Much of what you do with the emmeans package involves these three basic steps:
EMM <- emmeans(...)(see scenarios below) to obtain estimates of means or marginal means
pairs(EMM)one or more times to obtain estimates of contrasts or pairwise comparisons among the means.
Note: A lot of users have developed the habit of
running something like
emmeans(model, pairwise ~ factor(s)), which conflates steps
1 and 2. We recommend against doing this because it often
yields output you don’t want or need – especially when there is more
than one factor. You are better off keeping steps 1 and 2 separate. What
you do in step 2 depends on how many factors you have, and how they
If one-factor model fits well and the factor is named
EMM <- emmeans(model, "treatment") # or emmeans(model, ~ treatment) EMM # display the means ### pairwise comparisons contrast(EMM, "pairwise") # or pairs(EMM)
You may specify other contrasts in the second argument of the
contrast() call, e.g.
"trt.vs.ctrl", ref = 1
(compare each mean to the first), or
"consec" (compare 2 vs
1, 3 vs 2, etc.), or
"poly", max.degree = 3 (polynomial
If the model fits well and factors are named
dose, and they don’t interact, follow the same steps as for
one factor at a time. That is, something like
(EMM1 <- emmeans(model, ~ treat)) pairs(EMM1) (EMM2 <- emmeans(model, ~ dose)) pairs(EMM2)
These analyses will yield the estimated marginal means for each factor, and comparisons/contrasts thereof.
In this case, unless the interaction effect is negligible, we usually want to do “simple comparisons” of the cell means. That is, compare or contrast the means separately, holding one factor fixed at each level.
EMM <- emmeans(model, ~ treat * dose) EMM # display the cell means ### Simple pairwise comparisons... pairs(EMM, simple = "treat") # compare treats for each dose -- "simple effects" pairs(EMM, simple = "dose") # compare doses for each treat
The default is to apply a separate Tukey adjustment to the P
values in each
by group (so if each group has just 2 means,
no adjustment at all is applied). If you want to adjust the whole family
combined, you need to undo the
by variable and specify the
desired adjustment (which can’t be Tukey because that method is
invalid when you have more than one set of pairwise comparisons.) For
test(pairs(EMM, by = "dose"), by = NULL, adjust = "mvt")
If the “diagonal” comparisons (where both factors differ)
are of interest, you would do
pairs(EMM) without a
by variable. But you get a lot more comparisons this
Sometimes you may want to examine interaction contrasts,
which are contrasts of contrasts. The thing to know here is that
contrast() or (
pairs()) creates the same kind
of object as
emmeans(), so you can run them multiple times.
CON <- pairs(EMM, by = "dose") contrast(CON, "consec", by = NULL) # by = NULL is essential here!
Or equivalently, the named argument
interaction can be
contrast(EMM, interaction = c("pairwise", "consec"))
After you have mastered the strategies for two factors, you can adapt them to three or more factors as appropriate, based on how they interact and what you need.
ref_grid()for additional arguments that may prove useful. Many of the most useful arguments are passed to
This is probably the most common issue, and it can happen when a treatment is coded as a numeric predictor rather than a factor. Instead of getting a mean for each treatment, you get a mean at the average of those numerical values.
factor(treatment)and re-fit the model.
at = list(treatment = c(3,5,7))to the
pairwise ~ ...recipe
The basic object returned by
contrast() is of class
emmGrid, and additional
contrast() calls can accept
emmGrid objects. However, some options create
emmGrid objects, and that makes things a
bit confusing. The most common case is using a call like
emmeans(model, pairwise ~ treat * dose), which computes the
means and all pairwise comparisons – a list of two
emmGrids. If you try to obtain additional contrasts, say,
of this result,
contrast() makes a guess that you want to
run it on just the first element.
This causes confusion (I know, because I get a lot of questions about
it). I recommend that you avoid using the
construct altogether: Get your means in one step, and get your contrasts
in separate step(s). The
pairwise ~ construct is generally
useful if you have only one factor; otherwise, it likely gives you
results you don’t want.
There are several of these vignettes that offser more details and more advanced topics. An index of all these vignette topics is available here.
The strings linked below are the names of the vignettes; i.e., they
can also be accessed via