Methods for Computing Spatial, Temporal, and Spatiotemporal Statistics
Methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) doi:10.1111/2041-210X.12188. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices. package
| Package: | synchrony |
| Type: | Package |
| Version: | 0.3.8 |
| Date: | 2019-12-05 |
| License: | GPL (>=2) |
| URL: | https://github.com/tgouhier/synchrony |
| LazyLoad: | yes |
Tarik C. Gouhier (tarik.gouhier@gmail.com)
Maintainer: Tarik C. Gouhier (tarik.gouhier@gmail.com)
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# Compute phase synchrony t1=runif(100) t2=runif(100) sync=phase.sync(t1, t2) # Distribution of phase difference hist(sync$deltaphase$mod_phase_diff_2pi) # Compute concordant peaks p=peaks(t1, t2, nrands=100) # Find proportion of time steps where both time series peak together p$peaks # Plot (null) distribution of proportion of time steps where both time # series peak together hist(p$rand) # p-value of observed value p$pval # Compute Kendall's W data(bird.traits) (w=kendall.w(bird.traits)) # Community matrix for 20 species undergoing random fluctuations comm.rand=matrix(runif(100), nrow=5, ncol=20) community.sync(comm.rand, nrands=10) # Community matrix for 20 species undergoing synchronized fluctuations comm.corr=matrix(rep(comm.rand[,1], 20), nrow=5, ncol=20) community.sync(comm.corr, nrands=10)