Hi John, very appreciate for your detailed explain and pointing out my misunderstanding of R^2 in regression. Your suggestions really helps me, thanks you very much.
Chi-Fu

I'm sorry, but you seem to think that even for a normal regression, that R^2 offers some "objective" measure of the regression, that it can offer a pass/fail rule for the regression. This is a classic misimpression I think.

There is no simple measure that I know of (including R^2) that will reliably tell you that a regression model is adequate for a general set of data. This will still be true for the TLS case. Nothing changes in that respect. That applies to a general model, and for a completely arbitrary set of data.

Nothing stops you from constructing some simple measure however, based on a subset of your many sets of data. For example, extract some subset of size N of those sets at random, and perform a careful analysis of the resulting models. Look at the residual plots for every one of those models. Compute various measures of fit (I'll offer a couple of example measures later.)

Choose a large enough subset of models such that there will be at least a few of the regression models that would be deemed inadequate. Now find a decision point for some parameter P, such that it reasonably predicts when the regression would have failed, in YOUR opinion, based on that subset of analyses.

Essentially I m suggesting that you choose some parameter P and a set point such that it offers a way to discriminate between "good" and "bad" models.

I can't tell you in advance that any given R^2 analogue, if it is greater than perhaps 0.975 (or any magic number) that it will offer the discriminant you desire. But the point is that you can choose some parameter, and essential calibrate things so that it might indicate when there MIGHT be a problem. Even so, any single number can be fooled by lack of fit masquerading as noise, but you seem insistent on having a magic measure.

So two such measures that would be appropriate for a TLS model might be:

1. Root Mean Square Total Error.

2. One minus the ratio of singular values. (Or maybe squared singular values. It is early in the morning for me, and my brain is a bit foggy now about which would be the proper analogue.)

So the first measure is simply an analogue of mean square error, so you compute the squared normal distance to the line for each point. Compute the sqrt of the mean of all of those squared distances, so RMSTE.

The second measure I listed is based on the idea of how the SVD works to generate a TLS model itself. You can think of the singular values as measuring the size of the point cloud in each of two directions, along the regression line, and normal to that line. So the ratio of the smaller singular value divided by the larger one will yield a number between 0 and 1. Subtract that from 1, and we get a number that will mimic R^2. A visual way of thinking about it is as if we are measuring the ratio of the axis lengths of an ellipse around your data. If that ellipse around your data formed a perfect circle, then this measure would return 0. If the data fell on a perfect straight line with no error, then this measure would return 1.

I'm now done consulting in the comments, with my apologies to the authors of this submission.

Hi John,
Thanks for letting me know to trust my eye more than a number. My problem is I have more than ten thousands curves and I want to rule out some data that fit really bad before I start to select data manually. So if there is any objective value that represent the goodness of fitting in TLS, it would be useful to me.
Best,
Chi-Fu