# emmeans

##### Estimated marginal means (Least-squares means)

Compute estimated marginal means (EMMs) for specified factors or factor combinations in a linear model; and optionally, comparisons or contrasts among them. EMMs are also known as least-squares means.

##### Usage

```
emmeans(object, specs, by = NULL, fac.reduce = function(coefs) apply(coefs,
2, mean), contr, options = get_emm_option("emmeans"), weights, offset,
trend, ...)
```

##### Arguments

- object
An object of class

`emmGrid`

; or a fitted model object that is supported, such as the result of a call to`lm`

or`lmer`

. Many fitted-model objects are supported; see`vignette("models", "emmeans")`

for details.- specs
A

`character`

vector specifying the names of the predictors over which EMMs are desired.`specs`

may also be a`formula`

or a`list`

(optionally named) of valid`spec`

s. Use of formulas is described in the Overview section below.- by
A character vector specifying the names of predictors to condition on.

- fac.reduce
A function that combines the rows of a matrix into a single vector. This implements the ``marginal averaging'' aspect of EMMs. The default is the mean of the rows. Typically if it is overridden, it would be some kind of weighted mean of the rows. If

`fac.reduce`

is nonlinear, bizarre results are likely, and EMMs will not be interpretable. NOTE: If the`weights`

argument is non-missing,`fac.reduce`

is ignored.- contr
A character value or

`list`

specifying contrasts to be added. See`contrast`

. NOTE:`contr`

is ignored when`specs`

is a formula.- options
If non-

`NULL`

, a named`list`

of arguments to pass to`update.emmGrid`

, just after the object is constructed.- weights
Character value, numeric vector, or numeric matrix specifying weights to use in averaging predictions. See “Weights” section below.

- offset
Numeric vector or scalar. If specified, this adds an offset to the predictions, or overrides any offset in the model or its reference grid. If a vector of length differing from the number of rows in the result, it is subsetted or cyclically recycled.

- trend
This is now deprecated. Use

`emtrends`

instead.- ...
This is used only when

`object`

is not already a`"ess"`

object, these arguments are passed to`ref_grid`

. Common examples are`at`

,`cov.reduce`

,`data`

, codetype,`transform`

,`df`

,`nesting`

, and`vcov.`

. Model-type-specific options (see`vignette("models", "emmeans")`

), commonly`mode`

, may be used here as well. In addition, if the model formula contains references to variables that are not predictors, you must provide a`params`

argument with a list of their names.

##### Details

Users should also consult the documentation for `ref_grid`

,
because many important options for EMMs are implemented there, via the
`...`

argument.

##### Value

When `specs`

is a `character`

vector or one-sided formula,
an object of class `"emmGrid"`

. A number of methods
are provided for further analysis, including
`summary.emmGrid`

, `confint.emmGrid`

,
`test.emmGrid`

, `contrast.emmGrid`

,
`pairs.emmGrid`

, and `CLD.emmGrid`

.
When `specs`

is a `list`

or a `formula`

having a left-hand
side, the return value is an `emm_list`

object, which is simply a
`list`

of `emmGrid`

objects.

##### Overview

Estimated marginal means or EMMs (sometimes called least-squares means) are
predictions from a linear model over a *reference grid*; or marginal
averages thereof. The `ref_grid`

function identifies/creates the
reference grid upon which `emmeans`

is based.

For those who prefer the terms “least-squares means” or
“predicted marginal means”, functions `lsmeans`

and
`pmmeans`

are provided as wrappers. See `wrappers`

.

If `specs`

is a `formula`

, it should be of the form `~ specs`

,
`~ specs | by`

, `contr ~ specs`

, or `contr ~ specs | by`

. The
formula is parsed and the variables therein are used as the arguments
`specs`

, `by`

, and `contr`

as indicated. The left-hand side is
optional, but if specified it should be the name of a contrast family (e.g.,
`pairwise`

). Operators like
`*`

or `:`

are needed in the formula to delineate names, but
otherwise are ignored.

In the special case where the mean (or weighted mean) of all the predictions
is desired, specify `specs`

as `~ 1`

or `"1"`

.

A number of standard contrast families are provided. They can be identified
as functions having names ending in `.emmc`

-- see the documentation
for `emmc-functions`

for details -- including how to write your
own `.emmc`

function for custom contrasts.

##### Weights

If `weights`

is a vector, its length must equal
the number of predictions to be averaged to obtain each EMM.
If a matrix, each row of the matrix is used in turn, wrapping back to the
first row as needed. When in doubt about what is being averaged (or how
many), first call `emmeans`

with `weights = "show.levels"`

.

If `weights`

is a string, it should partially match one of the following:

`"equal"`

Use an equally weighted average.

`"proportional"`

Weight in proportion to the frequencies (in the original data) of the factor combinations that are averaged over.

`"outer"`

Weight in proportion to each individual factor's marginal frequencies. Thus, the weights for a combination of factors are the outer product of the one-factor margins

`"cells"`

Weight according to the frequencies of the cells being averaged.

`"flat"`

Give equal weight to all cells with data, and ignore empty cells.

`"show.levels"`

This is a convenience feature for understanding what is being averaged over. Instead of a table of EMMs, this causes the function to return a table showing the levels that are averaged over, in the order that they appear.

Outer weights are like the 'expected' counts in a chi-square test of
independence, and will yield the same results as those obtained by
proportional averaging with one factor at a time. All except `"cells"`

uses the same set of weights for each mean. In a model where the predicted
values are the cell means, cell weights will yield the raw averages of the
data for the factors involved. Using `"flat"`

is similar to
`"cells"`

, except nonempty cells are weighted equally and empty cells
are ignored.

##### Offsets

Unlike in `ref_grid`

, an offset need not be scalar. If not enough values
are supplied, they are cyclically recycled. For a vector of offsets, it is
important to understand that the ordering of results goes with the first
name in `specs`

varying fastest. If there are any `by`

factors,
those vary slower than all the primary ones, but the first `by`

variable
varies the fastest within that hierarchy. See the examples.

##### See Also

##### Examples

```
# NOT RUN {
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
emmeans (warp.lm, ~ wool | tension)
# or equivalently emmeans(warp.lm, "wool", by = "tension")
emmeans (warp.lm, poly ~ tension | wool)
# }
# NOT RUN {
### Offsets: Consider a silly example:
emmeans(warp.lm, ~ tension | wool, offset = c(17, 23, 47)) @ grid
# note that offsets are recycled so that each level of tension receives
# the same offset for each wool.
# But using the same offsets with ~ wool | tension will probably not
# be what you want because the ordering of combinations is different.
# }
```

*Documentation reproduced from package emmeans, version 1.2.4, License: GPL-2 | GPL-3*