I have 2 data sets of equal length. I've done a LinearModelFit of them which gives me:
d = Transpose[{d1, d2}];
lm = LinearModelFit[d, x, x]
FittedModel[0.000245082 + 0.000514887x]
This reflects a relationship of y axis values that vary proportionately to time. I actually suspect that visually seeing this relationship having the data scaled to it may turn up some interesting patterns.
I have a notion of now scaling a plot of the original data proportionate to the FittedModel. Not certain how to go about this and I may not have even articulated the question clearly. A clue, some fields refer to this as "square" scaling, but I haven't seen the term used or described widely.
Maybe what doesn't seem to work to do this might help clarify things.
I do know that AspectRatio doesn't seem to get me to what I have in mind. It scales the entire plot including the axes labels and titles. I want to scale the plot area (within the plot frame) itself.
It does seem that I might create a couple of plots, say one without labels with the AspectRatio then another with just the axes labels and combine them with Show or Grid, but this seems cumbersome.
I travel over the holiday weekend, so I may not have the connectivity to respond with additional information until late Monday. I will if I can.
In the meantime, any thoughts appreciated.
Late thought: Would ScalingFunctions help to do this?
