Analysis of Variance for Balanced Designs
DESCRIPTION:
Fits an analysis of variance model for balanced designs.
USAGE: 
aov.genyates(formula, data=<<see below>>, onedf=F)
REQUIRED ARGUMENTS:
formula:
the formula for the model. The formula must not include an Error term, nor may it have a multiple response.
OPTIONAL ARGUMENTS:
data:
if supplied, a data frame in which the objects named in the formula are to be found. If data is omitted, the current search list is used; frequently, a data frame will have been attached.
onedf:
logical flag: when onedf==TRUE, the function returns single degree of freedom projections, otherwise, projections are collapsed.
VALUE:
an object describing the fit. It will be of class aov. In addition, the fit contains the projection matrix:
proj:
an orthogonal matrix of the projections for the model.
DETAILS:
This function produces a more direct computation of an analysis of variance model than does aov. The aov.genyates function will be faster than the aov function for large balanced datasets; for small datasets there is virtually no difference in speed.

The proj component is identical to the result of the proj.lm function applied to the aov structure. Thus the two expressions: proj.lm(aov(formula, data, qr=T, onedf=F)) aov.genyates(formula, data, onedf=F)$proj yield the same result. When onedf is TRUE, there is one projection for each degree of freedom in the model. When onedf==FALSE, the function collapses the single degree of freedom projections into multi-degree of freedom projections. Each column of the collapsed result represents one term of the analysis of variance table. The sum of squares of each column is the sum of squares for the corresponding term in the model.

WARNING:
aov.genyates does no checking for balance (and in fact will erroneously print that effects are balanced when they are not). It is up to the user to make sure that the design is balanced.
SEE ALSO:
proj , proj.lm , aov , aov.object .
EXAMPLES: 
aov.genyates(Yield ~ Temp * Conc * Cat, catalyst)