Hello JMP team ...
Nelson's approach is very likely what I was looking for. I am conversing w/ him on a similar need with mann kendall tau test.
My input is not in a single row.
My data is typically contains several parameters each measured at a different time step. Tidy data format; parameters are in columns, each row is a different time state - i.e. chronology is in rows.
I am looking into many statistical functions measuring trend in one of these or a fit versus some other X in time but as a function of rolling windows. So instead of regressing the whole history of X vs Y for instance, I like a logic that looks into regression of the two within constant past N time steps. Each row then re-evaluates and gets me a new result. These could a slope from linear regression, or a tau test from a non-parametric MK test or some other stat output.
Many times, we do not know when the correlation between the X's have changed, so this is one attempt to look into the impact of time for any analysis at hand. N mentioned above can be changed to various values to examine short term vs mid/long terms trends.
I am ok w/ scripts, they are not my preference unless the computations can great benefit a method offered only by scripts.
My preference is generally to put the calculations into formulaes and have them maintained within the data frames.
Until today, I did not know that script structures could be deployed within formulaes.
That probably was the main cause of my confusion.
Appreciate all your help.
