Here we document what model objects may be used with
emmeans, and some special features of some of them that
may be accessed by passing additional arguments through
ref_grid
or emmeans()
.
Certain objects are affected by optional arguments to functions that
construct emmGrid
objects, including
ref_grid()
, emmeans()
,
emtrends()
, and emmip()
. When
“arguments” are mentioned in the subsequent quick reference and
object-by-object documentation, we are talking about arguments in these
constructors.
Some options cause transformations and links to be resolved at the
time the reference grid is created, thus performing a kind of implied
re-gridding at the very start. Such options are marked in the table with
a “twiddle” (e.g., "prob"~
). For those options, no links or
transformations are passed along.
If a model type is not included here, users may be able to obtain
usable results via the qdrg()
function; see its help page.
Package developers may support their models by writing appropriate
recover_data
and emm_basis
methods. See the
package documentation for extending-emmeans
and
vignette("xtending")
for details.
Here is an alphabetical list of model classes that are supported, and the arguments that apply. Detailed documentation follows, with objects grouped by the code in the “Group” column. Scroll down or follow the links to those groups for more information.
Object.class | Package | Group | Arguments / notes (Suffix of ~ indicates
re-gridding) |
---|---|---|---|
aov | stats | A | |
aovList | stats | V | Best with balanced designs, orthogonal coding |
averaging | MuMIn | I | formula , subset (see
details) |
betareg | betareg | B | mode = c("link", "precision", "phi.link", |
"variance"~, "quantile"~) |
|||
brmsfit | brms | P | Supported in brms package |
carbayes | CARBayes | S | data is required |
clm | ordinal | O | mode = c("latent"~, "linear.predictor", "cum.prob"~, |
"exc.prob"~, "prob"~, "mean.class"~, "scale") |
|||
clmm | ordinal | O | Like clm but no "scale"
mode |
coxme | coxme | G | |
coxph | survival | G | |
gam | mgcv | G | freq = FALSE ,
unconditional = FALSE , |
what = c("location", "scale", "shape", "rate", "prob.gt.0") |
|||
gamm | mgcv | G | call = object$gam$call |
Gam | gam | G | nboot = 800 |
gamlss | gamlss | H | what = c("mu", "sigma", "nu", "tau") |
gee | gee | E | vcov.method = c("naive", "robust") |
geeglm | geepack | E | vcov.method = c("vbeta", "vbeta.naiv", "vbeta.j1s", |
"vbeta.fij", "robust", "naive") or a
matrix |
|||
geese | geepack | E | Like geeglm |
glm | stats | G | |
glm.nb | MASS | G | |
glmerMod | lme4 | G | |
glmgee | glmtoolbox | E | vcov.method = c("robust", "df-adjusted", "model", |
"bias-corrected", "jackknife") |
|||
glmmadmb | glmmADMB | No longer supported | |
glmmPQL | MASS | G | inherits lm support |
glmmTMB | glmmTMB | P | Supported in glmmTMB package |
gls | nlme | K | mode = c("auto", "df.error", "satterthwaite", "asymptotic") |
gnls | nlme | A | Supports params part. Requires
param = "<name>" |
hurdle | pscl | C | mode = c("response", "count", "zero", "prob0"), |
lin.pred = c(FALSE~, TRUE) |
|||
lm | stats | A | Several other classes inherit from this and may be supported |
lme | nlme | K | sigmaAdjust = c(TRUE, FALSE), |
mode = c("auto", containment", "satterthwaite", "asymptotic"), |
|||
extra.iter = 0 |
|||
lmerMod | lme4 | L | lmer.df = c("kenward-roger", "satterthwaite", "asymptotic") , |
pbkrtest.limit = 3000 ,
disable.pbkrtest = FALSE . |
|||
emm_options(lmer.df =, pbkrtest.limit =, disable.pbkrtest =) |
|||
logistf | glmmTMB | P | Supported in logistf package |
lqm,lqmm | lqmm | Q | tau = "0.5" (must match an entry in
object$tau ) |
Optional: method , R ,
seed , startQR (must be fully spelled-out) |
|||
manova | stats | M | mult.name , mult.levs |
maov | stats | M | mult.name , mult.levs |
mblogit | mclogit | N | mode = c("prob"~, "latent") |
Always include response in specs for
emmeans() |
|||
mcmc | mcmc | S | May require formula ,
data |
MCMCglmm | MCMCglmm | S | (see also M) mult.name ,
