> > > > > What exactly are trying to fit because it is rather bad practice to fit > a model to some summarized data as you lose the uncertainty in the > original data? > If you define your boxes, you can loop through directly on each box and > even fit the equation: > > model=mu +beta1*obs > > The extension is to fit the larger equation: > model=mu + boxes + beta1*obs + beta2*obs*boxes > > where your boxes is a indicator or dummy variable for each box. > Since you are only interested in the box by model term, you probably can > use this type of model > model=mu + boxes + beta2*obs*boxes > > However, these models assume that the residual variance is identical for > all boxes. (That is solved by a mixed model approach.) > > Bruce
Bruce, I am trying to determine spatially based linear corrections for surface winds in order to force a wave model. The basic idea is, use satellite observations from sattelites to determine the errors and the wind, and reduce them by applying a linear correction prior to forcing the wave model. I am not sure I understand what you are saying, I am possibly trying to do what you are describing. i.e. for each box, gather observations, determine a linear correction, and apply it to the model model = a*x + b Does that make sense? Cheers Tom > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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