summary.emmGrid  R Documentation 
emmGrid
objectsThese are the primary methods for obtaining numerical or tabular results from
an emmGrid
object. summary.emmGrid
is the general function for
summarizing emmGrid
objects. It also serves as the print method for
these objects; so for convenience, summary()
arguments may be included
in calls to functions such as emmeans
and
contrast
that construct emmGrid
objects. Note that by
default, summaries for Bayesian models are diverted to
hpd.summary
.
## S3 method for class 'emmGrid'
summary(object, infer, level, adjust, by,
cross.adjust = "none", type, df, calc, null, delta, side, frequentist,
bias.adjust = get_emm_option("back.bias.adj"), sigma, ...)
## S3 method for class 'emmGrid'
confint(object, parm, level = 0.95, ...)
test(object, null, ...)
## S3 method for class 'emmGrid'
test(object, null = 0, joint = FALSE, verbose = FALSE,
rows, by, status = FALSE, ...)
## S3 method for class 'emmGrid'
predict(object, type, interval = c("none", "confidence",
"prediction"), level = 0.95,
bias.adjust = get_emm_option("back.bias.adj"), sigma, ...)
## S3 method for class 'emmGrid'
as.data.frame(x, row.names = NULL, optional,
check.names = TRUE, destroy.annotations = FALSE, ...)
## S3 method for class 'summary_emm'
x[..., as.df = FALSE]
object 
An object of class 
infer 
A vector of one or two logical values. The first determines whether confidence intervals are displayed, and the second determines whether t tests and P values are displayed. If only one value is provided, it is used for both. 
level 
Numerical value between 0 and 1. Confidence level for confidence
intervals, if 
adjust 
Character value naming the method used to adjust 
by 
Character name(s) of variables to use for grouping into separate tables. This affects the family of tests considered in adjusted P values. 
cross.adjust 
Character: 
type 
Character: type of prediction desired. This only has an effect if
there is a known transformation or link function. 
df 
Numeric. If nonmissing, a constant number of degrees of freedom to
use in constructing confidence intervals and P values ( 
calc 
Named list of character value(s) or formula(s).
The expressions in 
null 
Numeric. Null hypothesis value(s), on the linearpredictor scale,
against which estimates are tested. May be a single value used for all, or
a numeric vector of length equal to the number of tests in each family
(i.e., 
delta 
Numeric value (on the linearpredictor scale). If zero, ordinary
tests of significance are performed. If positive, this specifies a
threshold for testing equivalence (using the TOST or twoonesidedtest
method), noninferiority, or nonsuperiority, depending on 
side 
Numeric or character value specifying whether the test is
lefttailed ( 
frequentist 
Ignored except if a Bayesian model was fitted. If missing
or 
bias.adjust 
Logical value for whether to adjust for bias in
backtransforming ( 
sigma 
Error SD assumed for bias correction (when

... 
Optional arguments such as 
parm 
(Required argument for 
joint 
Logical value. If 
verbose 
Logical value. If 
rows 
Integer values. The rows of L to be tested in the joint test. If
missing, all rows of L are used. If not missing, 
status 
logical. If 
interval 
Type of interval desired (partial matching is allowed):

x 
object of the given class 
row.names 
passed to 
optional 
required argument, but ignored in 
check.names 
passed to 
destroy.annotations 
Logical value. If 
as.df 
Logical value. With 
confint.emmGrid
is equivalent to summary.emmGrid with
infer = c(TRUE, FALSE)
. The function test.emmGrid
, when called with
joint = FALSE
, is equivalent to summary.emmGrid
with infer = c(FALSE, TRUE)
.
With joint = TRUE
, test.emmGrid
calculates the Wald test of the
hypothesis linfct %*% bhat = null
, where linfct
and
bhat
refer to slots in object
(possibly subsetted according to
by
or rows
). An error is thrown if any row of linfct
is
nonestimable. It is permissible for the rows of linfct
to be linearly
dependent, as long as null == 0
, in which case a reduced set of
contrasts is tested. Linear dependence and nonzero null
cause an
error. The returned object has an additional "est.fcns"
attribute, which
is a list of the linear functions associated with the joint test.
summary.emmGrid
, confint.emmGrid
, and
test.emmGrid
return an object of class "summary_emm"
, which
is an extension of data.frame
but with a special print
method that displays it with custom formatting. For models fitted using
MCMC methods, the call is diverted to hpd.summary
(with
prob
set to level
, if specified); one may
alternatively use general MCMC summarization tools with the
results of as.mcmc
.
predict
returns a vector of predictions for each row of object@grid
.
The as.data.frame
method returns an object that inherits
from "data.frame"
.
The misc
slot in object
may contain default values for
by
, calc
, infer
, level
, adjust
,
type
, null
, side
, and delta
.
These defaults vary depending
on the code that created the object. The update
method may be
used to change these defaults. In addition, any options set using
‘emm_options(summary = ...)’ will trump those stored in the object's
misc
slot.
With type = "response"
, the transformation assumed can be found in
‘object@misc$tran’, and its label, for the summary is in
‘object@misc$inv.lbl’. Any t
or z
tests are still performed
on the scale of the linear predictor, not the inversetransformed one.
