeans")

  • Various Bayesian models (carBayes, MCMCglmm, MCMCpack) are supported by way of creating a posterior sample of least-squares means or contrasts thereof, which may then be examined using tools such as in the coda package.

  • Package developers may provide emmeans support for their models by writing recover_data and emm_basis methods. See vignette("extending", package = "emmeans")

Versions and installation

  • CRAN The latest CRAN version may be found at https://CRAN.R-project.org/package=emmeans. Also at that site, formatted versions of this package's vignettes may be viewed.

  • Github To install the latest development version from Github, install the newest version (definitely 2.0 or higher) of the devtools package; then run

devtools::install_github("rvlenth/emmeans", dependencies = TRUE, build_opts = "")

### To install without vignettes (faster):

Note: If you are a Windows user, you should also first download and install the latest version of Rtools.

For the latest release notes on this development version, see the NEWS file

Functions in emmeans

Name Description
emmGrid-class The emmGrid class
contrast Contrasts and linear functions of EMMs
auto.noise Auto Pollution Filter Noise
emtrends Estimated marginal means of linear trends
emmeans Estimated marginal means (Least-squares means)
emmeans-package Estimated marginal means (aka Least-squares means)
extending-emmeans Support functions for model extensions
models Models supported in emmeans
as.mcmc.emmGrid Support for MCMC-based estimation
neuralgia Neuralgia data
regrid Reconstruct a reference grid with a new transformation or posterior sample
ref_grid Create a reference grid from a fitted model
nutrition Nutrition data
plot.emmGrid Plot an emmGrid or summary_emm object
add_grouping Add a grouping factor
pwpp Pairwise P-value plot
emmip Interaction-style plots for estimated marginal means
as.list.emmGrid Convert to and from emmGrid objects
emmobj Construct an emmGrid object from scratch
joint_tests Compute joint tests of the terms in a model
qdrg Quick and dirty reference grid
make.tran Response-transformation extensions
rbind.emmGrid Combine or subset emmGrid objects
str.emmGrid Miscellaneous methods for emmGrid objects
emm_list The emm_list class
CLD Extract and display information on all pairwise comparisons of estimated marginal means.
MOats Oats data in multivariate form
emm Support for multcomp::glht
hpd.summary Summarize an emmGrid from a Bayesian model
lsmeans Wrappers for alternative naming of EMMs
xtable.emmGrid Using xtable for EMMs
fiber Fiber data
feedlot Feedlot data
emm_options Set or change emmeans options
contrast-methods Contrast families
pigs Effects of dietary protein on free plasma leucine concentration in pigs
update.emmGrid Update an emmGrid object
summary.emmGrid Summaries, predictions, intervals, and tests for emmGrid objects
oranges Sales of oranges
eff_size Calculate effect sizes and confidence bounds thereof
No Results!

Vignettes of emmeans

No Results!

Last month downloads


Include our badge in your README