| Name : R-ADPF
| |
| Version : 0.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.13
| Date : 2024-06-14 10:57:19
|
| Group : Development/Libraries/Other
| Source RPM : R-ADPF-0.0.1-lp153.2.13.src.rpm
|
| Size : 0.04 MB
| |
| Packager : (none)
| |
| Summary : 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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