Similarly, confidence intervals are computed on the linearpredictor scale,
then inversetransformed.
Be aware that only univariate transformations and links are
supported in this way. Some multivariate transformations are supported by
mvregrid
.
When bias.adjust
is TRUE
, then backtransformed estimates
are adjusted by adding
0.5 h''(u)\sigma^2
, where h
is the inverse transformation and
u
is the linear predictor. This is based on a secondorder Taylor
expansion. There are better or exact adjustments for certain specific
cases, and these may be incorporated in future updates.
Note: In certain models, e.g., those with nongaussian families,
sigma
is initialized as NA
, and so by default, bias adjustment
is skipped and a warning is issued. You may override this by specifying a
value for sigma
. However, with ordinary generalized linear models,
bias adjustment is inappropriate and you should not try to do it. With GEEs and GLMMs,
you probably should not use sigma(model)
, and instead you should create an
appropriate value using the estimated random effects, e.g., from VarCorr(model)
.
An example is provided in the “transformations” vignette.
The adjust
argument specifies a multiplicity adjustment for tests or
confidence intervals. This adjustment always is applied separately
to each table or subtable that you see in the printed output (see
rbind.emmGrid
for how to combine tables). If there are nonestimable
cases in a by
group, those cases are excluded before determining
the adjustment; that means there could be different adjustments in different groups.
The valid values of adjust
are as follows:
"tukey"
Uses the Studentized range distribution with the number of means in the family. (Available for twosided cases only.)
"scheffe"
Computes p
values from the F
distribution, according to the Scheffe critical value of
\sqrt{rF(\alpha; r, d)}
, where d
is
the error degrees of freedom and r
is the rank of the set of linear
functions under consideration. By default, the value of r
is
computed from object@linfct
for each by group; however, if the
user specifies an argument matching scheffe.rank
, its value will
be used instead. Ordinarily, if there are k
means involved, then
r = k  1
for a full set of contrasts involving all k
means, and
r = k
for the means themselves. (The Scheffe adjustment is available
for twosided cases only.)
"sidak"
Makes adjustments as if the estimates were independent (a conservative adjustment in many cases).
"bonferroni"
Multiplies p
values, or divides significance
levels by the number of estimates. This is a conservative adjustment.
"dunnettx"
Uses our ownad hoc approximation to the
Dunnett distribution for a family of estimates having pairwise
correlations of 0.5
(as is true when comparing treatments with a
control with equal sample sizes). The accuracy of the approximation
improves with the number of simultaneous estimates, and is much faster
than "mvt"
. (Available for twosided cases only.)
"mvt"
Uses the multivariate t
distribution to assess the
probability or critical value for the maximum of k
estimates. This
method produces the same p
values and intervals as the default
summary
or confint
methods to the results of
as.glht
. In the context of pairwise comparisons or comparisons
with a control, this produces “exact” Tukey or Dunnett adjustments,
respectively. However, the algorithm (from the mvtnorm package) uses a
Monte Carlo method, so results are not exactly repeatable unless the same
randomnumber seed is used (see set.seed
). As the family
size increases, the required computation time will become noticeable or even
intolerable, making the "tukey"
, "dunnettx"
, or others more
attractive.
"none"
Makes no adjustments to the p
values.
For tests, not confidence intervals, the Bonferroniinequalitybased adjustment
methods in p.adjust
are also available (currently, these
include "holm"
, "hochberg"
, "hommel"
,
"bonferroni"
, "BH"
, "BY"
, "fdr"
, and
"none"
). If a p.adjust.methods
method other than
"bonferroni"
or "none"
is specified for confidence limits, the
straight Bonferroni adjustment is used instead. Also, if an adjustment method
is not appropriate (e.g., using "tukey"
with onesided tests, or with
results that are not pairwise comparisons), a more appropriate method
(usually "sidak"
) is substituted.
In some cases, confidence and p
value adjustments are only approximate
– especially when the degrees of freedom or standard errors vary greatly
within the family of tests. The "mvt"
method is always the correct
onestep adjustment, but it can be very slow. One may use
as.glht
with methods in the multcomp package to obtain
nonconservative multistep adjustments to tests.
Warning: Nonestimable cases are included in the family to which adjustments
are applied. You may wish to subset the object using the []
operator
to work around this problem.
The cross.adjust
argument is a way of specifying a multiplicity
adjustment across the by
groups (otherwise by default, each group is
treated as a separate family in regards to multiplicity adjustments). It
applies only to p
values. Valid options are one of the
p.adjust.methods
or "sidak"
. This argument is ignored unless
it is other than "none"
, there is more than one by
group, and
they are all the same size. Under those conditions, we first use
adjust
to determine the withingroup adjusted p
values.
Imagine each group's adjusted p
values arranged in sidebyside
columns, thus forming a matrix with the number of columns equal to the
number of by
groups. Then we use the cross.adjust
method to
further adjust the adjusted p
values in each row of this matrix. Note
that an overall Bonferroni (or Sidak) adjustment is obtainable by
specifying both adjust
and cross.adjust
as
"bonferroni"
(or "sidak"
). However, less conservative (but
yet conservative) overall adjustments are available when it is possible to
use an “exact” withingroup method (e.g., adjust = "tukey"
for pairwise comparisons) and cross.adjust
as a conservative
adjustment. [cross.adjust
methods other than "none"
,
"bonferroni"
, or "sidak"
do not seem advisable, but other
p.adjust
methods are available if you can make sense of them.]
