\HeaderA{f.Q}{Compute Cochran's Q statistic}{f.Q}
\keyword{htest}{f.Q}
\begin{Description}\relax
Compute Cochran's Q statistic for testing whether the a fixed effects or
a random effects model will be appropriate.
\end{Description}
\begin{Usage}
\begin{verbatim}
f.Q(dadj, varadj)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{dadj}] A matrix, each row is a gene, each column a study, of the
estimated t-statistics. 
\item[\code{varadj}] A matrix, each row is a gene, each column a study, of
the estimated, adjusted variances of the t-statistics.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
A straightforward computation of Cochran's Q statistic. If the null
hypothesis that the data are well modeled by a fixed effects design is
true then the estimate Q values will have approximately a chi-squared
distribution with degrees of freedom equal to the number of studies
minus one.
\end{Details}
\begin{Value}
A vector of length equal to the number of rows of \code{dadj} with the
Q statistics.
\end{Value}
\begin{Author}\relax
L. Lusa and R. Gentleman
\end{Author}
\begin{References}\relax
Choi et al, Combining multiple microarray studies and
modeling interstudy variation. Bioinformatics, 2003, i84-i90.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{dstar}{dstar}},\code{\LinkA{sigmad}{sigmad}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
##none now, this requires a pretty elaborate example
\end{ExampleCode}
\end{Examples}

