R help - Feb 2011 - Constraints in projection pursuit regression

Hi,


I am solving a projection pursuit regression problem, of the 
form y = \sum_i f_i (a_i^T x), where a_i are unknown directions, while 
f_i are unknown univariate link functions. The following is known about 
each f_i:


1. f_i (0) = 0  (that is, each f_i passes through the origin)

2. f_i is monotonic.


Is there a way to ensure that the function ppr() in R produces solutions that
respect the above two conditions on f_i?


Also, are there ways to enforce constraints on the a_i, say, that the first
component is positive?
Or that one of the a_i is known.

Thanks!




      
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