\HeaderA{anova.mlm}{Comparisons between Multivariate Linear Models}{anova.mlm}
\aliasA{anova.mlmlist}{anova.mlm}{anova.mlmlist}
\keyword{regression}{anova.mlm}
\keyword{models}{anova.mlm}
\keyword{multivariate}{anova.mlm}
\begin{Description}\relax
Compute a (generalized) analysis of variance table for one or more 
multivariate linear models.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'mlm':
anova(object, ...,
      test = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy", "Spherical"),
      Sigma = diag(nrow = p),
      T = Thin.row(proj(M) - proj(X)), M = diag(nrow = p), X = ~0,
      idata = data.frame(index = seq(length=p)))
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] an object of class \code{"mlm"}.
\item[\code{...}] further objects of class \code{"mlm"}.
\item[\code{test}] choice of test statistic (see below).
\item[\code{Sigma}] (only relevant if  \code{test=="Spherical"}).  Covariance
matrix assumed proportional to \code{Sigma}.
\item[\code{T}] transformation matrix.  By default computed from \code{M} and
\code{X}.
\item[\code{M}] formula or matrix describing the outer projection (see below).
\item[\code{X}] formula or matrix describing the inner projection (see below).
\item[\code{idata}] data frame describing intra-block design.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The \code{anova.mlm} method uses either a multivariate test statistic for
the summary table, or a test based on sphericity assumptions (i.e.
that the covariance is proportional to a given matrix).

For the multivariate test, Wilks' statistic is most popular in the
literature, but the default Pillai--Bartlett statistic is
recommended by Hand and Taylor (1987).

For the \code{"Spherical"} test, proportionality is usually with the
identity matrix but a different matrix can be specified using \code{Sigma}).
Corrections for asphericity known as the Greenhouse--Geisser,
respectively Huynh--Feldt, epsilons are given and adjusted \eqn{F}{} tests are
performed.

It is common to transform the observations prior to testing. This
typically involves 
transformation to intra-block differences, but more complicated
within-block designs can be encountered,
making more elaborate transformations necessary.  A
transformation matrix \code{T} can be given directly or specified as
the difference between two projections onto the spaces spanned by
\code{M} and \code{X}, which in turn can be given as matrices or as
model formulas with respect to \code{idata} (the tests will be
invariant to parametrization of the quotient space \code{M/X}).

As with \code{anova.lm}, all test statistics use the SSD matrix from
the largest model considered as the (generalized) denominator.

Contrary to other \code{anova} methods, the intercept is not excluded
from the display in the single-model case.  When contrast
transformations are involved, it often makes good sense to test for a
zero intercept.
\end{Details}
\begin{Value}
An object of class \code{"anova"} inheriting from class \code{"data.frame"}
\end{Value}
\begin{Note}\relax
The Huynh--Feldt epsilon differs from that calculated by SAS (as of
v. 8.2) except when the DF is equal to the number of observations
minus one.  This is believed to be a bug in SAS, not in \R.
\end{Note}
\begin{References}\relax
Hand, D. J. and Taylor, C. C.  (1987)
\emph{Multivariate Analysis of Variance and Repeated Measures.}
Chapman and Hall.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{summary.manova}{summary.manova}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
example(SSD) # Brings in the mlmfit and reacttime objects

mlmfit0 <- update(mlmfit, ~0)

### Traditional tests of intrasubj. contrasts
## Using MANOVA techniques on contrasts:
anova(mlmfit, mlmfit0, X=~1)

## Assuming sphericity
anova(mlmfit, mlmfit0, X=~1, test="Spherical") 

### tests using intra-subject 3x2 design
idata <- data.frame(deg=gl(3,1,6,labels=c(0,4,8)),
                    noise=gl(2,3,6,labels=c("A","P")))

anova(mlmfit, mlmfit0, X = ~ deg + noise, idata = idata, test = "Spherical")
anova(mlmfit, mlmfit0, M = ~ deg + noise, X = ~ noise, idata = idata,
          test="Spherical" )
anova(mlmfit, mlmfit0, M = ~ deg + noise, X = ~ deg, idata = idata,
          test="Spherical" )

f <- factor(rep(1:2,5)) # bogus, just for illustration
mlmfit2 <- update(mlmfit, ~f)
anova(mlmfit2, mlmfit, mlmfit0, X=~1, test="Spherical")
anova(mlmfit2, X=~1, test="Spherical") # one-model form, eqiv. to previous 

### There seems to be a strong interaction in these data
plot(colMeans(reacttime))
\end{ExampleCode}
\end{Examples}

