Nothing
## The function is currently defined as
mat.jump <-function(dObj, inModern, envColumn, cutoff=0, calib=T)
{
#DECLARE EMPTY VECTORS FOR OUTPUT
outcorr=vector('numeric')
outavgvec=vector('numeric')
#
#CREATE DISSIMILARITY MATRIX
dimnames(dObj$sqdist)=NULL
dimnames(dObj$position)=NULL
sqmat=t(dObj$sqdist)
numcols=ncol(sqmat)
numrows=nrow(sqmat)
#
#CREATE MATRIX OF RECONSTRUCTED ENVIRONMENTAL VARIABLES
reconvec = inModern[,envColumn]
reconmat = matrix(data=reconvec[t(dObj$position)],nrow=numrows,ncol=numcols)
if(calib)
{
numeratorStartCol = 3
numeratorEndCol = numcols
denominatorStartCol = 2
demonimatorEndCol = numcols-1
}
else
{
numeratorStartCol = 2
denominatorStartCol = 1
numeratorEndCol = numcols-1
demonimatorEndCol = numcols
}
#
#MAKE THE JUMP MATRIX
jumpmat = ((sqmat[,numeratorStartCol: numeratorEndCol]/sqmat[,denominatorStartCol: demonimatorEndCol]) - 1) * 100
jumpmat[is.infinite(jumpmat)]=0
#
#IF CUTOFF>0 THEN REMOVE ALL RECONSTRUCTED VALUES > THE DISSIMILARITY THREASHOLD
if (cutoff > 0) {
reconmat[sqmat>=cutoff]=NA
}
numrows=nrow(jumpmat)
#
#ITERATE OVER JUMPS FROM ZERO TO ONE-HUNDRED PERCENT
for(i in 0:100){
tfmat=jumpmat>i
for(k in 1:numrows){
jumppoint = match(TRUE,tfmat[k,])
if(!is.na(jumppoint)) {
currow=c(reconmat[k,denominatorStartCol],reconmat[k,numeratorStartCol:numcols][1:jumppoint])
currow=currow[!is.na(currow)]
outavgvec=c(outavgvec,sum(currow)/length(currow))
}
else
{
currow=reconmat[k,denominatorStartCol:numcols]
currow=currow[!is.na(currow)]
outavgvec=c(outavgvec,sum(currow)/length(currow))
}
}
tcor = cor(reconvec, outavgvec,"pairwise.complete.obs")^2
outcorr = c(outcorr, tcor)
outavgvec=vector('numeric')
}
#
#OUTPUT THE CORRELATIONS AND JUMP VALUES FOR PLOTTING
tseq=0:100
maxalpha=max(outcorr)
list(alpha = tseq[which(outcorr==maxalpha)], alphacor=cbind(alpha=0:100,correlation=outcorr))
}
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