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ptw (version 1.9-13)

Parametric Time Warping

Description

Parametric Time Warping aligns patterns, i.e. it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported.

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Version

Install

install.packages('ptw')

Monthly Downloads

1,357

Version

1.9-13

License

GPL (>= 2)

Maintainer

Ron Wehrens

Last Published

May 25th, 2018

Functions in ptw (1.9-13)

mzchannel2pktab

Conversion between peak lists from hyphenated MS (LCMS, GCMS, ...) data and input for stptw.

Select traces from a data set according to several criteria

Pad matrix with zeros

Trend estimation with asymmetric least squares

Plot a ptw object

Weighted Whittaker smoothing with a second order finite difference penalty

Transform time according to a given warping function

Prediction of warped signals

Calculation of warping coefficients when applying more than one warping function successively

Correction for warping coefficients when using zeropadding

Chromatogram selection using the CODA algorithm

Smoothing with a finite difference penalty

Parametric Time Warping

Calculate RMS or WCC values on a grid

Baseline Correction using asymmetric least squares

Identification of optimal reference

16 calibration GC traces

Weighted auto- and cross-correlation measures

Parts of 3 proteomic LC-MS samples

Weighted Whittaker smoothing with a first order finite difference penalty

Quality criteria for comparing patterns with shifts