\HeaderA{MArrayLM-class}{Microarray Linear Model Fit - class}{MArrayLM.Rdash.class}
\keyword{classes}{MArrayLM-class}
\keyword{regression}{MArrayLM-class}
\begin{Description}\relax
A list-based class for storing the results of fitting gene-wise linear models to a batch of microarrays.
Objects are normally created by \code{\LinkA{lmFit}{lmFit}}.
\end{Description}
\begin{Section}{Slots/Components}
\code{MArrayLM} objects do not contain any slots (apart from \code{.Data}) but they should contain the following list components:
\describe{
\item[\code{coefficients}:] \code{matrix} containing fitted coefficients or contrasts
\item[\code{stdev.unscaled}:] \code{matrix} containing unscaled standard deviations of the coefficients or contrasts
\item[\code{sigma}:] \code{numeric} vector containing residual standard deviations for each gene
\item[\code{df.residual}:] \code{numeric} vector containing residual degrees of freedom for each gene
}
Objects may also contain the following optional components:
\describe{
\item[\code{Amean}:] \code{numeric} vector containing the average log-intensity for each probe over all the arrays in the original linear model fit.
Note this vector does not change when a contrast is applied to the fit using \code{contrasts.fit}.
\item[\code{genes}:] \code{data.frame} containing gene names and annotation
\item[\code{design}:] design \code{matrix} of full column rank
\item[\code{contrasts}:] \code{matrix} defining contrasts of coefficients for which results are desired
\item[\code{F}:] \code{numeric} vector giving moderated F-statistics for testing all contrasts equal to zero
\item[\code{F.p.value}:] \code{numeric} vector giving p-value corresponding to \code{F.stat}
\item[\code{s2.prior}:] \code{numeric} value giving empirical Bayes estimated prior value for residual variances
\item[\code{df.prior}:] \code{numeric} vector giving empirical Bayes estimated degrees of freedom associated with \code{s2.prior} for each gene
\item[\code{s2.post}:] \code{numeric} vector giving posterior residual variances
\item[\code{t}:] \code{matrix} containing empirical Bayes t-statistics
\item[\code{var.prior}:] \code{numeric} vector giving empirical Bayes estimated prior variance for each true coefficient
\item[\code{cov.coefficients}:] numeric \code{matrix} giving the unscaled covariance matrix of the estimable coefficients
\item[\code{pivot}:] \code{integer} vector giving the order of coefficients in \code{cov.coefficients}. Is computed by the QR-decomposition of the design matrix.  
}
If there are no weights and no missing values, then the \code{MArrayLM} objects returned by \code{lmFit} will also contain the QR-decomposition of the design matrix, and any other components returned by \code{lm.fit}.
\end{Section}
\begin{Section}{Methods}
\code{RGList} objects will return dimensions and hence functions such as \code{\LinkA{dim}{dim}}, \code{\LinkA{nrow}{nrow}} and \code{\LinkA{ncol}{ncol}} are defined. 
\code{MArrayLM} objects inherit a \code{show} method from the virtual class \code{LargeDataObject}.

The functions \code{\LinkA{ebayes}{ebayes}} and \code{\LinkA{classifyTestsF}{classifyTestsF}} accept \code{MArrayLM} objects as arguments.
\end{Section}
\begin{Author}\relax
Gordon Smyth
\end{Author}
\begin{SeeAlso}\relax
\LinkA{02.Classes}{02.Classes} gives an overview of all the classes defined by this package.
\end{SeeAlso}

