# emmeans v1.4.8

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## 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

## Features

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

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

## Functions in emmeans

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

## Vignettes of emmeans

 Name FAQs.Rmd basics.Rmd comparisons.Rmd confidence-intervals.Rmd interactions.Rmd messy-data.Rmd models.Rmd predictions.Rmd sophisticated.Rmd transformations.Rmd utilities.Rmd vignette-topics.Rmd xplanations.Rmd xtending.Rmd No Results!