syncexpl | R Documentation |
Gives amount of synchrony explained by a wavelet linear model, as a function of timescale, and related quantities (see details)
syncexpl(object) ## S3 method for class 'wlm' syncexpl(object)
object |
A |
This function only works for norm="powall"
at present. See
Sheppard et al (2018) for details of the meaning and computation of the
columns.
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.
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
, predsync
, wlmtest
,
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=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)
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