predsync: Predicted synchrony of a wavelet linear model
In wsyn: Wavelet Approaches to Studies of Synchrony in Ecology and Other Fields

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

Predicted synchrony of a wlm object. This is described in the first paragraph of Appendix S15 of Sheppard et al (2019).

predsync(wlmobj)

## S3 method for class 'wlm'
predsync(wlmobj)

wlmobj

A wlm object

predsync returns a tts object. Plotting the magnitude (see plotmag) displays a picture of predicted synchrony versus time and timescale that is comparable with the wavelet mean field (see wmf) of the response variable of the model. Calling the power function on that tts object should give the same results as one of the columns of output of syncexpl. Only norm="powall" implemented so far.

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

wlm, tts, plotmag, wmf, power, syncexpl, browseVignettes("wsyn")

times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]<-ts1+ts2+rnorm(length(times),mean=0,sd=.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=1),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_i<-matrix(rnorm(11*length(times)),11,length(times)) #the irrelevant
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
artsig_i<-cleandat(artsig_i,times,1)$cdat
dat<-list(driven=artsig_y,driver=artsig_x,irrelevant=artsig_i)
resp<-1
pred<-2:3
norm<-"powall"
wlmobj<-wlm(dat,times,resp,pred,norm)

res<-predsync(wlmobj)