peaks: 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)

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

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

Author

Tarik C. Gouhier (tarik.gouhier@gmail.com)

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

Run this code

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

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