Department of Biological Sciences, University of Manitoba, Winnipeg, Canada.
PLoS One. 2011 Jan 28;6(1):e15850. doi: 10.1371/journal.pone.0015850.
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.
为了消除由于压力传感器精度随时间变化而产生的误差，需要对时间深度记录仪测量的潜水深度进行零位偏移修正。目前，该过程所使用的方法属于专有软件领域，研究人员无法对其进行充分详细的研究，因此对数据的修改方式几乎没有控制能力。GNU R 包 diveMove 在自由软件领域实现了一种程序，该程序包括使用移动分位数对输入时间序列进行递归平滑和滤波。本文通过使用“完美”数据集来描述、演示和评估所提出的方法，然后对其进行了损坏，以提供给所提出的程序输入。通过将校正后的时间序列与原始、未损坏的南极软毛海豹（Arctocephalus gazella Peters，1875）数据集进行比较，评估了该方法。校正后数据集的均方根误差与“完美”数据集的均方根误差几乎相同，后者的噪声大小也几乎相同。因此，该方法为潜水行为分析提供了一种灵活、可靠和高效的零位偏移修正机制。我们展示了该方法在使用不同采样协议和仪器特性测量的四种潜水行为差异较大的物种数据集上的应用。
