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)}
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
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) {
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