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fastanova([Model] [,print:F or silent:T,fstats:T,pvals:T])

Keywords: glm, anova
fastanova(Model) computes the analysis of variance table for the model
given in the CHARACTER variable Model.  No variates (only factors) can
be in the model.  It uses an iterative algorithm rather than the
modified Gram-Schmidt used by anova().

Caution:  If there are empty cells in the design, the degrees of freedom
and hence the mean squares may be in error.

fastanova(), with no Model specified, uses the model from the most
recent GLM command such as anova() or poisson() or the model in

fastanova(Model,maxiter:N,epsilon:eps) where N is a positive integer and
eps is a small REAL scalar, iterates no more than N times (default is
25) on each fit and uses the value of eps (default is 1e-6) to determine
when convergence has occurred.  Either keyword can appear without the

Other keyword phrases that can be used with fastanova() are 'print:T',
'silent:T', 'fstats:T' and 'pvals:T'.  See topic 'glm_keys' for details.
See topic 'options' for information on changing the default values of
'fstats' and 'pvals'.

Keyword phrases 'coefs:F' and 'marginal:T' cannot be used with

See topic 'models' for information on specifying Model.

Coefficients may be retrieved by coefs(); standard errors are not

contrast() does not work properly after fastanova().  coefs() may or may
not give the correct answers; fastanova() will warn you when coefs()
will fail.  cellstats() results include the estimated values for any
missing data.

The iterative fitting method used by fastanova() is faster than Gram-
Schmidt for large unbalanced data sets.  The time used is roughly
proportional to length(y)*nterms, where y is the response variable, and
nterms is the number of terms in the model (the not the model degrees of
freedom).  Thus, fastanova() gives greatest speed improvements for
models with relatively few terms, each with relatively many degrees of
freedom.  fastanova() is least effective for models with many terms,
each with few degrees of freedom.  In fact, it may be slower than
anova() for such models.

Gary Oehlert 2003-01-15