I have a large data set that has multiple independent and dependent variables. For smaller systems, I have organized the data such that the independent variables are defined in the Column Name. However, for much larger sets, where more than a few Xs and multiple Ys are involved, this becomes an extremely tedious exercise. Moreover, it makes for very wide, unwieldy tables and is hard to visualize graphically.
Does anyone in the community have any suggestions on a more straightforward method of organizing large spectral data sets for PLS?
Thanks for the response. I know about organizing data via column groups; what I'm looking for is a more organized way to input multi-spectral input to PLS. For PLS input, I currently convert all the independent variables to columns using 'split' operations and then have the dependent variable(s) as the column value. It would be great if PLS could accept x,y,z, ... as values in their respective columns, and then have the response (dependent) variables as separate columns.
As for this kind of data being hard to visualize, it would be great to (at least) have a graphics capability which would allow topographical maps to be generated from 3D data. 3D scatterplot is just okay for this kind of visualization.