# se.contrast

0th

Percentile

##### Standard Errors for Contrasts in Model Terms

Returns the standard errors for one or more contrasts in an aov object.

Keywords
models
##### Usage
se.contrast(object, …)
# S3 method for aov
se.contrast(object, contrast.obj,
coef = contr.helmert(ncol(contrast))[, 1],
data = NULL, …)
##### Arguments
object

A suitable fit, usually from aov.

contrast.obj

The contrasts for which standard errors are requested. This can be specified via a list or via a matrix. A single contrast can be specified by a list of logical vectors giving the cells to be contrasted. Multiple contrasts should be specified by a matrix, each column of which is a numerical contrast vector (summing to zero).

coef

used when contrast.obj is a list; it should be a vector of the same length as the list with zero sum. The default value is the first Helmert contrast, which contrasts the first and second cell means specified by the list.

data

The data frame used to evaluate contrast.obj.

further arguments passed to or from other methods.

##### Details

Contrasts are usually used to test if certain means are significantly different; it can be easier to use se.contrast than compute them directly from the coefficients.

In multistratum models, the contrasts can appear in more than one stratum, in which case the standard errors are computed in the lowest stratum and adjusted for efficiencies and comparisons between strata. (See the comments in the note in the help for aov about using orthogonal contrasts.) Such standard errors are often conservative.

Suitable matrices for use with coef can be found by calling contrasts and indexing the columns by a factor.

##### Value

A vector giving the standard errors for each contrast.

contrasts, model.tables

##### Aliases
• se.contrast
• se.contrast.aov
• se.contrast.aovlist
##### Examples
library(stats) # NOT RUN { ## From Venables and Ripley (2002) p.165. N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) npk <- data.frame(block = gl(6,4), N = factor(N), P = factor(P), K = factor(K), yield = yield) ## Set suitable contrasts. options(contrasts = c("contr.helmert", "contr.poly")) npk.aov1 <- aov(yield ~ block + N + K, data = npk) se.contrast(npk.aov1, list(N == "0", N == "1"), data = npk) # or via a matrix cont <- matrix(c(-1,1), 2, 1, dimnames = list(NULL, "N")) se.contrast(npk.aov1, cont[N, , drop = FALSE]/12, data = npk) ## test a multi-stratum model npk.aov2 <- aov(yield ~ N + K + Error(block/(N + K)), data = npk) se.contrast(npk.aov2, list(N == "0", N == "1")) ## an example looking at an interaction contrast ## Dataset from R.E. Kirk (1995) ## 'Experimental Design: procedures for the behavioral sciences' score <- c(12, 8,10, 6, 8, 4,10,12, 8, 6,10,14, 9, 7, 9, 5,11,12, 7,13, 9, 9, 5,11, 8, 7, 3, 8,12,10,13,14,19, 9,16,14) A <- gl(2, 18, labels = c("a1", "a2")) B <- rep(gl(3, 6, labels = c("b1", "b2", "b3")), 2) fit <- aov(score ~ A*B) cont <- c(1, -1)[A] * c(1, -1, 0)[B] sum(cont) # 0 sum(cont*score) # value of the contrast se.contrast(fit, as.matrix(cont)) (t.stat <- sum(cont*score)/se.contrast(fit, as.matrix(cont))) summary(fit, split = list(B = 1:2), expand.split = TRUE) ## t.stat^2 is the F value on the A:B: C1 line (with Helmert contrasts) ## Now look at all three interaction contrasts cont <- c(1, -1)[A] * cbind(c(1, -1, 0), c(1, 0, -1), c(0, 1, -1))[B,] se.contrast(fit, cont) # same, due to balance. rm(A, B, score) ## multi-stratum example where efficiencies play a role ## An example from Yates (1932), ## a 2^3 design in 2 blocks replicated 4 times Block <- gl(8, 4) A <- factor(c(0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1, 0,1,0,1,0,1,0,1,0,1,0,1)) B <- factor(c(0,0,1,1,0,0,1,1,0,1,0,1,1,0,1,0,0,0,1,1, 0,0,1,1,0,0,1,1,0,0,1,1)) C <- factor(c(0,1,1,0,1,0,0,1,0,0,1,1,0,0,1,1,0,1,0,1, 1,0,1,0,0,0,1,1,1,1,0,0)) Yield <- c(101, 373, 398, 291, 312, 106, 265, 450, 106, 306, 324, 449, 272, 89, 407, 338, 87, 324, 279, 471, 323, 128, 423, 334, 131, 103, 445, 437, 324, 361, 302, 272) aovdat <- data.frame(Block, A, B, C, Yield) fit <- aov(Yield ~ A + B * C + Error(Block), data = aovdat) cont1 <- c(-1, 1)[A]/32 # Helmert contrasts cont2 <- c(-1, 1)[B] * c(-1, 1)[C]/32 cont <- cbind(A = cont1, BC = cont2) colSums(cont*Yield) # values of the contrasts se.contrast(fit, as.matrix(cont)) # } # NOT RUN { # comparison with lme library(nlme) fit2 <- lme(Yield ~ A + B*C, random = ~1 | Block, data = aovdat) summary(fit2)\$tTable # same estimates, similar (but smaller) se's. # } 
Documentation reproduced from package stats, version 3.6.1, License: Part of R 3.6.1

### Community examples

Looks like there are no examples yet.