# summary.aov

##### Summarize an Analysis of Variance Model

Summarize an analysis of variance model.

- Keywords
- models, regression

##### Usage

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

##### Examples

`library(stats)`

```
## 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)
```

*Documentation reproduced from package stats, version 3.3.3, License: Part of R 3.3.3*