Hi Rolf,
I think the MLE should be 1.71, no? And yes, I am aware of the
maximum=TRUE argument. I still feel something is wrong here.
Thanks!
On Wed, Nov 18, 2015 at 6:23 PM, Rolf Turner <r.tur...@auckland.ac.nz>
wrote:
> On 19/11/15 11:31, C W wrote:
>
>> Dear R list,
>>
>> I am trying to find the MLE of the likelihood function. I will plot the
>> log-likelihood to check my answer.
>>
>> Here's my R code:
>>
>> xvec <- c(2,5,3,7,-3,-2,0)
>>
>> fn <- function(theta){
>>
>> sum(0.5 * (xvec - rep(theta, 7)) ^ 2 / 1 + 0.5 * log(1))
>>
>> }
>>
>> gn <- Vectorize(fn)
>>
>> curve(gn, -5, 20)
>>
>> optimize(gn, c(-5, 20))
>>
>> $minimum
>>
>> [1] 1.714286
>>
>> $objective
>>
>> [1] 39.71429
>>
>>
>> The MLE using optimize() is 1.71, but what curve() gives me is the
>> absolute
>> minimum.
>>
>> I think 1.71 is the right answer, but why does the graph showing it's the
>> minimum? What is going on here?
>>
>
> Your graph shows that there is indeed a *minimum* at 1.71. And optimise()
> is correctly finding that minimum.
>
> If you want optimise() to find the maximum, set maximum=TRUE. In which
> case it will return "20" (or something very close to 20).
>
> Your function fn() appears not to be the log likelihood that you had in
> mind. Perhaps you the negative of fn()???
>
> cheers,
>
> Rolf Turner
>
> --
> Technical Editor ANZJS
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
>
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