This vignette describes various ways of summarizing
`emmGrid`

objects.

`summary()`

, `confint()`

, and
`test()`

The most important method for `emmGrid`

objects is
`summary()`

. For one thing, it is called by default when you
display an `emmeans()`

result. The `summary()`

function has a lot of options, and the detailed documentation via
`help("summary.emmGrid")`

is worth a look.

For ongoing illustrations, let’s re-create some of the objects in the
“basics” vignette for the `pigs`

example:

```
mod4 <- lm(inverse(conc) ~ source + factor(percent), data = pigs)
RG <- ref_grid(mod4)
EMM.source <- emmeans(RG, "source")
```

Just `summary(<object>)`

by itself will produce a
summary that varies somewhat according to context. It does this by
setting different defaults for the `infer`

argument, which
consists of two logical values, specifying confidence intervals and
tests, respectively. [The exception is models fitted using MCMC methods,
where `summary()`

is diverted to the
`hpd.summary()`

function, a preferable summary for many
Bayesians.]

The summary of a newly made reference grid will show just estimates
and standard errors, but not confidence intervals or tests (that is,
`infer = c(FALSE, FALSE)`

). The summary of an
`emmeans()`

result, as we see above, will have intervals, but
no tests (i.e., `infer = c(TRUE, FALSE)`

); and the result of
a `contrast()`

call (see comparisons and contrasts) will show test
statistics and *P* values, but not intervals (i.e.,
`infer = c(FALSE, TRUE)`

). There are courtesy methods
`confint()`

and `test()`

that just call
`summary()`

with the appropriate `infer`

setting;
for example,

`test(EMM.source)`

```
## source emmean SE df t.ratio p.value
## fish 0.0337 0.000926 23 36.380 <.0001
## soy 0.0257 0.000945 23 27.141 <.0001
## skim 0.0229 0.000994 23 22.989 <.0001
##
## Results are averaged over the levels of: percent
## Results are given on the inverse (not the response) scale.
```

It is not particularly useful, though, to test these EMMs against the
default of zero – which is why tests are not usually shown. It makes a
lot more sense to test them against some target concentration, say 40.
And suppose we want to do a one-sided test to see if the concentration
is greater than 40. Remembering that the response is inverse-transformed
in this model, and that the inverse transformation reverses the
direction of comparisons, so that a *right*-tailed test on the
`conc`

scale becomes a *left*-tailed test on the
`inverse(conc)`

scale,

`test(EMM.source, null = inverse(40), side = "<")`

```
## source emmean SE df null t.ratio p.value
## fish 0.0337 0.000926 23 0.025 9.383 1.0000
## soy 0.0257 0.000945 23 0.025 0.697 0.7535
## skim 0.0229 0.000994 23 0.025 -2.156 0.0209
##
## Results are averaged over the levels of: percent
## Results are given on the inverse (not the response) scale.
## P values are left-tailed
```

It is also possible to add calculated columns to the summary, via the
`calc`

argument. The calculations can include any columns up
through `df`

in the summary, as well as any variable in the
object’s `grid`

slot. Among the latter are usually weights in
a column named `.wgt.`

, and we can use that to include sample
size in the summary:

`confint(EMM.source, calc = c(n = ~.wgt.))`

```
## source emmean SE df n lower.CL upper.CL
## fish 0.0337 0.000926 23 10 0.0318 0.0356
## soy 0.0257 0.000945 23 10 0.0237 0.0276
## skim 0.0229 0.000994 23 9 0.0208 0.0249
##
## Results are averaged over the levels of: percent
## Results are given on the inverse (not the response) scale.
## Confidence level used: 0.95
```

Transformations and link functions are supported in several ways in
**emmeans**, making this a complex topic worthy of its own vignette. Here, we show just the
most basic approach. Namely, specifying the argument
`type = "response"`

will cause the displayed results to be
back-transformed to the response scale, when a transformation or link
function is incorporated in the model. For example, let’s try the
preceding `test()`

call again:

`test(EMM.source, null = inverse(40), side = "<", type = "response")`

```
## source response SE df null t.ratio p.value
## fish 29.7 0.816 23 40 9.383 1.0000
## soy 39.0 1.436 23 40 0.697 0.7535
## skim 43.8 1.903 23 40 -2.156 0.0209
##
## Results are averaged over the levels of: percent
## P values are left-tailed
## Tests are performed on the inverse scale
```

Note what changes and what doesn’t change. In the `test()`

call, we *still* use the 1/40 as the null value;
`null`

must always be specified on the linear-prediction
scale, in this case the inverse. In the output, the displayed estimates,
as well as the `null`

value, are shown back-transformed. As
well, the standard errors are altered (using the delta method). However,
the *t* ratios and *P* values are identical to the
preceding results. That is, the tests themselves are still conducted on
the linear-predictor scale (as is noted in the output).

