| Name : R-RolWinMulCor
| |
| Version : 1.2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.5.4
| Date : 2024-06-14 11:34:28
|
| Group : Development/Libraries/Other
| Source RPM : R-RolWinMulCor-1.2.0-lp153.5.4.src.rpm
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| Size : 0.12 MB
| |
| Packager : (none)
| |
| Summary : Subroutines to Estimate Rolling Window Multiple Correlation
|
Description :
Rolling Window Multiple Correlation (\'RolWinMulCor\') estimates the
rolling (running) window correlation for the bi- and multi-variate
cases between regular (sampled on identical time points) time series,
with especial emphasis to ecological data although this can be applied
to other kinds of data sets. \'RolWinMulCor\' is based on the concept of
rolling, running or sliding window and is useful to evaluate the
evolution of correlation through time and time-scales. \'RolWinMulCor\'
contains six functions. The first two focus on the bi-variate case: (1)
rolwincor_1win() and (2) rolwincor_heatmap(), which estimate the
correlation coefficients and the their respective 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. That is, the multi-variate case estimates
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 since these
contains several parameters to control the estimation of correlation
and the features of the plot output, e.g. to remove the (linear) trend
contained in the time series under analysis, to choose different
p-value correction methods (which are used to address the multiple
comparison problem) or to personalise the plot outputs. The
\'RolWinMulCor\' package also provides examples with synthetic and
real-life ecological time series to exemplify its use. Methods derived
from H. Abdi. (2007)
< https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>, R.
Telford (2013) < https://quantpalaeo.wordpress.com/2013/01/04/, J. M.
Polanco-Martinez (2019) < doi:10.1007/s11071-019-04974-y>, and J. M.
Polanco-Martinez (2020) < doi:10.1016/j.ecoinf.2020.101163>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