mult.levs , trait , |
mode = c("default", "multinomial") ;
data is required |
|||
mira | mice | I | Optional arguments per class of $analyses
elements |
mixed | afex | P | Supported in afex package |
mlm | stats | M | mult.name , mult.levs |
mmer | sommer | G | |
mmrm | mmrm | P | Supported in the mmrm package |
multinom | nnet | N | mode = c("prob"~, "latent") |
Always include response in specs for
emmeans() |
|||
nauf | nauf.xxx | P | Supported in nauf package |
nlme | nlme | A | Supports fixed part. Requires
param = "<name>" |
polr | MASS | O | mode = c("latent"~, "linear.predictor", "cum.prob"~, |
"exc.prob"~, "prob"~, "mean.class"~) |
|||
rlm | MASS | A | inherits lm support |
rms | rms | O | mode = ("middle"~, "latent"~, "linear.predictor", |
"cum.prob"~, "exc.prob"~, "prob"~, "mean.class"~) |
|||
rq,rqs | quantreg | Q | tau = object$tau |
Creates a pseudo-factor tau with levels
tau |
|||
Optional: se , R ,
bsmethod , etc. |
|||
rlmerMod | robustlmm | P | Supported in robustlmm package |
rsm | rsm | P | Supported in rsm package |
stanreg | rstanarm | S | Args for stanreg_ xxx similar to
those for xxx |
survreg | survival | A | |
svyglm | survey | A | |
svyolr | survey | O | Piggybacks on polr support |
zeroinfl | pscl | C | mode = c("response", "count", "zero", "prob0") , |
lin.pred = c(FALSE~, TRUE) |
Models in this group, such as lm
, do not have unusual
features that need special support; hence no extra arguments are needed.
Some may require data
in the call.
The additional mode
argument for betareg
objects has possible values of "response"
,
"link"
, "precision"
, "phi.link"
,
"variance"
, and "quantile"
, which have the
same meaning as the type
argument in
predict.betareg
– with the addition that
"phi.link"
is like "link"
, but for the
precision portion of the model. When mode = "quantile"
is
specified, the additional argument quantile
(a numeric
scalar or vector) specifies which quantile(s) to compute; the default is
0.5 (the median). Also in "quantile"
mode, an additional
variable quantile
is added to the reference grid, and its
levels are the values supplied. Modes "variance"
and
"quantile"
cause an implied re-grid.
Two optional arguments – mode
and lin.pred
– are provided. The mode
argument has possible values
"response"
(the default), "count"
,
"zero"
, or "prob0"
. lin.pred
is
logical and defaults to FALSE
.
With lin.pred = FALSE
, the results are comparable to
those returned by predict(..., type = "response")
,
predict(..., type = "count")
,
predict(..., type = "zero")
, or
predict(..., type = "prob")[, 1]
. See the documentation for
predict.hurdle
and predict.zeroinfl
. Note that
specifying lin.pred = FALSE
causes re-gridding to take
place.
The option lin.pred = TRUE
only applies to
mode = "count"
and mode = "zero"
. The results
returned are on the linear-predictor scale, with the same transformation
as the link function in that part of the model. The predictions for a
reference grid with mode = "count"
,
lin.pred = TRUE
, and type = "response"
will be
the same as those obtained with lin.pred = FALSE
and
mode = "count"
; however, any EMMs derived from these grids
will be different, because the averaging is done on the log-count scale
and the actual count scale, respectively – thereby producing geometric
means versus arithmetic means of the predictions.
If the vcov.
argument is used (see details in the
documentation for ref_grid
), it must yield a matrix of the
same size as would be obtained using vcov.hurdle
or
vcov.zeroinfl
with its model
argument set to
("full", "count", "zero")
in respective correspondence with
mode
of ("mean", "count", "zero")
. If
vcov.
is a function, it must support the model
argument.
These models all have more than one covariance estimate available,
and it may be selected by supplying a string as the
vcov.method
argument. It is partially matched with the
available choices shown in the quick reference. In geese
and geeglm
, the aliases "robust"
(for
"vbeta"
) and "naive"
(for
"vbeta.naiv"
are also accepted.
