# emmc-functions: Contrast families In emmeans: Estimated Marginal Means, aka Least-Squares Means

 contrast-methods R Documentation

## 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, 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, ...)

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` (including any enhancements – see examples) `wts` 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.

### 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.

### 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.

### Note

Caution is needed in cases where the user alters the ordering of results (e.g., using the the `"[...]"` operator), because the contrasts generated depend on the order of the levels provided. For example, suppose `trt.vs.ctrl1` contrasts are applied to two `by` groups with levels ordered (Ctrl, T1, T2) and (T1, T2, Ctrl) respectively, then the contrasts generated will be for (T1 - Ctrl, T2 - Ctrl) in the first group and (T2 - T1, Ctrl - T1) in the second group, because the first level in each group is used as the reference level.

### Examples

``````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")
## Not run:
## Same when enhanced labeling is used:
contrast(warp.emm, "trt.vs.ctrl",
enhance.levels = "tension", ref = "tensionM")
## End(Not run)

# 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")
## Not run:
# See what is used for polynomial contrasts with 6 levels
emmeans:::poly.emmc(1:6)

## End(Not run)
``````

emmeans documentation built on Oct. 18, 2023, 1:13 a.m.