summary.aov {stats} | R Documentation |

## Summarize an Analysis of Variance Model

### Description

Summarize an analysis of variance model.

### Usage

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

### Arguments

`object` |
An object of class |

`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 |

### Value

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

or
`"summary.aovlist"`

respectively.

For 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.

### 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.

### See Also

`aov`

, `summary`

, `model.tables`

,
`TukeyHSD`

### Examples

```
## For a simple example see example(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)
```

*stats*version 4.4.0 Index]