Going along with what Phil is saying, perhaps you are taking a log of a very
very small number, resulting in NaNs. Or, alternatively, columns have a very
small norm, so when you normalize, it is effectively a division by zero?
On Tue, Aug 2, 2011 at 2:57 PM, Phil Steitz wrote:
> On 8/2/11 12:24 PM, dan wrote:
> > 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.
>
> Are you sure there are no NaNs in your input data?
>
> Phil
> >
> > Thank you,
> > Dan
> >
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