phase.partnered: Phase partnered time series

In synchrony: Methods for Computing Spatial, Temporal, and Spatiotemporal Statistics

Description

Create two time series with specific autocorrelation γ, cross-correlation ρ, mean ts.mean, and standard deviation ts.sd using the phase partnered algorithm described by Vasseur (2007)

Usage

phase.partnered (n = 2000, rho = 1, gamma = 1, sigma = 0.1, mu = 0)

Arguments

n	number of time steps in time series. Default is 2000.
rho	cross-correlation between the two time series (-1≤ ρ ≤ 1). Default is 1.
gamma	autocorrelation of each time series. Gamma (γ) describes the relationship between frequency f and power P: P(f)=1/f^γ. If -2≤ γ ≤ 0: blue noise and 0≤ γ ≤ 2: red noise. Default is 1.
sigma	standard deviation of both time series. Default is 0.1.
mu	mean of both time series. Default is 0.

Value

Returns a named list containing the following:

rho	Cross-correlation of the time series
gamma	Autocorrelation of the time series
sigma	Standard deviation of the time series
mu	Mean of the time series
timeseries	n x 2 matrix containing the time series

Author(s)

Tarik C. Gouhier ([e-mail])

References

Gouhier, T. C., F. Guichard, and A. Gonzalez. 2010. Synchrony and stability of food webs in metacommunities. The American Naturalist 175:E16-E34.

Vasseur, D. A. 2007. Environmental colour intensifies the Moran effect when population dynamics are spatially heterogeneous. Oikos 116:1726-1736.

Examples

# Positively cross-correlated white noise
pos.corr=phase.partnered(n = 100, rho = 0.7, gamma = 0)
# Negatively cross-correlated white noise
neg.corr=phase.partnered(n = 100, rho = -1, gamma = 0)
par(mfrow=c(2,1))
matplot (pos.corr$timeseries, t="l", lty=1)
matplot (neg.corr$timeseries, t="l", lty=1)

Example output

synchrony 0.2.3 loaded.

synchrony documentation built on March 26, 2020, 7:14 p.m.