If a matrix or function is supplied as vcov.method
, it
is interpreted as a vcov.
specification as described for
...
in the documentation for ref_grid
.
Most models in this group receive only standard support as in Group A, but typically the tests and confidence intervals
are asymptotic. Thus the df
column for tabular results will
be Inf
.
Some objects in this group may require that the original or reference
dataset be provided when calling ref_grid()
or
emmeans()
.
For coxph
objects, the estimates we obtain are
comparable to running predict.coxph()
with
reference = "zero"
; that is, no covariate centering is
done. The user may use at
to specify adjusted covariate
values. For example, the default reference grid sets each covariate to
its mean, so estimates comparable to
predict.coxph(..., reference = "sample")
would be obtained
by specifying at = list(x = 0)
.
In the case of mgcv::gam
objects, there are optional
freq
and unconditional
arguments as is
detailed in the documentation for mgcv::vcov.gam()
. Both
default to FALSE
. The value of unconditional
matters only if freq = FALSE
and object$Vc
is
non-null.
For mgcv::gamm
objects, emmeans()
results
are based on the object$gam
part. Unfortunately, that is
missing its call
component, so the user must supply it in
the call
argument (e.g.,
call = quote(gamm(y ~ s(x), data = dat))
) or give the
dataset in the data
argument. Alternatively (and
recommended), you may first set object$gam$call
to the
quoted call ahead of time. The what
arguments are used to
select which model formula to use: "location", "scale"
apply to gaulss
and gevlss
families,
"shape"
applies only to gevlss
, and
"rate", "prob.gt.0"
apply to ziplss
.
With gam::Gam
objects, standard errors are estimated
using a bootstrap method when there are any smoothers involved.
Accordingly, there is an optional nboot
argument that sets
the number of bootstrap replications used to estimate the variances and
covariances of the smoothing portions of the model. Generally, it is
better to use models fitted via mgcv::gam()
rather than
gam::gam()
.
gamlss
modelsThe what
argument has possible values of
"mu"
(default), "sigma"
, "nu"
, or
"tau"
depending on which part of the model you want results
for. Currently, there is no support when the selected part of the model
contains a smoothing method like pb()
.
These objects are the results of fitting several models with
different predictor subsets or imputed values. The bhat
and
V
slots are obtained via averaging and, in the case of
multiple imputation, adding a multiple of the between-imputation
covariance per Rubin’s rules, along with an associated
degrees-of-freedom adjustment (Barnard & Rubin 1999). In the case of
mira
models with model classes not supported by
emmeans, GitHub issue
#446 includes a function pool_estimates_for_qdrg()
that
may be useful for obtaining results via qdrg()
. Another
useful link may be a page that shows how
to pool several emmeans
results; that is, instead of
pooling the models and then running emmeans()
, we do just
the reverse.
Support for MuMIn::averaging
objects may be somewhat
dodgy, as it is not clear that all supported model classes will work.
The object must have a "modelList"
attribute
(obtained by constructing the object explicitly from a model list or by
including fit = TRUE
in the call). And each model should be
fitted with data
as a named argument in
the call; or else provide a data
argument in the call to
emmeans()
or ref_grid()
. Only ``full’’
averaging is supported; conditional averaging can result in
non-positive-definite covariance matrices, and so cannot be considered.
No estimability checking is done at present (not clear what we even mean
by it).
Also, be aware that support for averaging
objects does
not pay attention to the class of the models being averaged
(nor any emmeans
options associated with that class, such
as alternative modes or d.f. methods), and that you can only obtain
direct results of linear predictions or back-transformations thereof
(type = "response"
). It does not take apart
multivariate models, nor multiple-intercept models (e.g. ordinal ones).
(But keep reading…)
Finally, note that special care is needed with models having multiple
components (e.g. hurdle models), where there is essentially more than
one set of coefficients. We can handle only one at a time. A
subset
argument must be provided to specify which
coefficients to use; that can be a named vector of integers
(where the names are the names of the actual model terms), or the
special character values "prefix:pfx"
, "pfx"
,
or wrap:wrp
which picks out all coefficients whose names
are prefixed by pfx
(e.g., pfxtrtB
) or wrapped
by wrp
(e.g., wrp(trtB)
). A
formula
option is also available for specifying an
appropriate formula other than object$formula
when that is
not suitable.
