`../vignettes/emmeans-examples.Rmd`

`emmeans-examples.Rmd`

The **emmeans** package provides a variety of *post hoc* analyses such as obtaining estimated marginal means (EMMs) and comparisons thereof, displaying these results in a graph, and a number of related tasks.

This vignette illustrates basic uses of **emmeans** with `lm_robust`

objects. For more details, refer to the **emmeans** package itself and its vignettes.

The `warpbreaks`

dataset provided in base R has the results of a two-factor experiment. We start by fitting a model

Typical use of `emmeans()`

is to obtain predictions, or marginal means thereof, via a formula of the form `~ primary.variables | by.variables`

:

These results may be plotted as side-by-side intervals or as an interaction-style plot:

This particular example has a response transformation. That transformation is detected and we may back-transform to the original scale:

`confint(emm, type = "response")`

We may do comparisons and other contrasts:

```
pairs(emm) # pairwise comparisons
contrast(emm, "trt.vs.ctrl", ref = "L", type = "response", adjust = "mvt")
```

Note that with a log transformations, it is possible to back-transform comparisons, and they become ratios. With other transformations, back-transforming is not possible.

Let’s create a variation on this example where one cell is omitted:

Note that the empty cell is detected and flagged as non-estimable.

Some additional explanation here. EMMs are based on a *reference grid*, defined as the grid created by all possible combinations of factor levels, together with the mean of each numerical predictor. The reference grid here (`rgi`

) is also an `"emmGrid"`

object just like the previous `emm`

. The grid itself is available as a data frame via the `grid`

member, and you can verify that the above results match those of the `predict`

function for the model:

`predict(warpi.rlm, newdata = rgi@grid, se.fit = TRUE)`

There is one exception for the empty cell. I will leave it as a user exercise to demonstrate that if we were to use different contrasts when fitting `warpi.rlm`

, the predictions will be the same *except* for the empty cell.

If there is a multivariate response, it is treated as another factor that is crossed with the other factors in the model. To illustrate, consider the dataset `MOats`

, provided in **emmeans**:

By default, the pseudo-factor is named `rep.meas`

, but we can change it if we like:

`emmeans(MOats.rlm, pairwise ~ nitro, mult.name = "nitro")`

This illustrates an additional feature of `emmeans`

that we can put a contrast method in the left side of a formula.

There are numerous capabilities of **emmeans** not illustrated here. See that package’s help files and vignettes. Using `vignette("basics", "emmeans")`

is a good starting point.