R/plot_distance_graph.R

In aqp: Algorithms for Quantitative Pedology

# TODO: better colors
# take a look at between-profile D
# for the range of indexes
plot_distance_graph <- function(D, idx=1:dim(as.matrix((D)))[1])
	{
	# convert to full matrix representation
	# careful with very large D
	d.mat <- as.matrix(D)
	
	# mask out NA
	d.mat[d.mat == 0] <- NA
	
	# get number of individuals
	n <- dim(d.mat)[1]
	
	# get the original labels
	labs <- attr(D, 'Labels')
	# abbreviate them
	labs <- abbreviate(labs, minlength=3)
	
	# get the range in D and rescale to {0,1}
	d.range <- range(d.mat, na.rm=TRUE)
	d.mat <- d.mat / d.range[2]
	
	# empty plot region
	plot(1,1, type='n', xlim=c(1, n), ylim=c((d.range[1]/d.range[2])-0.1, 1), 
	axes=FALSE, xlab='', ylab='Relative Distance')
	
	# add embellishments
	box()
	axis(2, las=2)
	axis(1, at=1:n, labels=labs, las=2)
	grid(col=grey(0.25))
	
	# add lines, labels, and symbols
	for(i in idx)
		{
		# which individual is the most similar (min distance)
		min.idx <- which.min(d.mat[, i])
		# plot a line for each individual
		lines(1:n, d.mat[, i], type='b', pch=NA, col=(i %% 5) + 1, lty=(i %% 3) + 1)
		# label each line at nodes, except for the node of max similarity (min distance)
		text(c(1:n)[-min.idx], d.mat[-min.idx, i], label=labs[i], font=1, cex=0.75, col=(i %% 5) + 1)
		
		# mark the pair with min distance
		points(min.idx, d.mat[min.idx, i], cex=2, pch='*', col=(i %% 5) + 1)
		text(min.idx, d.mat[min.idx, i], label=labs[i], cex=0.78, font=2, pos=1, col=(i %% 5) + 1)
		}
	}