[R] Optimize multiple variable sets

[Jonathan P Daily]

Correct me if I'm wrong, but isn't the minimal x value in your example the 
same regardless of what positive coefficient you apply to x? If that is 
the case, you would expect the same min(x) for each iteration.

i.e. in the interval [0,1] the minimum x value of x^2 + x is the same as 
x^2 + 100000000*x, at x = 0.
--------------------------------------
Jonathan P. Daily
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r-help-bounces at r-project.org wrote on 12/06/2010 07:00:57 AM:

> [image removed] 
> 
> [R] Optimize multiple variable sets
> 
> sandra lag 
> 
> to:
> 
> r-help
> 
> 12/06/2010 08:54 AM
> 
> Sent by:
> 
> r-help-bounces at r-project.org
> 
> 
> 
> Hi,
> I usually use optimize function for ML Estimation. Now I´ve got a 
> data frame with many sets, but I can´t save estimates each time I 
> run the code for each data set (I´m using a for loop with my 
> loglikelihood function and works ok but when I apply another for loop 
to:
> optimize(my.loglikelihood.function[i], int=c(0.0001,10))
> it doesn´t work;
> 
> alternatively, using optimize inside the for loop (like in the 
> example below), it returns always the same value, which is not expected:
> 
> data<-matrix(c(1,1,1, 2,2,2, 3,3,3, 4,4,4), nrow=3, ncol=4)
> c<-dim(data)[2]
> results<-vector(length=c)
> for (i in 1:c){
> f<-function(x){
> x^2+x*sum(data[,i])
> }
> results[i]<-optimize(f,int=c(0.0001,10))[1] #minimum
> }
> #results
> 
> Can someone please indicate me if there´s a different function/ way 
> to do so? (with no need of initial parameter values)
> Thanks! Sandra 
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