Nothing
wave.local.multiple.regression <- #3.0.0
function(xx, M, window="gauss", p=.975, ymaxr=NULL) {
df.swap.list <- function(xx){
yy <- list()
for (i in seq_along(xx[[1]])){
yy[[i]] <- as.data.frame( lapply(xx,'[[',i) )
names(yy[[i]]) <- names(xx)
attr(yy[[i]],"wave") <- attr(xx[[1]],"class")
}
names(yy) <- names(xx[[1]])
return(yy)
}
d <- length(xx) #number of series
dd <- d*(d-1)/2 #number of correlations
l <- length(xx[[1]]) #number of scales J+1 (wavelet coefficients at levels 1 to J plus the scaling coeffs at level J+1)
# N <- length(xx[[1]][[1]]) #number of observations
wav <- attr(xx[[1]],"wavelet")
if(substr(wav,1,1)=="h") {L<-2
} else if(substr(wav,1,1)=="d") {L<-as.numeric(substring(wav,2))
} else if(substr(wav,1,1)=="l") {L<-as.numeric(substring(wav,3))} else L<-8
###IMPORTANT: must shift wave coeffs to left by L_i/2 at each level to be in phase with time series!!! (Getal, p.145)
for(j in 1:d) {
xx[[j]] <- phase.shift(xx[[j]], wav)
}
cor <- reg <- YmaxR <- list()
x <- df.swap.list(xx)
cat("\nlev:")
for(i in 1:l) { cat(sprintf("%s",i))
out <- local.multiple.regression(x[[i]], M, window=window, p=p, ymaxr=ymaxr)
cor[[i]] <- out$cor
reg[[i]] <- out$reg
YmaxR[[i]]<- out$YmaxR
}
YmaxR <- as.data.frame(YmaxR)
names(cor) <- names(reg) <- names(YmaxR) <- names(xx[[1]])
Lst <- list(cor=cor,reg=reg,YmaxR=YmaxR,data=xx)
return(Lst)
}
#--------------------------------------------------------------
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