Paul A Bristow wrote:
| 3) This "normalizes" the interface for the calls to the distribution | functions - every call for "P" has exactly one argument, and | not two or three or four depending on the distribution in use.
How would you envisage this working with Fisher, for example which has degrees of freedom 1 and 2, and a variance ratio.
Is this a 1D or 2D or 3D?
Its inversion will return df1 (given df2 and F and Probability) or df2 (given df1, F and Prob) or F (given Df1 and df2 and Prob)
WOuld you like to flesh out how you suggest handling all these?
I can't say that I would :-) This is going somewhat beyond my area of expertise ... it may well be the case that my model is just wrong, but happens to work within the subset of probability and statistics that I've had cause to use. I'd be perfectly willing to hear that criticism. Some questions from the clueless to help me think through this: Is it really the case that df1 in the Fisher distribution is any different than, say, the mean or deviation in the Normal? For example, I wouldn't talk about inverting the normal and extracting the mean. Or would I? That's not something I've ever thought about. In my work, I wouldn't try to extract the number of degrees of freedom for my Chi-squared distribution, but then again, I only do fits with same, in which case I always know the dof a priori. I guess I should really read through that Probability and Statistics text I bought but haven't gotten around to reading :-) -- ------------------------------------------------------------------------------- Kevin Lynch voice: (617) 353-6025 Physics Department Fax: (617) 353-9393 Boston University office: PRB-361 590 Commonwealth Ave. e-mail: krlynch@bu.edu Boston, MA 02215 USA http://budoe.bu.edu/~krlynch -------------------------------------------------------------------------------