turbotrend: turbotrend: a fast scatterplot smoother
In TurboNorm: A fast scatterplot smoother suitable for microarray normalization

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/turbotrend.R

A fast scatterplot smoother based on B-splines with second order difference penalty

1	turbotrend(x, y, w = rep(1, length(y)), n = 100, lambda=10^seq(-10, 10, length=1000), iter=0, method=c("original", "demmler"))

`x,y`	vectors giving the coordinates of the points in the scatter plot.
`w`	vector of weights of with same length as the data for a weighted smoothing. Default all weights are 1.
`n`	an integer indicating the number of intervals equal to 1 + number of knots. Currently the intervals must be langer than 10.
`lambda`	Optionally a user-defined penalty parameter can be provided, if not generalized cross-validation is used to find the optimal penalty parameter.
`iter`	Number of robustifying iterations similar as lowess.
`method`	method for solving the system of linear equations either using the data in the original space or transformed to the Demmler-Reinsch basis.

some details about implementation

An object of type pspline is returned as a list with the following items:

`x`	original data vector x
`y`	fitted y-values with same length as vector x
`w`	vector of weights
`n`	number of bins
`ytrend`	binnend fitted y-values
`xtrend`	binned x-values
`lambda`	if scalar penalty parameter used else if vector of two lower and upper bound of the grid
`iter`	number of robustifying iterations
`gcv`	generalized cross-validation
`edf`	effective degrees of freedom (trace of the smoother matrix)
`call`	function call which produced this output

Maarten van Iterson, Chantal van Leeuwen

van Iterson M, Duijkers FA, Meijerink JP, Admiraal P, van Ommen GJ, Boer JM, van Noesel MM, Menezes RX (2012). A novel and fast normalization method for high-density arrays. SAGMB, 11(4).

Paul .H.C. Eilers and Brain D. Marx (1996). Flexible smoothing with B-splines and Penalties. Statistical Science, Vol 11, No. 2, 89-121.

loess,lowess, smooth, smooth.spline and smooth.Pspline

library(marray)
data(swirl)

x <- maA(swirl)[,1]
y <- maM(swirl)[,1]
xord <- x[order(x)]
yord <- y[order(x)]

plot(xord, yord, main = "data(swirl)  &  smoothing splines + lowess")
lines(turbotrend(xord, yord), col = "red", lwd=2)
lines(smooth.spline(xord, yord), col = "green", lwd=2)
lines(lowess(xord, yord), col = "purple", lwd=2)
legend("topleft", c("piecewise constant P-splines", "Cubic B-splines", "lowess"), text.col=c("red","green","purple"), bty="n")