Package 'ADPF'

Title: Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay
Description: This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.
Authors: Phillip Barak [aut], Samuel Kruse [cre, aut]
Maintainer: Samuel Kruse <[email protected]>
License: GPL-3
Version: 0.0.1
Built: 2024-11-13 03:26:58 UTC
Source: https://github.com/cran/ADPF

Help Index

Adaptive Degree Polynomial Filter [ADPF]

Description

ADPF outputs a data.frame containing a column for the original data, the polynomial degree used to smooth it, and the requested derivative(s).

Usage

ADPF(YData, SthDeriv,MaxOrder,FilterLength, DeltaX, WriteFile)

Arguments

YData

a numeric data.frame, matrix or vector to transform
SthDeriv

differentiation order
MaxOrder

maximum polynomial order
FilterLength

window size (must be odd)
DeltaX

optional sampling interval
WriteFile

a boolean that writes a data.frame to the working directory if true

Details

This is a code listing of a smoothing algorithm published in 1995 and written by Phillip Barak. ADPF modifies the Savitzky-Golay algorithm with a statistical heurism that increases signal fidelty while decreasing statisical noise. Mathematically, it operates simply as a weighted sum over a given window:

ftn,s=∑i=−mmhin,s,tyif_{t}^{n,s}=\sum{_{i=-m}^{m} h_{i}^{n,s,t}y_{i}}

Where hin,s,th_{i}^{n,s,t} is the convolution weight of the iith point to the evaluate the ssth derivative at point tt using a polynomial of degree nn on 2m+1m+1 data points, yy. These convolution weights hh are calculated using Gram polynomials which are optimally selected using a FchiF_{chi} test. This improves upon the signal fidelity of Savitzky-Golay by optimally choosing the Gram polynomial degree between zero and the max polynomial order give by the user while removing statistical noise. The sampling interval specified with the DeltaX argument is used for scaling and get numerically correct derivatives. For more details on the statistical heurism see the Barak, 1995 article. This can be found at http://soils.wisc.edu/facstaff/barak/ under the publications section.

Author(s)

Phillip Barak

Samuel Kruse

References

Barak, P., 1995. Smoothing and Differentiation by and Adaptive-Degree Polynomial filter; Anal. Chem. 67, 2758-2762.

Marchand, P.; Marmet, L. Rev. Sci. Instrum. 1983, 54, 1034-1041.

Greville, T. N. E., Ed. Theory and Applications of Spline Functions; Academic Press: New York, 1969.

Press, W. H.; Flannery, B. P.; Teukolsky, S. A.;Vetterling. W. T. Numerical Recipes; Cambridge University Press: Cambridge U.K., 1986.

Savitzky, A., and Golay, M. J. E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627-1639.

Macauly, F. R. The Smoothing of Time Series; National Bureau of Economic Research, Inc,: New York, 1931.

Gorry, P. A. Anal. Chem. 1964, 36,1627-1639.

Steiner, J.; Termonia, Y.; Deltour, J. Anal. Chem. 1972, 44. 1906-1909.

Ernst, R. R. Adv. magn. Reson. 1966, 2,1-135.

Gorry P. A. Anal. Chem. 1991, 64, 534-536.

Ratzlaff, K. L.; Johnson, J. T. Adal. Chem. 1989, 61, 1303-1305.

Kuo, J. E.; Wang, H.; Pickup, S. Anal. Chem. 1991, 63,630-645.

Enke, C. G; Nieman, T. A. Anal. Chm 1976, 48, 705A-712A.

Phillips, G. R., Harris, J. M. Anal. Chem. 1990, 62, 2749-2752.

Duran, B.S. Polynomial Regression. In Encyclopedia of the Statistical Sciences, Kotz, S., Johnsonn N. L., Eds.; Wiley: New York, 1986; Vol. 7, pp 700-703.

Bevington, P. R. Data Reduction and Error Analysis for the Physical Sciences; McGraw-Hill Book Co,: New York, 1969; Chapter 10.

Snedecor, G. W.; Cochran, W. G. Statistical Methods, 6th ed.; Iowa State University Press: Ames, IA, 1967; Chapter 15.

Hanning, R. W. Digital Filters, 2nd ed.; Prentice-Hall: Englewood Cliffs, NJ, 1983; Chapter 3.

Ralston, A. A First Course in Numerical Analysis McGraw-Hill: New York, 1965; Chapter 6.

Robert De Levie. 2008. Advanced Excel for Scientific data analysis. 2nd edn. Chapter 3.15 Least squares for equidistant data. Oxford Univ. Press, New York, NY.

Wentzell, P. D., and Brown, C. D., 2000. Signal processing in analytical chemistry. Encyclopedia of Analytical Chemistry, 9764-9800.

Examples

ADPF::CHROM

smooth<-ADPF(CHROM[,6],0,9,13)
numpoints=length(CHROM[,6])
plot(x=1:numpoints,y=CHROM[,6]);lines(x=1:numpoints, y=smooth[,3])

Data frame of Chromatogram values

Description

This file contains a data.frame of sample chromotography data. The 6th column is data without noise and the first five all have some gaussian noise added; these data sets showcase the advantages of ADPF over Savitzky-Golay.

Usage

data("CHROM")

Format

A data frame with 201 observations on the following 6 variables.

CHROM1

a numeric vector

CHROM2

a numeric vector

CHROM3

a numeric vector

CHROM4

a numeric vector

CHROM5

a numeric vector

CHROM6

a numeric vector

Source

Barak, P., 1995. Smoothing and Differentiation by and Adaptive-Degree Polynomial filter; Anal. Chem. 67, 2758-2762.

Examples

ADPF::CHROM

smooth<-ADPF(CHROM[,6],0,9,13)
numpoints=length(CHROM[,6])
plot(x=1:numpoints,y=CHROM[,6]);lines(x=1:numpoints, y=smooth[,3])