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ptw (version 1.9-16)

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. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site.

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Install

install.packages('ptw')

Monthly Downloads

921

Version

1.9-16

License

GPL (>= 2)

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Last Published

January 19th, 2022

Functions in ptw (1.9-16)

warp.time

Transform time according to a given warping function
bestref

Identification of optimal reference
mzchannel2pktab

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

Pad matrix with zeros
select.traces

Select traces from a data set according to several criteria
ptwgrid

Calculate RMS or WCC values on a grid
whit1

Weighted Whittaker smoothing with a first order finite difference penalty
whit2

Weighted Whittaker smoothing with a second order finite difference penalty
wcc

Weighted auto- and cross-correlation measures
predict.ptw

Prediction of warped signals
plot.ptw

Plot a ptw object
ptw

Parametric Time Warping
ptw-package

ptw
gaschrom

16 calibration GC traces
lcms

Parts of 3 proteomic LC-MS samples
baseline.corr

Baseline Correction using asymmetric least squares
calc.multicoef

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

Chromatogram selection using the CODA algorithm
calc.zerocoef

Correction for warping coefficients when using zeropadding
difsm

Smoothing with a finite difference penalty
RMS

Quality criteria for comparing patterns with shifts
asysm

Trend estimation with asymmetric least squares