This thread seems to have concentrated on forks using the primitives. That may have been part of John's specification, but one of the joys of J is its ability to express alternative approaches to a problem simply. I think most of the cases of real interest arise with defined functions.
If mean is a dyadic function with weights on the left and values on the right, and sd is also dyadic with similar arguments, and both functions are ambivalent with the monadic case weights of 1 Then sd%mean is the coefficient of variation for both the monadic and dyadic cases and both commonly occur. Add to these functions the deviations from the (weighted) mean, and a var function for the variance, and you have powerful tools for handling distributions. Regression provides other simple examples. We might have, using conventions of y and x as common in stats reg yx a monadic form y reg1 x a dyadic form To extract information from the regression result you might use (Coeff % CoeffSE) r to get the t-statistics, but it is only monadic because the data contains at least two distinct objects, the coefficients, and their variance matrix. but in a particular context it may have been appropriate to write (y coeff1 x) % (y coeffse1 x) Of course in the standard case that would mean possibly twice the computations, but there are related situations where the two calculations can be almost independent. >From the user perspective, this ability to formulate the problem in either a dyadic or a monadic form can be important, and adds to the flexibility of J as a tool of thought. Fraser ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
