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RolWinMulCor: An R package for estimating rolling window multiple correlation in ecological time series
Ecological Informatics ( IF 5.1 ) Pub Date : 2020-10-07 , DOI: 10.1016/j.ecoinf.2020.101163
Josué M. Polanco-Martínez

RolWinMulCor estimates the rolling window correlation for bi- and multi-variate cases between regular time series, with particular emphasis on ecological data. It is based on the concept of rolling, running or sliding window correlation, being useful for evaluating the evolution and stability of correlation over time. RolWinMulCor contains six functions to estimate and to plot the correlation coefficients and their respective p-values. The first two focus on the bi-variate case: (1) rolwincor_1win and (2) rolwincor_heatmap, estimate the correlation coefficients and the p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win and (4) rolwinmulcor_heatmap, are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different (the multi-variate case estimate the adjusted coefficients of determination instead of the correlation coefficients). The last two functions: (5) plot_1win and (6) plot_heatmap, are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in RolWinMulCor are highly flexible, containing several parameters for controlling the estimation of correlation and the features of the plot output. The RolWinMulCor package also provides examples with synthetic and real-life ecological time series for illustrating its use.



中文翻译:

RolWinMulCor:R包,用于估算生态时间序列中的滚动窗口多重相关

RolWinMulCor估计常规时间序列之间的双变量和多变量案例的滚动窗口相关性,尤其着重于生态数据。它基于滚动,运行或滑动窗口相关性的概念,可用于评估相关性随时间的演变和稳定性。RolWinMulCor包含六个函数,用于估计和绘制相关系数及其各自的p值。前两个重点关注双变量情况:(1)rolwincor_1win和(2)rolwincor_heatmap,估计相关系数和p-值仅适用于一个窗口长度(时标),并分别考虑所有可能的窗口长度或一个窗口长度带。后两个函数:(3)rolwinmulcor_1win和(4)rolwinmulcor_heatmap,用于分析多变量情况,遵循双变量情况以可视方式显示结果,但是这两种方法在方法上有所不同(多变量情况估计调整后的确定系数,而不是相关系数。最后两个函数:(5)plot_1win和(6)plot_heatmap,用于以图形方式将上述四个函数的输出表示为简单图或热图。RolWinMulCor中包含的功能高度灵活,包含几个参数,用于控制相关性的估计和绘图输出的特征。所述RolWinMulCor包还提供了与合成的和现实生活中的生态时间序列的例子用于说明其使用。

更新日期:2020-10-11
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