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")

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