\HeaderA{monoSmu}{Monotonic smooth method}{monoSmu}
\keyword{methods}{monoSmu}
\begin{Description}\relax
Fit the monotonic-constraint spline curve
\end{Description}
\begin{Usage}
\begin{verbatim}
monoSmu(x, y, newX = NULL, nSupport = min(200, length(x)), nKnots = 6, rotate = FALSE, ifPlot = FALSE, xlab = 'x', ylab = 'y', ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a vector represents x values 
\item[\code{y}] a vector represents y values 
\item[\code{newX}] the new values to be transformed. If not provided, "x" will be used. 
\item[\code{nSupport}] downsampled data points 
\item[\code{nKnots}] parameter used by \code{\LinkA{monoSpline}{monoSpline}} 
\item[\code{rotate}] determine whether to rotate the axis with 45 degrees in clockwise, i.e., fit the curve in the MA-plot. 
\item[\code{ifPlot}] determine whether to plot intermediate results 
\item[\code{xlab}] the xlab of the plot 
\item[\code{ylab}] the ylab of the plot 
\item[\code{...}] parameters used by \code{\LinkA{supsmu}{supsmu}} and \code{\LinkA{plot}{plot}} 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
function called by lumiN.rsn. The function first fits a monotonic spline between vector x and y, then transforms the vector newX based on the fitted spline. (After transformation the fitted spline is supposed to be a diagonal line, i.e., x=y)
\end{Details}
\begin{Value}
Return the transformed "newX" based on the smoothed curve
\end{Value}
\begin{Author}\relax
Simon Lin, Pan Du
\end{Author}
\begin{References}\relax
Lin, S.M., Du, P., Kibbe, W.A., (2008) 'Model-based  Variance-stabilizing Transformation for Illumina Microarray Data', Nucleic Acids Res. 36, e11
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{monoSpline}{monoSpline}}
\end{SeeAlso}

