summary.aov {stats} | R Documentation |

Summarize an analysis of variance model.

## S3 method for class 'aov': summary(object, intercept = FALSE, split, expand.split = TRUE, keep.zero.df = TRUE, ...) ## S3 method for class 'aovlist': summary(object, ...)

`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` . |

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

or
`"summary.aovlist"`

respectively.

For a fits with a single stratum the result will be a list of
ANOVA tables, one for each response (even if there is only one response):
the tables are of class `"anova"`

inheriting from class
`"data.frame"`

. They have columns `"Df"`

, `"Sum Sq"`

,
`"Mean Sq"`

, as well as `"F value"`

and `"Pr(>F)"`

if
there are non-zero residual degrees of freedom. There is a row for
each term in the model, plus one for `"Residuals"`

if there
are any.

For multistratum fits the return value is a list of such summaries,
one for each stratum.

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.

`aov`

, `summary`

, `model.tables`

,
`TukeyHSD`

## 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) ( 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))) # split on just one var summary(CC.aov, split = list(P = list(lin = 1, quad = 2))) summary(CC.aov, split = list(P = list(lin = 1, quad = 2)), expand.split=FALSE)

[Package *stats* version 2.5.0 Index]