Similar statements apply to confidence intervals on the response scale:

`confint(EMM.source, side = "<", level = .90, type = "response")`

```
## source response SE df lower.CL upper.CL
## fish 29.7 0.816 23 28.6 Inf
## soy 39.0 1.436 23 37.2 Inf
## skim 43.8 1.903 23 41.4 Inf
##
## Results are averaged over the levels of: percent
## Confidence level used: 0.9
## Intervals are back-transformed from the inverse scale
```

With `side = "<"`

, an *upper* confidence limit
is computed on the inverse scale, then that limit is back-transformed to
the response scale; and since `inverse`

reverses everything,
those upper confidence limits become lower ones on the response scale.
(We have also illustrated how to change the confidence level.)

Both tests and confidence intervals may be adjusted for simultaneous
inference. Such adjustments ensure that the confidence coefficient for a
whole set of intervals is at least the specified level, or to control
for multiplicity in a whole family of tests. This is done via the
`adjust`

argument. For `ref_grid()`

and
`emmeans()`

results, the default is
`adjust = "none"`

. For most `contrast()`

results,
`adjust`

is often something else, depending on what type of
contrasts are created. For example, pairwise comparisons default to
`adjust = "tukey"`

, i.e., the Tukey HSD method. The
`summary()`

function sometimes *changes*
`adjust`

if it is inappropriate. For example, with

`confint(EMM.source, adjust = "tukey")`

```
## Note: adjust = "tukey" was changed to "sidak"
## because "tukey" is only appropriate for one set of pairwise comparisons
```

```
## source emmean SE df lower.CL upper.CL
## fish 0.0337 0.000926 23 0.0313 0.0361
## soy 0.0257 0.000945 23 0.0232 0.0281
## skim 0.0229 0.000994 23 0.0203 0.0254
##
## Results are averaged over the levels of: percent
## Results are given on the inverse (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
```

the adjustment is changed to the Sidak method because the Tukey adjustment is inappropriate unless you are doing pairwise comparisons.

An adjustment method that is usually appropriate is Bonferroni;
however, it can be quite conservative. Using `adjust = "mvt"`

is the closest to being the “exact” all-around method “single-step”
method, as it uses the multivariate *t* distribution (and the
**mvtnorm** package) with the same covariance structure as
the estimates to determine the adjustment. However, this comes at high
computational expense as the computations are done using simulation
techniques. For a large set of tests (and especially confidence
intervals), the computational lag becomes noticeable if not
intolerable.

For tests, `adjust`

increases the *P* values over
those otherwise obtained with `adjust = "none"`

. Compare the
following adjusted tests with the unadjusted ones previously
computed.

`test(EMM.source, null = inverse(40), side = "<", adjust = "bonferroni")`

```
## source emmean SE df null t.ratio p.value
## fish 0.0337 0.000926 23 0.025 9.383 1.0000
## soy 0.0257 0.000945 23 0.025 0.697 1.0000
## skim 0.0229 0.000994 23 0.025 -2.156 0.0627
##
## Results are averaged over the levels of: percent
## Results are given on the inverse (not the response) scale.
## P value adjustment: bonferroni method for 3 tests
## P values are left-tailed
```

Sometimes you want to break a summary down into smaller pieces; for
this purpose, the `by`

argument in `summary()`

is
useful. For example,

`confint(RG, by = "source")`

```
## source = fish:
## percent prediction SE df lower.CL upper.CL
## 9 0.0385 0.00135 23 0.0357 0.0413
## 12 0.0333 0.00125 23 0.0307 0.0359
## 15 0.0326 0.00138 23 0.0297 0.0354
## 18 0.0304 0.00138 23 0.0275 0.0332
##
## source = soy:
## percent prediction SE df lower.CL upper.CL
## 9 0.0305 0.00126 23 0.0279 0.0331
## 12 0.0253 0.00124 23 0.0227 0.0278
## 15 0.0245 0.00128 23 0.0219 0.0272
## 18 0.0223 0.00162 23 0.0190 0.0257
##
## source = skim:
## percent prediction SE df lower.CL upper.CL
## 9 0.0277 0.00127 23 0.0251 0.0303
## 12 0.0225 0.00125 23 0.0199 0.0250
## 15 0.0217 0.00139 23 0.0189 0.0246
## 18 0.0195 0.00163 23 0.0162 0.0229
##
## Results are given on the inverse (not the response) scale.
## Confidence level used: 0.95
```

If there is also an `adjust`

in force when `by`

variables are used, by default, the adjustment is made
*separately* on each `by`

group; e.g., in the above,
we would be adjusting for sets of 4 intervals, not all 12 together (but
see “cross-adjustments” below.)

