optim really isn't intended for [1D] functions. And if you have a constrained search area,
it pays to use it. The result you are getting is like the second root of a quadratic that
you are not interested in.
You may want to be rather careful about the problem to make sure you have the function
You're minimizing the log likelihood, but you want to minimize the *negative*
log likelihood. Also, optimize() is better than optim() if you have a
function of only one argument.
Replace
Jon Moroney wrote:
>
> #Create the log likelihood function
> LL<-function(x) {(trials*log(x))-(x*sumvect)}
Hi all,
I'm trying to make a little script to determine an "unknown" rate for a
number of known exponential trials.
My Code:
#Set Trials and generate number
trials=100
rand<-runif(1,0,1)
vector=0
#Generate vector of 100 random exponentials and sum them
for (i in 1:100) {
vector<-rexp(trials
3 matches
Mail list logo
