aov {base} | R Documentation

## Summarize an Analysis of Variance Model

### Description

Summarize an analysis of variance model.

### Usage

    summary(object, intercept = FALSE, split,
             expand.split = TRUE, keep.zero.df = TRUE, ...)
    summary(object, ...)

### Arguments

`object` | An object of class `"aov"` or `"aovlist"`.
`intercept` | logical: should intercept terms be included?
`split` | an optional named list, with names corresponding to terms in the model. Each component is itself a list with integer components giving contrasts whose contributions are to be summed.
`expand.split` | logical: should the split apply also to interactions involving the factor?
`keep.zero.df` | logical: should terms with no degrees of freedom be included?
`...` | Arguments to be passed to or from other methods, for `summary.aovlist` including those for `summary.aov`.

### Value

An object of class `c("summary.aov", "listof")` or `"summary.aovlist"` respectively.

### Note

The use of `expand.split = TRUE` is little tested: it is always possible to set it to `FALSE` and specify exactly all the splits required.

### Author(s)

B. D. Ripley

### See Also

`aov`, `summary`, `model.tables`, `TukeyHSD`

### Examples

    ## From Venables and Ripley (1997) p.210.
    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)

    ( npk.aov <- aov(yield ~ block + N*P*K, npk) )
    summary(npk.aov)
    coefficients(npk.aov)

    # Cochran and Cox (1957, p.164)
    # 3x3 factorial with ordered factors, each is average of 12.
    CC <- data.frame(
        y = c(449, 413, 326, 409, 358, 291, 341, 278, 312)/12,
        P = ordered(gl(3, 3)), N = ordered(gl(3, 1, 9))
    )
    CC.aov <- aov(y ~ N * P, data = CC , weights = rep(12, 9))
    summary(CC.aov)

    # Split both main effects into linear and quadratic parts.
    summary(CC.aov, split = list(N = list(L = 1, Q = 2), P = list(L = 1, Q = 2)))

    # Split only the interaction
    summary(CC.aov, split = list("N:P" = list(L.L = 1, Q = 2:4)))

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