There can be a `by`

specification in
`emmeans()`

(or equivalently, a `|`

in the
formula); and if so, it is passed on to `summary()`

and used
unless overridden by another `by`

. Here are examples, not
run:

```
emmeans(mod4, ~ percent | source) ### same results as above
summary(.Last.value, by = "percent") ### grouped the other way
```

Specifying `by = NULL`

will remove all grouping.

`by`

groupsAs was mentioned, each `by`

group is regarded as a
separate family with regards to the `adjust`

procedure. For
example, consider a model with interaction for the
`warpbreaks`

data, and construct pairwise comparisons of
`tension`

by `wool`

:

```
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
warp.pw <- pairs(emmeans(warp.lm, ~ tension | wool))
warp.pw
```

```
## wool = A:
## contrast estimate SE df t.ratio p.value
## L - M 20.556 5.16 48 3.986 0.0007
## L - H 20.000 5.16 48 3.878 0.0009
## M - H -0.556 5.16 48 -0.108 0.9936
##
## wool = B:
## contrast estimate SE df t.ratio p.value
## L - M -0.556 5.16 48 -0.108 0.9936
## L - H 9.444 5.16 48 1.831 0.1704
## M - H 10.000 5.16 48 1.939 0.1389
##
## P value adjustment: tukey method for comparing a family of 3 estimates
```

We have two sets of 3 comparisons, and the (default) Tukey adjustment
is made *separately* in each group.

However, sometimes we want the multiplicity adjustment to be broader.
This broadening can be done in two ways. One is to remove the
`by`

variable, which then treats all results as one family.
In our example:

`test(warp.pw, by = NULL, adjust = "bonferroni")`

```
## contrast wool estimate SE df t.ratio p.value
## L - M A 20.556 5.16 48 3.986 0.0014
## L - H A 20.000 5.16 48 3.878 0.0019
## M - H A -0.556 5.16 48 -0.108 1.0000
## L - M B -0.556 5.16 48 -0.108 1.0000
## L - H B 9.444 5.16 48 1.831 0.4396
## M - H B 10.000 5.16 48 1.939 0.3504
##
## P value adjustment: bonferroni method for 6 tests
```

This accomplishes the goal of putting all the comparisons in one
family of 6 comparisons. Note that the Tukey adjustment may not be used
here because we no longer have *one* set of pairwise
comparisons.

An alternative is to specify `cross.adjust`

, which
specifies an additional adjustment method to apply to corresponding sets
of within-group adjusted *P* values:

`test(warp.pw, adjust = "tukey", cross.adjust = "bonferroni")`

```
## wool = A:
## contrast estimate SE df t.ratio p.value
## L - M 20.556 5.16 48 3.986 0.0013
## L - H 20.000 5.16 48 3.878 0.0018
## M - H -0.556 5.16 48 -0.108 1.0000
##
## wool = B:
## contrast estimate SE df t.ratio p.value
## L - M -0.556 5.16 48 -0.108 1.0000
## L - H 9.444 5.16 48 1.831 0.3407
## M - H 10.000 5.16 48 1.939 0.2777
##
## P value adjustment: tukey method for comparing a family of 3 estimates
## Cross-group P-value adjustment: bonferroni
```

These adjustments are less conservative than the previous result, but
it is still a conservative adjustment to the set of 6 tests. Had we also
specified `adjust = "bonferroni"`

, we would have obtained the
same adjusted *P* values as we obtained with
`by = NULL`

.

There is also a `simple`

argument for
`contrast()`

that is in essence the inverse of
`by`

; the contrasts are run using everything *except*
the specified variables as `by`

variables. To illustrate,
let’s consider the model for `pigs`

that includes the
interaction (so that the levels of one factor compare differently at
levels of the other factor).

```
mod5 <- lm(inverse(conc) ~ source * factor(percent), data = pigs)
RG5 <- ref_grid(mod5)
contrast(RG5, "consec", simple = "percent")
```

```
## source = fish:
## contrast estimate SE df t.ratio p.value
## percent12 - percent9 -6.64e-03 0.00285 17 -2.328 0.0834
## percent15 - percent12 -6.68e-05 0.00285 17 -0.023 1.0000
## percent18 - percent15 -1.40e-03 0.00285 17 -0.489 0.9283
##
## source = soy:
## contrast estimate SE df t.ratio p.value
## percent12 - percent9 -4.01e-03 0.00255 17 -1.572 0.3171
## percent15 - percent12 2.61e-04 0.00255 17 0.102 0.9993
## percent18 - percent15 -2.18e-03 0.00361 17 -0.605 0.8873
##
## source = skim:
## contrast estimate SE df t.ratio p.value
## percent12 - percent9 -5.26e-03 0.00255 17 -2.061 0.1393
## percent15 - percent12 -2.86e-03 0.00285 17 -1.001 0.6526
## percent18 - percent15 -3.76e-03 0.00383 17 -0.982 0.6651
##
## Note: contrasts are still on the inverse scale
## P value adjustment: mvt method for 3 tests
```