When delta = 0
, test statistics are the usual tests of significance.
They are of the form
‘(estimate  null)/SE’. Notationally:
H_0: \theta = \theta_0
versus
H_1: \theta < \theta_0
(leftsided), or
H_1 \theta > \theta_0
(rightsided), or
H_1: \theta \ne \theta_0
(twosided)
The test statistic is
t = (Q  \theta_0)/SE
where Q
is our estimate of \theta
;
then left, right, or twosided p
values are produced,
depending on side
.
When delta
is positive, the test statistic depends on side
as
follows.
H_0: \theta \ge \theta_0 + \delta
versus H_1: \theta < \theta_0 + \delta
t = (Q  \theta_0  \delta)/SE
The p
value is the lowertail probability.
H_0: \theta \le \theta_0  \delta
versus H_1: \theta > \theta_0  \delta
t = (Q  \theta_0 + \delta)/SE
The p
value is the uppertail probability.
H_0: \theta  \theta_0 \ge \delta
versus H_1: \theta  \theta_0 < \delta
t = (Q  \theta_0  \delta)/SE
The p
value is the lowertail probability.
Note that t
is the maximum of t_{nonsup}
and t_{noninf}
.
This is equivalent to choosing the less
significant result in the twoonesidedtest (TOST) procedure.
When the model is rankdeficient, each row x
of object
's
linfct
slot is checked for estimability. If sum(x*bhat)
is found to be nonestimable, then the string NonEst
is displayed for the
estimate, and associated statistics are set to NA
.
The estimability check is performed
using the orthonormal basis N
in the nbasis
slot for the null
space of the rows of the model matrix. Estimability fails when
Nx^2 / x^2
exceeds tol
, which by default is
1e8
. You may change it via emm_options
by setting
estble.tol
to the desired value.
See the warning above that nonestimable cases are still included when determining the family size for Pvalue adjustments.
Some in the statistical and scientific community argue that the term “statistical significance” should be completely abandoned, and that criteria such as “p < 0.05” never be used to assess the importance of an effect. These practices can be too misleading and are prone to abuse. See the “basics” vignette for more discussion.
In doing testing and a transformation and/or link is in force, any
null
and/or delta
values specified must always be on the
scale of the linear predictor, regardless of the setting for 'type'. If
type = "response"
, the null value displayed in the summary table
will be backtransformed from the value supplied by the user. But the
displayed delta
will not be changed, because there (often) is
not a natural way to backtransform it.
When we have type = "response"
, and bias.adj = TRUE
,
the null
value displayed in the output is both backtransformed
and biasadjusted, leading to a rather nonintuitivelooking null value.
However, since the tests themselves are performed on the link scale,
this is the response value at which a *P* value of 1 would be obtained.
The default show
method for emmGrid
objects (with the
exception of newly created reference grids) is print(summary())
.
Thus, with ordinary usage of emmeans
and such, it is
unnecessary to call summary
unless there is a need to
specify other than its default options.
If a data frame is needed, summary
, confint
,
and test
serve this need. as.data.frame
routes to
summary
by default; calling it with destroy.annotations = TRUE
is not recommended for exactly that reason.
If you want to see more digits in the output, use
print(summary(object), digits = ...)
; and if you always want
to see more digits, use emm_options(opt.digits = FALSE)
.
hpd.summary
warp.lm < lm(breaks ~ wool * tension, data = warpbreaks)
warp.emm < emmeans(warp.lm, ~ tension  wool)
warp.emm # implicitly runs 'summary'
confint(warp.emm, by = NULL, level = .90)
# 
pigs.lm < lm(log(conc) ~ source + factor(percent), data = pigs)
pigs.emm < emmeans(pigs.lm, "percent", type = "response")
summary(pigs.emm) # (inherits type = "response")
summary(pigs.emm, calc = c(n = ".wgt.")) # Show sample size
# For which percents is EMM noninferior to 35, based on a 10% threshold?
# Note the test is done on the log scale even though we have type = "response"
test(pigs.emm, null = log(35), delta = log(1.10), side = ">")
con < contrast(pigs.emm, "consec")
test(con)
test(con, joint = TRUE)
# default Scheffe adjustment  rank = 3
summary(con, infer = c(TRUE, TRUE), adjust = "scheffe")
# Consider as some of many possible contrasts among the six cell means
summary(con, infer = c(TRUE, TRUE), adjust = "scheffe", scheffe.rank = 5)
# Show estimates to more digits
print(test(con), digits = 7)
# 
# Crossadjusting P values
prs < pairs(warp.emm) # pairwise comparisons of tension, by wool
test(prs, adjust = "tukey", cross.adjust = "bonferroni")
# Same comparisons taken as one big family (more conservative)
test(prs, adjust = "bonferroni", by = NULL)
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