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()???
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