In fact, we may do *all* one-factor comparisons by specifying
`simple = "each"`

. This typically produces a lot of output,
so use it with care.

From the above, we already know how to test individual results. For pairwise comparisons (details in the “comparisons” vignette), we might do

```
PRS.source <- pairs(EMM.source)
PRS.source
```

```
## contrast estimate SE df t.ratio p.value
## fish - soy 0.00803 0.00134 23 6.009 <.0001
## fish - skim 0.01083 0.00137 23 7.922 <.0001
## soy - skim 0.00280 0.00134 23 2.092 0.1136
##
## Results are averaged over the levels of: percent
## Note: contrasts are still on the inverse scale
## P value adjustment: tukey method for comparing a family of 3 estimates
```

But suppose we want an *omnibus* test that all these
comparisons are zero. Easy enough, using the `joint`

argument
in `test`

(note: the `joint`

argument is
*not* available in `summary()`

; only in
`test()`

):

`test(PRS.source, joint = TRUE)`

```
## df1 df2 F.ratio p.value note
## 2 23 34.009 <.0001 d
##
## d: df1 reduced due to linear dependence
```

Notice that there are three comparisons, but only 2 d.f. for the test, as cautioned in the message.

The test produced with `joint = TRUE`

is a “type III” test
(assuming the default equal weights are used to obtain the EMMs). See
more on these types of tests for higher-order effects in the “interactions” vignette section on
contrasts.

For convenience, there is also a `joint_tests()`

function
that performs joint tests of contrasts among each term in a model or
`emmGrid`

object.

`joint_tests(RG5)`

```
## model term df1 df2 F.ratio p.value
## source 2 17 30.309 <.0001
## percent 3 17 8.441 0.0012
## source:percent 6 17 0.481 0.8135
```

The tests of main effects are of families of contrasts; those for interaction effects are for interaction contrasts. These results are essentially the same as a “Type-III ANOVA”, but may differ in situations where there are empty cells or other non-estimability issues, or if generalizations are present such as unequal weighting. (Another distinction is that sums of squares and mean squares are not shown; that is because these really are tests of contrasts among predictions, and they may or may not correspond to model sums of squares.)

One may use `by`

variables with `joint_tests`

.
For example:

`joint_tests(RG5, by = "source")`

```
## source = fish:
## model term df1 df2 F.ratio p.value
## percent 3 17 2.967 0.0614
##
## source = soy:
## model term df1 df2 F.ratio p.value
## percent 3 17 1.376 0.2840
##
## source = skim:
## model term df1 df2 F.ratio p.value
## percent 3 17 4.835 0.0130
```

In some models, it is possible to specify
`submodel = "type2"`

, thereby obtaining something akin to a
Type II analysis of variance. See the messy-data vignette for an
example.

The `delta`

argument in `summary()`

or
`test()`

allows the user to specify a threshold value to use
in a test of equivalence, noninferiority, or nonsuperiority. An
equivalence test is kind of a backwards significance test, where small
*P* values are associated with small differences relative to a
specified threshold value `delta`

. The help page for
`summary.emmGrid`

gives the details of these tests. Suppose
in the present example, we consider two sources to be equivalent if they
are within 0.005 of each other. We can test this as follows:

`test(PRS.source, delta = 0.005, adjust = "none")`

```
## contrast estimate SE df t.ratio p.value
## fish - soy 0.00803 0.00134 23 2.268 0.9835
## fish - skim 0.01083 0.00137 23 4.266 0.9999
## soy - skim 0.00280 0.00134 23 -1.641 0.0572
##
## Results are averaged over the levels of: percent
## Note: contrasts are still on the inverse scale
## Statistics are tests of equivalence with a threshold of 0.005
## P values are left-tailed
```

Using the 0.005 threshold, the *P* value is quite small for
comparing soy and skim, providing some statistical evidence that their
difference is enough smaller than the threshold to consider them
equivalent.

Graphical displays of `emmGrid`

objects are described in
the “basics” vignette