bin_cor {BINCOR}

Binned correlation

Description

The bin_cor function convert an irregular time series to a binned one and its parameters are described in the following lines.

Usage

bin_cor(ts1, ts2, FLAGTAU=3, ofilename)

Arguments

ts1, ts2

ts1 and ts2 are the unevenly spaced time series.

FLAGTAU

FLAGTAU defines the method used to estimate the persistence or memory of the unevenly spaced time series. Options (by default is 3):
If 1 then est_tau = tau_x + tau_y [Eq. 7.44, Mudelsee (2010, 2014)].
If 2 then est_tau = max(tau_x, tau_y) [Eq. 7.45, Mudelsee (2010, 2014)].
If 3 then est_tau = dist_x_y/ln(a_x_y_est) [Eq. 7.48, Mudelsee (2010, 2014).]

ofilename

The output filename (ASCII format) containing the binned time series.

Details

The bin_cor function convert an irregular times series to a binned time series and depends on the R dplR package to carry out this task. dplR (redfitTauest function) estimate the persistence contained in the irregular climate time series by means of the method of Mudelsee (2002).

Value

A list of 16 elements:

Binned_time_series

An object containing the binned time series.

Auto._cor._coef._ts1

The autocorrelation for the binned time series number 1.

Persistence_ts1

The persistence or memory for the binned time series number 1.

Auto._cor._coef._ts2

The autocorrelation for the binned time series number 2.

Persistence_ts2

The persistence or memory for the binned time series number 2.

bin width

The bin width.

Number_of_bins

The number of bins.

Average spacing

The mean value of the times for the binned time series.

VAR. ts1

Variance of ts1

VAR. bin ts1

Variance of the binned ts1.

VAR. ts2

Variance for ts2.

VAR. bin ts2

Variance of the binned ts2.

VAR. ts1 - VAR bints1

Variance of ts1 minus variance of the binned ts1.

VAR. ts2 - VAR bints2

Variance of ts2 minus variance of the binned ts2.

% of VAR. lost ts1

Percentage of variance lost for ts1.

% of VAR. lost ts2

Percentage of variance lost for ts2.

Note

Needs dplR (redfitTauest function) to estimate the persistence contained in the irregular time series by means of the method of Mudelsee (2002). Please, look at the code tauest_dplR.R in the directory R of our BINCOR package.

Author(s)

Josué M. Polanco-Martínez (a.k.a. jomopo).
BC3 - Basque Centre for Climate Change, Bilbao, SPAIN.
EPOC UMR CNRS 5805 - U. de Bordeaux, Pessac, FRANCE.
Web1: https://scholar.google.es/citations?user=8djLIhcAAAAJ&hl=en.
Web2: http://www.researchgate.net/profile/Josue_Polanco-Martinez.
Email: [e-mail]

References

Bunn, A., Korpela, M., Biondi, F., Campelo, F., Mérian, P., Qeadan, F., Zang, C., Buras, A., Cecile, J., Mudelsee, M., Schulz, M. (2015). Dendrochronology Program Library in R. R package version 1.6.3. URL https://CRAN.R-project.org/package=dplR.

Mudelsee, M. (2002). TAUEST: A computer program for estimating persistence in unevenly spaced weather/climate time series. Computers & Geosciences 28 (1), 69–72.
URL http://www.climate-risk-analysis.com/software/.

Mudelsee, M. (2010). Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Springer.

Mudelsee, M. (2014). Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, Second Edition. Springer.

Polanco-Martínez, J.M., Medina-Elizalde, M.A., Sánchez Goñi, M.F., M. Mudelsee. (2018). BINCOR: an R package to estimate the correlation between two unevenly spaced time series. Ms. under review (second round).

Examples

 #####################################################################
 #::  Figure 1 D (Polanco-Martínez et al. (2018), (mimeo)). 
 #####################################################################
 library("BINCOR") 

 #####################################################################
 #:: Loading the time series under analysis: example 1 (ENSO vs. NHSST) 
 #####################################################################
 data(ENSO) 
 data(NHSST)

 #####################################################################
 # Testing our bin_cor function
 #####################################################################
 bincor.tmp    <- bin_cor(ENSO.dat, NHSST.dat, FLAGTAU=3, "output_ENSO_NHSST.tmp")
 binnedts      <- bincor.tmp$Binned_time_series