Just love it when R2 comes out negative

[#1]

Hi all,

Does anyone know what JMP does in the bivariate fit platform when calculating the “fit measured on original scale”? I just got a negative R². Does it estimate a model without the intercept?

I know the data is peculiar and when using the log-log transformation I have many missing observations but still, something must be going on....

Attached is the data table with a script to reproduce the results I got.

Thanks,

Ron

[#2]

Unfortunately their usefulness and comparability is limited IMHO. 

Additional note--if you want to make predictions on the original scale from a log linear model , then anti log is necessary. However, you would also need to apply an adjustment by adding s²/2, i.e., y* = exp[log(y*) + (s²/2)]. This blog post explains why: Econometrics Beat: Dave Giles' Blog: Forecasting From Log-Linear Regressions.

[#3]

Thank you very much, this is all relevant and interesting.

[#4]

in this case i would like to ask whether you know if JMP uses this correction (or any other) when asking predicted values from the fit model platform (after transforming on the fly)? what about the prediction profiler?

[#5]

Only in Bivariate/Fit Special in Fit Y by X will JMP back transform the predicted values to the original scale, but it doesn't make adjustment with the anti-log transformation as referenced in the above-mentioned literature.  When you use a transformed response with Fit Model or Fit Y by X (except Fit Special) the prediction is made on the transformed response, so does the Prediction Profiler.

[#6]

thanks for he clarification.