synthetic_data

The data set <code><a rd-options="" href="/link/synthetic_data?package=RolWinMulCor&version=0.4.0" data-mini-rdoc="RolWinMulCor::synthetic_data">synthetic_data</a></code> contains five columns, the first one is the time and the next four columns are sinusoidal time series that contains two periodical signals (at 11 and 51, with a phase of zero and amplitude of 1 --please note that these quantities are dimensionless) 'contaminated' by Gaussian noise (with mean of 0 and standard deviation of 0.25) for the intervals 1-100 (signal at 11) and 200-400 (signal at 51) and Gaussian noise (with mean of 0 and standard deviation of 1) otherwise.

dataset1

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 environmental data (although this can be applied to other kinds of data sets). 'RolWinMulCor' is based on the concept of rolling or running window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains four functions: (1) the first two functions are focus on the bi-variate case, one of them produces a simple plot of correlation coefficients and p-values (<=0.05) for only one window-length (time-scale), and the other function produces a heat map for the statistically significant (p-values <=0.05) correlation coefficients taking into account all the possible window-lengths (which are determined by the number of elements of the time series under analysis) or for a band of window-lengths; (2) the second two functions are designed to analyse the multi-variate case and follow the bi-variate case to display visually the results although these two methods are different. The four functions contained in 'RolWinMulCor' are highly flexible since this contains a great number of 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 output (e.g. this can be displayed in the screen or can be saved as PNG, JPEG, EPS or PDF formats). The 'RolWinMulCor' package also provides examples with synthetic and real environmental time series to exemplify its use. Methods derived from H. Abdi. (2007) <https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>, J. M. Polanco-Martinez (2019) <doi:10.1007/s11071-019-04974-y>, and R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/running-correlations-running-into-problems/>.

Josue Polanco-Martinez

RolWinMulCor

Subroutines to Estimate Rolling Window Multiple Correlation

Josue M. Polanco-Martinez

synthetic_data function

One file in ASCII format and columns are separated by spaces.

Format

The data set <code><a rd-options='' href='synthetic_data'>synthetic_data</a></code> contains five columns, the first one is the time and the next four columns are sinusoidal time series that contains two periodical signals (at 11 and 51, with a phase of zero and amplitude of 1 --please note that these quantities are dimensionless) 'contaminated' by Gaussian noise (with mean of 0 and standard deviation of 0.25) for the intervals 1-100 (signal at 11) and 200-400 (signal at 51) and Gaussian noise (with mean of 0 and standard deviation of 1) otherwise.

synthetic_data: Synthetic data set to test the functions of RolWinMulCor.

Description

Usage

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