In many cases (especially with multivariate or ordinal models), you
are better off making a copy of one of the averaged models that has all
required terms, and hacking that object by replacing the coefficients by
coef(object, full = TRUE)
and the covariance matrix by
vcov(object, full = TRUE)
. You need to be careful to match
the names of the coefficients correctly, and to use the same indexes to
permute the rows and columns of the covariance matrix. You then use the
emmeans
support, including any options available, for the
class of the model object, rather than for class averaging
.
An example is given towards the end of Issue 442.
gls
and lme
modelsThe sigmaAdjust
argument is a logical value that
defaults to TRUE
. It is comparable to the
adjustSigma
option in nlme::summary.lme
(the
name-mangling is to avoid conflicts with the often-used
adjust
argument), and determines whether or not a
degrees-of-freedom adjustment is performed with models fitted using the
ML method.
The optional mode
argument affects the degrees of
freedom. The mode = "satterthwaite"
option determines
degrees of freedom via the Satterthwaite method: If s^2
is
the estimate of some variance, then its Satterthwaite d.f. is
2*s^4 / Var(s^2)
. In case our numerical methods for this
fail, we also offer mode = "appx-satterthwaite"
as a
backup, by which quantities related to Var(s^2)
are
obtained by randomly perturbing the response values. Currently, only
"appx-satterthwaite"
is available for lme
objects, and it is used if "satterthwaite"
is requested.
Because appx-satterthwaite
is simulation-based, results may
vary if the same analysis is repeated. An extra.iter
argument may be added to request additional simulation runs (at
[possibly considerable] cost of repeating the model-fitting that many
more times). (Note: Previously, "appx-satterthwaite"
was
termed "boot-satterthwaite"
; this is still supported for
backward compatibility. The “boot” was abandoned because it is really an
approximation method, not a bootstrap method in the sense as many
statistical methods.)
An alternative method is "df.error"
(for
gls
) and "containment"
(for lme
).
df.error
is just the error degrees of freedom for the
model, minus the number of extra random effects estimated; it generally
over-estimates the degrees of freedom. The asymptotic
mode
simply sets the degrees of freedom to infinity.
"containment"
mode (for lme
models) determines
the degrees of freedom for the coarsest grouping involved in the
contrast or linear function involved, so it tends to under-estimate the
degrees of freedom. The default is mode = "auto"
, which
uses Satterthwaite if there are estimated random effects and the
non-Satterthwaite option otherwise.
User reports indicate that models with special terms like
poly()
are not adequately supported by gls
in
that the needed basis is not recoverable from its terms
component. This is not a problem with lme
.
The extra.iter
argument is ignored unless the d.f.
method is (or defaults to) appx-satterthwaite
.
lmerMod
modelsThere is an optional lmer.df
argument that defaults to
get_EMM_option("lmer.df")
(which in turn defaults to
"kenward-roger"
). The possible values are
"kenward-roger"
, "satterthwaite"
, and
"asymptotic"
(these are partially matched and
case-insensitive). With "kenward-roger"
, d.f. are obtained
using code from the pbkrtest package, if installed.
With "satterthwaite"
, d.f. are obtained using code from the
lmerTest package, if installed. With
"asymptotic"
, or if the needed package is not installed,
d.f. are set to Inf
. (For backward compatibility, the user
may specify mode
in lieu of lmer.df
.)
A by-product of the Kenward-Roger method is that the covariance
matrix is adjusted using pbkrtest::vcovAdj()
. This can
require considerable computation; so to avoid that overhead, the user
should opt for the Satterthwaite or asymptotic method; or, for backward
compatibility, may disable the use of pbkrtest via
emm_options(disable.pbkrtest = TRUE)
(this does not disable
the pbkrtest package entirely, just its use in
emmeans). The computation time required depends roughly
on the number of observations, N, in the design matrix (because
a major part of the computation involves inverting an N x
N matrix). Thus, pbkrtest is automatically
disabled if N exceeds the value of
get_emm_option("pbkrtest.limit")
, for which the factory
default is 3000. (The user may also specify pbkrtest.limit
or disable.pbkrtest
as an argument in the call to
emmeans()
or ref_grid()
)
Similarly to the above, the disable.lmerTest
and
lmerTest.limit
options or arguments affect whether
Satterthwaite methods can be implemented.
