On Jun 12, 2004, at 4:35 PM, Prof Brian Ripley wrote:
This is just a linear programming problem. So the packages which do linear programming are `particularly well suited to this sort of task' and theory tells you a lot about the solution.
Indeed. With the package quantreg, for example, you could do:
rq(y ~ x1 + x2, tau = eps)
for any small eps. Note that in the example sum(y) and the additive
factor .03 aren't really germane. If the problem is large you might want to
add method = "fn" to the call to use interior point optimization rather than
simplex (exterior point) methods).
url: www.econ.uiuc.edu/~roger Roger Koenker email [EMAIL PROTECTED] Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820
[....]
On Sat, 12 Jun 2004, Michaell Taylor wrote:
I am attempting to optimize a regression model's parameters to meet a specific
target for the sum of positive errors over sum of the dependent variable
(minErr below).
=========== Sample Problem = first approach ================ # linear model (presumably yielding B1=.8 and B2=.2)
m<-lm(y ~ x1+ x2) # test on summation of positive errors. e <- resid(m) minErr <- (sum(ifelse(e<0,0,e))/sum(y))-.03
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