contrast-methods {emmeans} | R Documentation |
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), ...) revpairwise.emmc(levs, exclude = integer(0), ...) 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), ...) 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), ...) del.eff.emmc(levs, exclude = integer(0), ...) consec.emmc(levs, reverse = FALSE, exclude = integer(0), ...) mean_chg.emmc(levs, reverse = FALSE, exclude = integer(0), ...)
levs |
Vector of factor levels |
exclude |
integer vector with indices of levels to exclude from consideration. These levels will receive weight 0 in all contrasts. |
... |
Additional arguments, passed to related methods as appropriate |
reverse |
Logical value to determine the direction of comparisons |
max.degree |
Integer specifying the maximum degree of polynomial contrasts |
ref |
Integer(s) specifying which level(s) to use as the reference |
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
.
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.
warp.lm <- lm(breaks ~ wool*tension, data = warpbreaks) warp.emm <- emmeans(warp.lm, ~ tension | wool) contrast(warp.emm, "poly") # Compare only low and high tensions # Note pairs(emm, ...) calls contrast(emm, "pairwise", ...) pairs(warp.emm, exclude = 2) ### 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") ## Not run: # See what is used for polynomial contrasts with 6 levels emmeans:::poly.emmc(1:6) ## End(Not run)