I am interested in creating regression equations for QSAR-related problems. The general idea is to find a linear regression equation from a pool of many (>200) independent variables or descriptors to predict one dependent variable. Typically, the number of rows/observations is small (n=50 at a minimum).
There are ways to do this using Generalized Regression in JMP. While the penalized regression methods or the simple forward or backward elimination can find a "good" linear regression equation, my concern is that these methods may not find the globally optimum solution and that they get stuck in a local minimum.
There are other techniques outside of JMP that can sample many more possible choices such as: genetic algorithms, simulated annealing, particle swarm optimization, ant/bee colony, etc. by taking subsets of descriptors, using a function to evaluate the resulting model, splitting up the subset to make and evaluate newer subsets.
I was wondering if anyone has created a way to do this within JMP or even has a way to do this outside of JMP (e.g. in R, Python, Matlab, SAS, etc.) and can perhaps think of a way to add-in this functionality within JMP? My specific need is to make linear regression equations and not neural networks, random forests, or other types of models that JMP can also make.
