# emmeans v1.4.4

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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

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

### To install without vignettes (faster):
devtools::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 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

 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 xtending.Rmd No Results!