estbase: Continuum Estimation

In kuwisdelu/matter: Scientific computing for out-of-memory signals and images

Continuum Estimation

Description

Estimate the continuum (baseline) of a signal.

Usage

# Continuum based on local extrema
estbase_loc(x,
    smooth = c("none", "loess", "spline"),
    span = 1/10, spar = NULL, upper = FALSE)

# Convex hull
estbase_hull(x, upper = FALSE)

# Sensitive nonlinear iterative peak clipping (SNIP)
estbase_snip(x, width = 100L, decreasing = TRUE)

# Running medians
estbase_med(x, width = 100L)

Arguments

x	A numeric vector.
smooth	A smoothing method to be applied after linearly interpolating the continuum.
span, spar	Smoothing parameters for loess and spline smoothing, respectively.
upper	Should the upper continuum be estimated instead of the lower continuum?
width	The width of the smoothing window in number of samples.
decreasing	Use a decreasing clipping window instead of an increasing window.

Details

estbase_loc() uses a simple method based on linearly interpolating from local extrema. It typically performs well enough for most situations. Signals with strong noise or wide peaks may require stronger smoothing after the interpolation step.

estbase_hull() estimates the continuum by finding the lower or upper convex hull using the monotonic chain algorithm of A. M. Andrew (1979).

estbase_snip() performs sensitive nonlinear iterative peak (SNIP) clipping using the adaptive clipping window from M. Morhac (2009).

estbase_med() estimates the continuum from running medians.

Value

A numeric vector the same length as x with the estimated continuum.

Author(s)

Kylie A. Bemis

References

A. M. Andrew. “Another efficient algorithm for convex hulls in two dimensions.” Information Processing Letters, vol. 9, issue 5, pp. 216-219, Dec. 1979.

M. Morhac. “An algorithm for determination of peak regions and baseline elimination in spectroscopic data.” Nuclear Instruments and Methods in Physics Research A, vol. 600, issue 2, pp. 478-487, Mar. 2009.

Examples

set.seed(1)
t <- seq(from=0, to=6 * pi, length.out=2000)
x <- sin(t) + 0.6 * sin(2.6 * t)
lo <- estbase_hull(x)
hi <- estbase_hull(x, upper=TRUE)

plot(x, type="l")
lines(lo, col="red")
lines(hi, col="blue")

kuwisdelu/matter documentation built on May 1, 2024, 5:17 a.m.