Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove
Zero offset correction of diving depth measured by time-depth recorders is required to remove artifacts arising from temporal changes in accuracy of pressure transducers. Currently used methods for this procedure are in the proprietary software domain, where researchers cannot study it in sufficient...
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ftpubmed:oai:pubmedcentral.nih.gov:3030565 2023-05-15T14:00:41+02:00 Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove Luque, Sebastián P. Fried, Roland 2011-01-28 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030565 http://www.ncbi.nlm.nih.gov/pubmed/21297976 https://doi.org/10.1371/journal.pone.0015850 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030565 http://www.ncbi.nlm.nih.gov/pubmed/21297976 http://dx.doi.org/10.1371/journal.pone.0015850 Luque, Fried. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2011 ftpubmed https://doi.org/10.1371/journal.pone.0015850 2013-09-03T10:33:26Z Zero offset correction of diving depth measured by time-depth recorders is required to remove artifacts arising from temporal changes in accuracy of pressure transducers. Currently used methods for this procedure are in the proprietary software domain, where researchers cannot study it in sufficient detail, so they have little or no control over how their data were changed. GNU R package diveMove implements a procedure in the Free Software domain that consists of recursively smoothing and filtering the input time series using moving quantiles. This paper describes, demonstrates, and evaluates the proposed method by using a “perfect” data set, which is subsequently corrupted to provide input for the proposed procedure. The method is evaluated by comparing the corrected time series to the original, uncorrupted, data set from an Antarctic fur seal (Arctocephalus gazella Peters, 1875). The Root Mean Square Error of the corrected data set, relative to the “perfect” data set, was nearly identical to the magnitude of noise introduced into the latter. The method, thus, provides a flexible, reliable, and efficient mechanism to perform zero offset correction for analyses of diving behaviour. We illustrate applications of the method to data sets from four species with large differences in diving behaviour, measured using different sampling protocols and instrument characteristics. Text Antarc* Antarctic Antarctic Fur Seal Arctocephalus gazella PubMed Central (PMC) Antarctic PLoS ONE 6 1 e15850 |
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Research Article Luque, Sebastián P. Fried, Roland Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
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Research Article |
description |
Zero offset correction of diving depth measured by time-depth recorders is required to remove artifacts arising from temporal changes in accuracy of pressure transducers. Currently used methods for this procedure are in the proprietary software domain, where researchers cannot study it in sufficient detail, so they have little or no control over how their data were changed. GNU R package diveMove implements a procedure in the Free Software domain that consists of recursively smoothing and filtering the input time series using moving quantiles. This paper describes, demonstrates, and evaluates the proposed method by using a “perfect” data set, which is subsequently corrupted to provide input for the proposed procedure. The method is evaluated by comparing the corrected time series to the original, uncorrupted, data set from an Antarctic fur seal (Arctocephalus gazella Peters, 1875). The Root Mean Square Error of the corrected data set, relative to the “perfect” data set, was nearly identical to the magnitude of noise introduced into the latter. The method, thus, provides a flexible, reliable, and efficient mechanism to perform zero offset correction for analyses of diving behaviour. We illustrate applications of the method to data sets from four species with large differences in diving behaviour, measured using different sampling protocols and instrument characteristics. |
format |
Text |
author |
Luque, Sebastián P. Fried, Roland |
author_facet |
Luque, Sebastián P. Fried, Roland |
author_sort |
Luque, Sebastián P. |
title |
Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
title_short |
Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
title_full |
Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
title_fullStr |
Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
title_full_unstemmed |
Recursive Filtering for Zero Offset Correction of Diving Depth Time Series with GNU R Package diveMove |
title_sort |
recursive filtering for zero offset correction of diving depth time series with gnu r package divemove |
publisher |
Public Library of Science |
publishDate |
2011 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030565 http://www.ncbi.nlm.nih.gov/pubmed/21297976 https://doi.org/10.1371/journal.pone.0015850 |
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Antarctic |
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Antarctic |
genre |
Antarc* Antarctic Antarctic Fur Seal Arctocephalus gazella |
genre_facet |
Antarc* Antarctic Antarctic Fur Seal Arctocephalus gazella |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030565 http://www.ncbi.nlm.nih.gov/pubmed/21297976 http://dx.doi.org/10.1371/journal.pone.0015850 |
op_rights |
Luque, Fried. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
op_rightsnorm |
CC-BY |
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https://doi.org/10.1371/journal.pone.0015850 |
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PLoS ONE |
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e15850 |
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