The df
argument may be used to specify some other
degrees of freedom. Note that if df
and
method = "kenward-roger"
are both specified, the covariance
matrix is adjusted but the K-R degrees of freedom are not used.
Finally, note that a user-specified covariance matrix (via the
vcov.
argument) will also disable the Kenward-Roger method;
in that case, the Satterthwaite method is used in place of
Kenward-Roger.
When there is a multivariate response, the different responses are
treated as if they were levels of a factor – named rep.meas
by default. The mult.name
argument may be used to change
this name. The mult.levs
argument may specify a named list
of one or more sets of levels. If this has more than one element, then
the multivariate levels are expressed as combinations of the named
factor levels via the function base::expand.grid
.
The reference grid includes a pseudo-factor with the same name and
levels as the multinomial response. (If the response is an expression,
the name of that pseudo-factor will be the first name in the expression;
e.g., if the model formula is cbind(col1, col2) ~ trt
, the
grid factors will be cbind
and trt
and the
levels of cbind
will be 1
and 2
.)
(You can change the assigned name for the multinomial response via the
mult.resp
argument.)
There is an optional mode
argument which should match
"prob"
or "latent"
. With
mode = "prob"
, the reference-grid predictions consist of
the estimated multinomial probabilities – and this implies a re-gridding
so no link functions are passed on. The "latent"
mode
returns the linear predictor, recentered so that it averages to zero
over the levels of the response variable (similar to sum-to-zero
contrasts). Thus each latent variable can be regarded as the log
probability at that level minus the average log probability over all
levels.
There are two optional arguments: mode
and
rescale
(which defaults to c(0, 1)
).
Please note that, because the probabilities sum to 1 (and the latent
values sum to 0) over the multivariate-response levels, all sensible
results from emmeans()
must involve that response as one of
the factors. For example, if resp
is a response with
k levels, emmeans(model, ~ resp | trt)
will yield
the estimated multinomial distribution for each trt
; but
emmeans(model, ~ trt)
will just yield the average
probability of 1/k for each trt
.
The reference grid for ordinal models will include all variables that
appear in the main model as well as those in the scale
or
nominal
models (if provided). There are two optional
arguments: mode
(a character string) and
rescale
(which defaults to c(0, 1)
).
mode
should match one of "latent"
(the
default), "linear.predictor"
, "cum.prob"
,
"exc.prob"
, "prob"
, "mean.class"
,
or "scale"
– see the quick reference and note which are
supported. With the exception of "linear.predictor"
, all of
these modes do an implied regrid.
With mode = "latent"
, the reference-grid predictions are
made on the scale of the latent variable implied by the model. The scale
and location of this latent variable are arbitrary, and may be altered
via rescale
. The predictions are multiplied by
rescale[2]
, then added to rescale[1]
. Keep in
mind that the scaling is related to the link function used in the model;
for example, changing from a probit link to a logistic link will inflate
the latent values by around \(\pi/\sqrt{3}\), all other things being
equal. rescale
has no effect for other values of
mode
. Even though the latent means comprise a re-scaling of
the linear predictor, we regard this as a re-gridding, and the
cumulative link function is not included in the reference grid.
With mode = "linear.predictor"
,
mode = "cum.prob"
, and mode = "exc.prob"
, the
boundaries between categories (i.e., thresholds) in the ordinal response
are included in the reference grid as a pseudo-factor named
cut
. The reference-grid predictions are then of the
cumulative probabilities at each threshold (for
mode = "cum.prob"
), exceedance probabilities (one minus
cumulative probabilities, for mode = "exc.prob"
), or the
link function thereof (for mode = "linear.predictor"
).
With mode = "prob"
, a pseudo-factor with the same name
as the model’s response variable is created, and the grid predictions
are of the probabilities of each class of the ordinal response. With
"mean.class"
, the returned results are means of the ordinal
response, interpreted as a numeric value from 1 to the number of
classes, using the "prob"
results as the estimated
probability distribution for each case.
With mode = "scale"
, and the fitted object incorporates
a scale model, EMMs are obtained for the factors in the scale model
(with a log response) instead of the response model. The grid is
constructed using only the factors in the scale model.
