wmf: Computes the wavelet mean field from a matrix of...
In wsyn: Wavelet Approaches to Studies of Synchrony in Ecology and Other Fields

Description Usage Arguments Value Author(s) References See Also Examples

Computes the wavelet mean field from a matrix of spatiotemporal data. Also the creator function for the wmf class. The wmf class inherits from the tts class, which inherits from the list class.

1	wmf(dat, times, scale.min = 2, scale.max.input = NULL, sigma = 1.05, f0 = 1)

`dat`	A locations (rows) x time (columns) matrix
`times`	A vector of time step values (e.g., years), spacing 1
`scale.min`	The smallest scale of fluctuation that will be examined. At least 2.
`scale.max.input`	The largest scale of fluctuation that will be examined. Note that if this is set too high relative to the length of the timeseries it will be truncated.
`sigma`	The ratio of each time scale examined relative to the next timescale. Greater than 1.
`f0`	The ratio of the period of fluctuation to the width of the envelope

wmf returns an object of class wmf. Slots are:

`values`	A matrix of complex numbers containing the wavelet mean field, of dimensions `length(times)` by the number of timescales. Entries not considered reliable (longer timescales, near the edges of the time span) are set to NA.
`times`	The time steps specified (e.g., years)
`timescales`	The timescales (1/frequency) computed for the wavelet transforms
`dat`	The data matrix (locations by time) from which the wmf was computed
`wtopt`	The inputted wavelet transform options scale.min, scale.max.input, sigma, f0 in a list

Jonathan Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

wmf_methods, tts, wpmf, plotmag, browseVignettes("wsyn")

times<-1:30 #generate time steps
#generate fake count data for 20 locations
dat<-matrix(rpois(20*length(times),20),nrow=20,ncol=length(times)) 
dat<-cleandat(dat=dat,times=times,clev=2)$cdat #detrend and demean
wmf<-wmf(dat,times)