thegeologician wrote: > > A plot of the actual temperature during a year (or thousands of years, > as people in palaeoclimate-studies are rather used to) is just so much > more intuitive, than some correlation-coefficients or such. I know I'm > largely speaking to statisticians in this forum, but in Earth Sciences, > most people aren't... I see the use of correlation coefficients and > -plots in proofing that an apparent correlation is "real", but the first > question upon presenting any statistic analysis is always "What does the > DATA look like?". >
Agreed - the data itself is much easier to get to grips with than correlation coefficients. thegeologician wrote: > > Of course, these plots could be plotted separately with a common x-axis, > it's just a matter of saving space and of being used to that kind of > graph. I can't imagine anyone being falsely lead to a thought like "oh > gosh, the temperature is much higher/bigger/more than the > precipitation!" - that makes no sense. I do see the point in graphs > where values are plotted together, whose possible interaction with each > other might lead to wrong conclusions. Then, it might not be obvious > that one is drawing a senseless conclusion. > I think in the temperature/ precipitation case, whether to draw multiple y-axes or not is a fairly minor decision. The reader would have to be pretty dumb to assume that temperatures and precipitations can be compared. The point is that it can appear that way - so the reader has to engage their brain to tell themselves "ignore the obvious comparisons between the lines that I perceive". This is clearly not a desirable trait in a graph. I've concocted an example to show that it's possible to mislead unwary readers by changing the y-axes scale. This uses the nottem temperature dataset built into R, and some made-up precipitation data. #Generate some precipitation data precipitation = 30+runif(240,5,10)*sin(seq(pi/6,40*pi,pi/6)+pi/4)+rnorm(240,0,3) pts <- ts(precipitation, start=1920, frequency=12) #First plot, correlation is apparent plot(nottem) par(new=TRUE) plot(pts, axes=FALSE, col="blue", ylab="") Axis(side=4) #Second plot, scale changing makes it appear that precipitation does not vary with temperature. plot(nottem) par(new=TRUE) plot(pts, axes=FALSE, col="blue", ylab="", ylim=c(0,10000)) Axis(side=4) I'm willing to concede that the attempt at misleading the audience is pretty artificial, and not very subtle. A more dangerous case would be the opposite situation - making a correlation become visible on a plot where none really exists, by fiddling with axes tranformations (you could use a log scale on the second y-axis, or any other transformation you wished). I suspect that the popularity of multiple y-axes arose from a greater need to save space in paper-based journals, but in the age of electronic documents, is space saving really that important? ----- Regards, Richie. Mathematical Sciences Unit HSL -- View this message in context: http://www.nabble.com/Thinking-about-using-two-y-scales-on-your-plot--tp16290293p16537217.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
