I have been using the OLSMultipleLinearRegression class successfully for a while now, but I am having trouble in my current application.
The code is very simple, and looks like this: OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression(); regression.setNoIntercept(true); regression.newSampleData(ys, z_bars); double [] new_eta = regression.estimateRegressionParameters(); When I run this code with my current data, all of the regression coefficients come back as NaNs. In the input data, the z_bars are vectors that have been normalized to sum to 1, and the ys are the logs of the "true" response variables (I am trying to reproduce the results from a research paper, in which it was claimed that logging the response variables made them more normally distributed, resulting in a better fit). Is there something wrong with my setup? It seems like, even if the logged data is not very linear, that it should still be possible to obtain some OLS fit, even if it is a poor one. Any help would be appreciated. Thank you, Dan --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
