Optimize multiple variable sets
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.
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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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