syncexpl {wsyn}

Amount of synchrony explained, and related quantities

Description

Gives amount of synchrony explained by a wavelet linear model, as a function of timescale, and related quantities (see details)

Usage

syncexpl(object)

## S3 method for class 'wlm'
syncexpl(object)

Arguments

object

A wlm object

Details

This function only works for norm="powall" at present. See Sheppard et al (2018) for details of the meaning and computation of the columns.

Value

syncexpl returns a data frame with columns for timescales, sync (the time-averaged square magnitude of the wavelet mean field of the response transforms), syncexpl (synchrony explained by the model predictors), columns named for each predictor (synchrony explained by that predictor), interactions (synchrony explained by all interaction effects), columns named for each pair of predictors (synchrony explained by individual pairwise interactions). There are also columns for crossterms and resids (residuals). The cross terms must be small for a given timescale band for the other results to be meaningful. All columns are functions of timescales.

Author(s)

Thomas Anderson, [e-mail], Jon Walter, [e-mail]; Lawrence Sheppard, [e-mail]; Daniel Reuman, [e-mail]

References

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

See Also

wlm, predsync, wlmtest, browseVignettes("wsyn")

Examples

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=1.5)
}
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=3),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<-syncexpl(wlmobj)