
Functions with an extension of .emmc
provide for named contrast
families. One of the standard ones documented here may be used, or the user
may write such a function.
pairwise.emmc(levs, exclude = integer(0), include, ...)revpairwise.emmc(levs, exclude = integer(0), include, ...)
tukey.emmc(levs, reverse = FALSE, ...)
poly.emmc(levs, max.degree = min(6, k - 1), ...)
trt.vs.ctrl.emmc(levs, ref = 1, reverse = FALSE, exclude = integer(0),
include, ...)
trt.vs.ctrl1.emmc(levs, ref = 1, ...)
trt.vs.ctrlk.emmc(levs, ref = length(levs), ...)
dunnett.emmc(levs, ref = 1, ...)
eff.emmc(levs, exclude = integer(0), include, wts = rep(1, length(levs)),
...)
del.eff.emmc(levs, exclude = integer(0), include, wts = rep(1,
length(levs)), ...)
consec.emmc(levs, reverse = FALSE, exclude = integer(0), include, ...)
mean_chg.emmc(levs, reverse = FALSE, exclude = integer(0), include, ...)
wtcon.emmc(levs, wts, cmtype = "GrandMean", ...)
identity.emmc(levs, exclude = integer(0), include, ...)
A data.frame, each column containing contrast coefficients for levs. The "desc" attribute is used to label the results in emmeans, and the "adjust" attribute gives the default adjustment method for multiplicity.
Vector of factor levels
integer vector of indices, or character vector of levels to
exclude from consideration. These levels will receive weight 0 in all
contrasts. Character levels must exactly match elements of levs
.
integer or character vector of levels to include (the
complement of exclude
). An error will result if the user specifies
both exclude
and include
.
Additional arguments, passed to related methods as appropriate
Logical value to determine the direction of comparisons
Integer specifying the maximum degree of polynomial contrasts
Integer(s) or character(s) specifying which level(s) to use
as the reference. Character values must exactly match elements of levs
(including any enhancements -- see examples)
Optional weights to use with eff.emmc
and del.eff.emmc
contrasts.
These default to equal weights.
If exclude
or include
are specified, wts
may be
either the same length as levs
or the length of the included levels.
In the former case, weights for any excluded levels are set to zero.
wts
has no impact on the results unless there are at least
three levels included in the contrast.
the type
argument passed to contrMat
Each standard contrast family has a default multiple-testing adjustment as
noted below. These adjustments are often only approximate; for a more
exacting adjustment, use the interfaces provided to glht
in the
multcomp package.
pairwise.emmc
, revpairwise.emmc
, and tukey.emmc
generate
contrasts for all pairwise comparisons among estimated marginal means at the
levels in levs. The distinction is in which direction they are subtracted.
For factor levels A, B, C, D, pairwise.emmc
generates the comparisons
A-B, A-C, A-D, B-C, B-D, and C-D, whereas revpairwise.emmc
generates
B-A, C-A, C-B, D-A, D-B, and D-C. tukey.emmc
invokes
pairwise.emmc
or revpairwise.emmc
depending on reverse
.
The default multiplicity adjustment method is "tukey"
, which is only
approximate when the standard errors differ.
poly.emmc
generates orthogonal polynomial contrasts, assuming
equally-spaced factor levels. These are derived from the
poly
function, but an ad hoc algorithm is used to
scale them to integer coefficients that are (usually) the same as in
published tables of orthogonal polynomial contrasts. The default multiplicity
adjustment method is "none"
.
trt.vs.ctrl.emmc
and its relatives generate contrasts for comparing
one level (or the average over specified levels) with each of the other
levels. The argument ref
should be the index(es) (not the labels) of
the reference level(s). trt.vs.ctrl1.emmc
is the same as
trt.vs.ctrl.emmc
with a reference value of 1, and
trt.vs.ctrlk.emmc
is the same as trt.vs.ctrl
with a reference
value of length(levs)
. dunnett.emmc
is the same as
trt.vs.ctrl
. The default multiplicity adjustment method is
"dunnettx"
, a close approximation to the Dunnett adjustment.
Note in all of these functions, it is illegal to have any overlap
between the ref
levels and the exclude
levels. If any is found,
an error is thrown.
consec.emmc
and mean_chg.emmc
are useful for contrasting
treatments that occur in sequence. For a factor with levels A, B, C, D, E,
consec.emmc
generates the comparisons B-A, C-B, and D-C, while
mean_chg.emmc
generates the contrasts (B+C+D)/3 - A, (C+D)/2 -
(A+B)/2, and D - (A+B+C)/3. With reverse = TRUE
, these differences go
in the opposite direction.
eff.emmc
and del.eff.emmc
generate contrasts that compare each
level with the average over all levels (in eff.emmc
) or over all other
levels (in del.eff.emmc
). These differ only in how they are scaled.
For a set of k EMMs, del.eff.emmc
gives weight 1 to one EMM and weight
-1/(k-1) to the others, while eff.emmc
gives weights (k-1)/k and -1/k
respectively, as in subtracting the overall EMM from each EMM. The default
multiplicity adjustment method is "fdr"
. This is a Bonferroni-based
method and is slightly conservative; see p.adjust
.
wtcon.emmc
generates weighted contrasts based on the function
contrMat
function in the multcomp package,
using the provided type
as documented there. If the user provides
wts
, they have to conform to the length of levs
; however,
if wts
is not specified, contrast
will fill-in what is
required, and usually this is safer (especially when by != NULL
which usually means that the weights are different in each by
group).
identity.emmc
simply returns the identity matrix (as a data frame),
minus any columns specified in exclude
. It is potentially useful in
cases where a contrast function must be specified, but none is desired.
warp.lm <- lm(breaks ~ wool*tension, data = warpbreaks)
warp.emm <- emmeans(warp.lm, ~ tension | wool)
contrast(warp.emm, "poly")
contrast(warp.emm, "trt.vs.ctrl", ref = "M")
if (FALSE) {
## Same when enhanced labeling is used:
contrast(warp.emm, "trt.vs.ctrl",
enhance.levels = "tension", ref = "tensionM")}
# Comparisons with grand mean
contrast(warp.emm, "eff")
# Comparisons with a weighted grand mean
contrast(warp.emm, "eff", wts = c(2, 5, 3))
# Compare only low and high tensions
# Note pairs(emm, ...) calls contrast(emm, "pairwise", ...)
pairs(warp.emm, exclude = 2)
# (same results using exclude = "M" or include = c("L","H") or include = c(1,3))
### Setting up a custom contrast function
helmert.emmc <- function(levs, ...) {
M <- as.data.frame(contr.helmert(levs))
names(M) <- paste(levs[-1],"vs earlier")
attr(M, "desc") <- "Helmert contrasts"
M
}
contrast(warp.emm, "helmert")
if (FALSE) {
# See what is used for polynomial contrasts with 6 levels
emmeans:::poly.emmc(1:6)
}
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