phase.partnered {synchrony}

Phase partnered time series

Description

Create two time series with specific autocorrelation \gamma, cross-correlation \rho, 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\le \rho \le 1). Default is 1.

gamma

autocorrelation of each time series. Gamma (\gamma) describes the relationship between frequency f and power P: P(f)=1/f^\gamma. If -2\le \gamma \le 0: blue noise and 0\le \gamma \le 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)