model.tables {stats} | R Documentation |

## Compute Tables of Results from an Aov Model Fit

### Description

Computes summary tables for model fits, especially complex `aov`

fits.

### Usage

model.tables(x, ...)
## S3 method for class 'aov':
model.tables(x, type = "effects", se = FALSE, cterms, ...)
## S3 method for class 'aovlist':
model.tables(x, type = "effects", se = FALSE, ...)

### Arguments

`x` |
a model object, usually produced by `aov` |

`type` |
type of table: currently only `"effects"` and
`"means"` are implemented. |

`se` |
should standard errors be computed? |

`cterms` |
A character vector giving the names of the terms for
which tables should be computed. The default is all tables. |

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

### Details

For `type = "effects"`

give tables of the coefficients for each
term, optionally with standard errors.

For `type = "means"`

give tables of the mean response for each
combinations of levels of the factors in a term.

The `"aov"`

method cannot be applied to components of a
`"aovlist"`

fit.

### Value

An object of class `"tables.aov"`

, as list which may contain components

`tables` |
A list of tables for each requested term. |

`n` |
The replication information for each term. |

`se` |
Standard error information. |

### Warning

The implementation is incomplete, and only the simpler cases have been
tested thoroughly.

Weighted `aov`

fits are not supported.

### See Also

`aov`

, `proj`

,
`replications`

, `TukeyHSD`

,
`se.contrast`

### Examples

## 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)
options(contrasts=c("contr.helmert", "contr.treatment"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
model.tables(npk.aov, "means", se = TRUE)
## as a test, not particularly sensible statistically
npk.aovE <- aov(yield ~ N*P*K + Error(block), npk)
model.tables(npk.aovE, se=TRUE)
model.tables(npk.aovE, "means")

[Package

*stats* version 2.5.0

Index]