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plot_wave.local.multiple.cross.regression <- #3.1.0
function(Lst, lmax, nsig=2, xaxt="s", pdf.write=NULL){
##Producing cross-regression plot
# requireNamespace(magrittr)
if (xaxt[1]!="s"){
at <- xaxt[[1]]
label <- xaxt[[2]]
xaxt <- "n"
}
cor <- Lst$cor
reg <- Lst$reg
YmaxR <- Lst$YmaxR
J <- length(Lst$YmaxR)-1
N <- length(Lst$data[[1]][[1]])
xxnames <- names(Lst$data)
level.lab <- c(paste("level",1:J),paste("s",J))
lagnames <- c(paste("Lead",lmax:1),paste("Lag",0:lmax))
reg.vars <- t(matrix(xxnames,length(Lst$data),N))
# requireNamespace(RColorBrewer)
mycolors <- RColorBrewer::brewer.pal(n=8, name="Dark2")
reg.vars <- t(matrix(xxnames,length(Lst$data),N)) #xy.mulcor$xy.mulreg$vars[1:J,]
for(j in 1:(J+1)) {
vj <- lapply(reg[[j]],function(x){x[,,-1]}) #exclude constant
vj$rord <- vj$rord-1
vj$rord[vj$rord==0] <-
vj$rval[vj$rord==0] <- #exclude dependent variable
vj$rstd[vj$rord==0] <-
vj$rlow[vj$rord==0] <-
vj$rupp[vj$rord==0] <-
vj$rpva[vj$rord==0] <- NA
vj.sel <- vj$rord<=nsig & vj$rpva<=0.05 #select firs nsig 5%signif. predictors
vj$rval.sig <- vj$rval*vj.sel
vj$rlow.sig <- vj$rlow*vj.sel
vj$rupp.sig <- vj$rupp*vj.sel
# vj$rpva.sig <- vj$rpva*vj.sel
vj$rord.sig <- vj$rord*vj.sel
vj$rval.sig[vj$rval.sig==0] <-
vj$rlow.sig[vj$rlow.sig==0] <-
vj$rupp.sig[vj$rupp.sig==0] <-
vj$rord.sig[vj$rord.sig==0] <- NA
if (!is.null(pdf.write))
cairo_pdf(paste0("plot_",pdf.write,"_WLMCR_",level.lab[j],".pdf"), width=8.27,height=11.69)
mark <- paste0("\u00A9jfm-wavemulcor3.1.0_",Sys.time()," ")
par(mfcol=c(lmax+1,2), las=1, pty="m", mar=c(2,3,1,0)+.1, oma=c(1.2,1.2,1.2,0))
ymin <- min(vj$rval,na.rm=TRUE) #head(unique(sort(vj$rval)))[2]
ymax <- max(vj$rval,na.rm=TRUE)
for(i in c(-lmax:0,lmax:1)+lmax+1) {
matplot(1:N,vj$rval[,i,], ylim=c(ymin-0.1,ymax+0.1),
type="n", xaxt=xaxt, lty=3, col=8,
xlab="", ylab="", main=lagnames[i])
# shade <- 1.96*vj$rstd %>% apply(1,max)
for (k in dim(vj$rstd)[3]:1){
shade <- 1.96*vj$rstd[,i,k]
polygon(c(1:N,rev(1:N)),c(-shade,rev(shade)), col=gray(0.8,alpha=0.2), border=NA)
}
matlines(1:N,vj$rval[,i,], lty=1, col=8)
if(abs(ymax-ymin)<3) lo<-2 else lo<-4
abline(h=seq(floor(ymin),ceiling(ymax),length.out=lo),col=8)
matlines(1:N, vj$rval.sig[,i,], lty=1, lwd=2, col=mycolors)
matlines(1:N, vj$rlow.sig[,i,], lty=2, col=mycolors)
matlines(1:N, vj$rupp.sig[,i,], lty=2, col=mycolors)
mtext(mark, side=1, line=-1, adj=1, col=rgb(0,0,0,.1),cex=.2)
col <- (vj$rord[,i,]<=3)*1 +(vj$rord[,i,]>3)*8
# xvar <- seq(1,N,M)
xvar <- t(t(which(abs(diff(sign(diff(vj$rval[,i,]))))==2,arr.ind=TRUE))+c(1,0))
text(xvar, vj$rval[xvar,i,2], labels=reg.vars[xvar,2], col=col,cex=.3)
text(xvar, vj$rval[xvar,i,], labels=vj$rord[xvar,i,],pos=1, col=col,cex=.3)
if (length(unique(YmaxR[[j]]))==1) {
mtext(xxnames[YmaxR[[j]]][1], at=1, side=3, line=-1, cex=.5)
} else {
xvaru <- t(t(which(diff(sign(diff(as.matrix(vj$rval[,i,]))))==-2))+1)
xvarl <- t(t(which(diff(sign(diff(as.matrix(vj$rval[,i,]))))==2))+1)
# xvar <- t(t(which(abs(diff(sign(diff(as.matrix(vj$rval[,i])))))==2))+1)
# xvar2 <- xvar[-length(xvar)]+diff(xvar)/2
mtext(xxnames[YmaxR[[j]]][xvaru], at=xvaru, side=3, line=-1, cex=.3)
# mtext(xxnames[YmaxR[[j]]][xvar2], at=xvar2, side=3, line=-1, cex=.3)
mtext(xxnames[YmaxR[[j]]][xvarl], at=xvarl, side=3, line=-1, cex=.3)
}
if (xaxt!="s") axis(side=1, at=at, labels=label)
}
par(las=0)
mtext('time', side=1, outer=TRUE, adj=0.5)
mtext('Wavelet Local Multiple Cross-Regression', side=2, outer=TRUE, adj=0.5)
mtext(level.lab[j], side=3, outer=TRUE, adj=0.5)
if (!is.null(pdf.write)) dev.off()
}
return()
}
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