Dear R users,
I am trying to minimize two function simultaneously in R,
function(x)
minimize x[1],x[2],x[3]
mean(distribution(x1,x2,x3) ) - observed mean
std(distribution(x1,x2,x3)) - observed std
What I want to achieve is that simulated mean and standard deviation
of distribution related to
On Thu, 2010-05-20 at 01:35 -0700, Fred wrote:
Dear R users,
I am trying to minimize two function simultaneously in R,
function(x)
minimize x[1],x[2],x[3]
mean(distribution(x1,x2,x3) ) - observed mean
std(distribution(x1,x2,x3)) - observed std
What I want to achieve is that
] Minimization problem
On Thu, 2010-05-20 at 01:35 -0700, Fred wrote:
Dear R users,
I am trying to minimize two function simultaneously in R,
function(x)
minimize x[1],x[2],x[3]
mean(distribution(x1,x2,x3) ) - observed mean
std(distribution(x1,x2,x3)) - observed std
What I want
Dear All,
Could someone give me some pointers (just a guide as to what functions I
need to look at would be fine)
as to how I go about this simple problem please;
The problem looks like this;
Choose x1 to x4 such that you minimize the MAXIMUM ABSOLUTE value returned
in the vector result of this
I'm not sure that it comes across as a matrix problem in the sense
of involving matrix algebra, but it's also possible that I don't
understand what you are saying. Seems that an appropriate application
of:
?abs
?max
?sapply
?which.min
... ought to do the trick. If you were hoping for
Hi Glenn,
perhaps I'm misunderstanding your problem, but it seems to me that the
zero vector (yielding a zero vector after the multiplication) will
happily minimize everything you want.
Apart from that, you may want to look at the Optimization Task View on CRAN.
Good luck,
Stephan
glenn
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