peaks {synchrony}

Find the proportion of local minima/maxima common to both time series and compute its significance via Monte Carlo randomizations

Description

Find the proportion of local minima/maxima common to both time series and compute its significance via Monte Carlo randomizations

Usage

peaks (t1, t2, nrands = 0, type = 1, quiet = FALSE)

Arguments

t1

time series 1 in matrix format (n rows x 2 columns). The first column should contain the time steps and the second column should contain the values. If t1 is a column vector instead of a matrix, then it will be automatically converted to a matrix with column 1 corresponding to a time index ranging from 1 to the length of t1

t2

time series 2 in matrix format (n rows x 2 columns). The first column should contain the time steps and the second column should contain the values. If t2 is a column vector instead of a matrix, then it will be automatically converted to matrix with column 1 corresponding to a time index ranging from 1 to the length of t2.

nrands

number of randomizations. Default is 0.

type

Randomization method. The type=1 method randomly shuffles each time series, thus destroying both the autocorrelation structure of each time series and their cross-correlation. The type=2 method shifts each time series by a random amount, thus preserving the autocorrelation structure but destroying the cross-correlation between the time series (Purves and Law 2002). Default is type=1

quiet

Suppress progress bar when set to TRUE. Default is FALSE

Value

Returns a named list containing:

pval

p-value computed by randomly shuffling both time series nrands times

rands

proportion of local minima/maxima common to both time series for each randomization

obs

proportion of local minima/maxima common to both time series in the observed dataset

index

indices of local minima/maxima common to both time series in the observed dataset

Author(s)

Tarik C. Gouhier ([e-mail])

References

Buonaccorsi, J. P., J. S. Elkinton, S. R. Evans, and A. M. Liebhold. 2001. Measuring and testing for spatial synchrony. Ecology 82:1668-1679.

Purves, D. W., and R. Law. 2002. Fine-scale spatial structure in a grassland community: quantifying the plant's eye view. Journal of Ecology 90:121-129.

Examples

t1=runif(100)
t2=runif(100)
(p=peaks(t1, t2))