jango wrote:
>
> I'm trying to calculate the maximum likelihood estimate for a binomial
> distribution. Here is my code:
>
> y <- c(2, 4, 2, 4, 5, 3)
> n <- length(y)
> binomial.ll <- function (pi, y, n) { ## define log-likelihood
> output <- y*log(pi)+(n-y)*(log(1-pi))
> return(output)
> }
> binomial.mle <- optim(0.01, ## starting value
> binomial.ll, ## log likelihood
> method="BFGS", ## optimization method
> hessian=TRUE, ## numerial Hessian
> control=list(fnscale=-1), ## max, not min
> y=y, n=n)
> binomial.mle.par <- c(binomial.mle$par, -1/binomial.mle$hessian[1,1])
> binomial.mle.par <- as.matrix(binomial.mle.par)
> rownames(binomial.mle.par) <- c("lambda", "s.e.")
> colnames(binomial.mle.par) <- c("MLE")
> print(binomial.mle.par)
>
> When I do this I get the following error message:
>
> Error in optim(0.01, binomial.ll, method = "BFGS", hessian = TRUE, control
> = list(fnscale = -1), :
> objective function in optim evaluates to length 6 not 1
>
>
After defining your binomial.ll function do this
binomial.ll(0.01,y,n)
and you will see that your function is returning a vector of length 6, which
is the length of y.
Your function is returning a vector but should return a scalar.
A likelihood is a scalar so maybe return(sum(output)).
Berend
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