Any grid point that is non-estimable by either the location or the
scale model (if present) is set to NA
, and any EMMs
involving such a grid point will also be non-estimable. A consequence of
this is that if there is a rank-deficient scale
model, then
all latent responses become non-estimable because the
predictions are made using the average log-scale estimate.
rms
models have an additional mode
. With
mode = "middle"
(this is the default), the middle intercept
is used, comparable to the default for rms::Predict()
. This
is quite similar in concept to mode = "latent"
, where all
intercepts are averaged together.
Models in this group have their emmeans support provided by the package that implements the model-fitting procedure. Users should refer to the package documentation for details on emmeans support. In some cases, a package’s models may have been supported here in emmeans; if so, the other package’s support overrides it.
The elements of tau
are included in the reference grid
as a pseudo-factor named tau
. In these models, the
covariance matrix is obtained via the model’s summary()
method with covariance = TRUE
. The user may specify one or
more of the other arguments for summary
(e.g.,
se = "boot"
) or to be passed to the ...
argument.
A caveat is that when there is more than one tau
value,
we do not have estimates of the covariances between regression
coefficients associated with different tau
s. Thus, a
contrast involving different tau
s can be estimated but its
SE will be NA
. Also, due to NA
s in the
covariance matrix, the "mvt"
adjustment is unavailable.
Note: Older versions of this rq
and
rqs
support required tau
; now it is
optional. Only tau
values included in
object$tau
are allowed; others are silently ignored. It is
more efficient (and less memory-greedy) to specify tau
than
to subset them using the at
argument to
ref_grid
.
Models fitted using MCMC methods contain a sample from the posterior
distribution of fixed-effect coefficients. In some cases (e.g., results
of MCMCpack::MCMCregress()
and
MCMCpack::MCMCpoisson()
), the object may include a
"call"
attribute that emmeans()
can use to
reconstruct the data and obtain a basis for the EMMs. If not, a
formula
and data
argument are provided that
may help produce the right results. In addition, the
contrasts
specifications are not necessarily recoverable
from the object, so the system default must match what was actually used
in fitting the model.
The summary.emmGrid()
method provides credibility
intervals (HPD intervals) of the results, and ignores the
frequentist-oriented arguments (infer
, adjust
,
etc.) An as.mcmc()
method is provided that creates an
mcmc
object that can be summarized or plotted using the
coda package (or others that support those objects). It
provides a posterior sample of EMMs, or contrasts thereof, for the given
reference grid, based on the posterior sample of the fixed effects from
the model object.
In MCMCglmm
objects, the data
argument is
required; however, if you save it as a member of the model object (e.g.,
object$data = quote(mydata)
), that removes the necessity of
specifying it in each call. The special keyword trait
is
used in some models. When the response is multivariate and numeric,
trait
is generated automatically as a factor in the
reference grid, and the arguments mult.levels
can be used
to name its levels. In other models such as a multinomial model, use the
mode
argument to specify the type of model, and
trait = <factor name>
to specify the name of the data
column that contains the levels of the factor response.
The brms package version 2.13 and later, has its own
emmeans
support. Refer to the documentation in that
package.
aovList
objects (also used with
afex_aov
objects)Support for these objects is limited. To avoid strong biases in the
predictions, it is strongly recommended that when fitting the model, the
contrasts
attribute of all factors should be of a type that
sums to zero – for example, "contr.sum"
,
"contr.poly"
, or "contr.helmert"
but
not "contr.treatment"
. If that is found not to be
the case, the model is re-fitted using sum-to-zero contrasts (thus
requiring additional computation). Doing so does not remove all
bias in the EMMs unless the design is perfectly balanced, and an
annotation is added to warn of that. This bias cancels out when doing
comparisons and contrasts.
Only intra-block estimates of covariances are used. That is, if a
factor appears in more than one error stratum, only the covariance
structure from its lowest stratum is used in estimating standard errors.
Degrees of freedom are obtained using the Satterthwaite method. In
general, aovList
support is best with balanced designs,
with due caution in the use of contrasts. If a vcov.
argument is supplied, it must yield a single covariance matrix for the
unique fixed effects (not a set of them for each error stratum). In that
case, the degrees of freedom are set to NA
.