[R] optimize linear function
roger koenker
rkoenker at uiuc.edu
Sun Jun 13 07:19:12 CEST 2004
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 rkoenker at uiuc.edu 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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