emmeans (version 1.5.3)

contrast-methods: Contrast families

Description

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.

Usage

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, ...)
del.eff.emmc(levs, exclude = integer(0), include, ...)
consec.emmc(levs, reverse = FALSE, exclude = integer(0), include, ...)
mean_chg.emmc(levs, reverse = FALSE, exclude = integer(0), include, ...)
identity.emmc(levs, exclude = integer(0), include, ...)

Arguments

levs

Vector of factor levels

exclude

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.

include

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

reverse

Logical value to determine the direction of comparisons

max.degree

Integer specifying the maximum degree of polynomial contrasts

ref

Integer(s) or character(s) specifying which level(s) to use as the reference. Character values must exactly match elements of levs.

Value

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.

Details

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.

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.

Examples

# NOT RUN {
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")

# 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")
# }
# NOT RUN {
# See what is used for polynomial contrasts with 6 levels
emmeans:::poly.emmc(1:6)
# }