monoSmu {lumi}R Documentation

Monotonic smooth method

Description

Fit the monotonic-constraint spline curve

Usage

monoSmu(x, y, newX = NULL, nSupport = min(200, length(x)), nKnots = 6, rotate = FALSE, ifPlot = FALSE, xlab = 'x', ylab = 'y', ...)

Arguments

x a vector represents x values
y a vector represents y values
newX the new values to be transformed. If not provided, "x" will be used.
nSupport downsampled data points
nKnots parameter used by monoSpline
rotate determine whether to rotate the axis with 45 degrees in clockwise, i.e., fit the curve in the MA-plot.
ifPlot determine whether to plot intermediate results
xlab the xlab of the plot
ylab the ylab of the plot
... parameters used by supsmu and plot

Details

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)

Value

Return the transformed "newX" based on the smoothed curve

Author(s)

Simon Lin, Pan Du

References

Lin, S.M., Du, P., Kibbe, W.A., {it Model-based Variance-stabilizing Transformation for Illumina Microarray Data}, submitted

See Also

monoSpline

Examples


x <- sort(runif(200) * 100)
y <- x + rnorm(200) * 10
temp <- monoSmu(x, y, ifPlot=TRUE)


[Package lumi version 1.2.0 Index]