Fractional Factorial Analysis Objects

DESCRIPTION:

Classes of objects that result from the analysis of fractional factorial designs.

GENERATION:

These classes of objects are returned from fac.aov. An object of class "fac.aov" represents the fit for a fractional factorial analysis of variance.

METHODS:

The class "fac.aov" has methods for the following generic functions: pareto , plot, predict, print, print.summary, qqnorm, summary.

INHERITANCE:

Class "fac.aov" inherits from class "aov".

STRUCTURE:

The fac.aov object is implemented as a list with the following components in addition to an aov object:

VALUE:

fcoefficients
The fractional factorial coefficients give the parametrization of a convential fit for factorial experiments, i.e. 1/2 of the average difference between high and low levels of the factor. These are sometimes referred to as half-effects.
feffects
For a standard fractional factorial design, these are the conventional effects (twice the fcoefficients or half-effects). For unbalanced or nonorthogonal designs, the feffects are calculated from the fcoefficients using the value of adj.coef to make all of the standard errors equal. (See below.)
seffects
The effects from the saturated model. In most cases these are identical to feffects. However if a fac.aov object has been refitted with update, forming a non-saturated model, the saturated effects are stored here, and the reduced model effects in feffects
adj.coef
A vector of adjustment factors used in feffects = fcoefficients*adj.coef so that feffects have a constant standard error, a property that is required for the computation methods used for pse, tse and ase.
mse
error estimates corresponding to the mean square standard error.
pse
error estimates corresponding to the pseudo standard error.
tse
error estimates corresponding to the trimmed standard error.
ase
error estimates corresponding to the adaptive standard error.
design.name
The design name, if any, from the original data.
x
the model matrix created by the call to aov.

SEE ALSO:

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