se.contrast {stats} | R Documentation |

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

object.

se.contrast(object, ...) ## S3 method for class 'aov' se.contrast(object, contrast.obj, coef = contr.helmert(ncol(contrast))[, 1], data = NULL, ...)

`object` |
A suitable fit, usually from |

`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 |

`data` |
The data frame used to evaluate |

`...` |
further arguments passed to or from other methods. |

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.

A vector giving the standard errors for each contrast.

## 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)) # 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.

[Package *stats* version 4.1.0 Index]