From: Berend Hasselman <bhh_at_xs4all.nl>

Date: Wed, 13 Apr 2011 06:56:18 -0700 (PDT)

Date: Wed, 13 Apr 2011 06:56:18 -0700 (PDT)

Questions:

Doing both those statements outside the loop once is more efficient.

Finally the likelihood function at the end of your code

#Maximum likelihood estimation using mle package

library(stats4)

#defining loglikelighood function

*#T <- length(v)
**#minuslogLik <- function(x,x2)
**#{ f <- rep(NA, length(x))
**# for(i in 1:T)
**# {
**# f[1] <- -1/T*sum(log(transdens(parameters = parameters, x =
*

c(v[i],v[i+1])))-log(Jac(outmat=outmat, x2=c(v[i],r[i])))

*# }
**# f
**# }
*

How do the arguments of your function x and x2 influence the calculations in
the likelihood function?

As written now with argument x and x2 not being used in the body of the
function, there is nothing to optimize.

Shouldn't f[1] be f[i] because otherwise the question is why are looping
for( i in 1:T)?

But then returning f as a vector seems wrong here. Shouldn't a likelihood
function return a scalar?

Berend

-- View this message in context: http://r.789695.n4.nabble.com/MLE-where-loglikelihood-function-is-a-function-of-numerical-solutions-tp3439436p3447224.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Wed 13 Apr 2011 - 13:58:27 GMT

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