\HeaderA{imputeMissingValues}{Imputing log2 ratios}{imputeMissingValues}
\keyword{models}{imputeMissingValues}
\begin{Description}\relax
Imputing log2 ratios
\end{Description}
\begin{Usage}
\begin{verbatim}
imputeMissingValues(seg, chrominfo = chrominfo.Mb, maxChrom =
23, smooth = 0.1)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{seg}] Object of class \code{\LinkA{SegList}{SegList}}
\item[\code{chrominfo}] a chromosomal information associated with the mapping of the data
\item[\code{maxChrom}] Highest chromosome to impute
\item[\code{smooth}] smoothing parameter for the lowess procedure
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
There are two main reasons to impute data.
One is that given that imputation is reasonable,
one can increase the analytical power and improve results.
Another, more practical, is that at the moment many widely used
fuctions in R do not support missing values.
While procedures such as kNN imputations is widely used for gene
expression data, it is more powerful to take advantage of the
genomic structure of the array CGH data and use a smoother.
Note that we perform only one pass of smoothing.
If there still remain missing values, they are imputed by the
median on the chromosome or chromosomal arm where applicable.
\end{Details}
\begin{Value}
Computes and returns the imputed log2 ratio matrix of the aCGH object.
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{SegList}{SegList}}
\end{SeeAlso}

