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
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")
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
remotes::install_github("rvlenth/emmeans", dependencies = TRUE, build_opts = "")
### To install without vignettes (faster):
remotes::install_github("rvlenth/emmeans")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