# Re: [R] maximizing the gamma likelihood

From: Xiaohui Chen <chenxh007_at_gmail.com>
Date: Sat, 24 May 2008 00:03:38 -0700

Hi Mark,

As I said in earlier emails, you do NOT need to explicitly code the likelihood function for gamma distn. I just code you example as below:

vsamples<- c(14.7, 18.8, 14, 15.9, 9.7, 12.8) gamma.nll <- function(par,data)
-sum(dgamma(data,shape=par[1],scale=par[2],log=T)) optim(c(1,1),gamma.nll,data=vsamples,method='BFGS')  >
\$par
[1] 24.9327797 0.5743043

\$value
[1] 14.72309

The mle is pretty robust to the starting values: if you change the initialization to c(10,10), the results would be  >
\$par
[1] 25.0285880 0.5721142

\$value
[1] 14.72299

Anyway, for the standard distns, which R has implementation, you definitely don't have to do everything from scratch.

Xiaohui

markleeds_at_verizon.net 写道:
> for learning purposes and also to help someone, i used roger peng's
> document to get the mle's of the gamma where the gamma is defined as
>
> f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) *
> exp(-scale*y_i)
>
> ( i'm defining the scale as lambda rather than 1/lambda. various books
> define it differently ).
>
> i found the likelihood to be n*shape*log(scale) +
> (shape-1)*sum(log(y_i) - scale*sum(y_i)
> then i wrote below which is just roger peng's likelihood example but
> using the gamma instead of the normal.
> I get estimates back but i separately found that the analytical mle of
> the scale is equal to 1/ analytical mle(shape).
> and my estimates aren't consistent with that fact ? this leads me to
> assume that my estimates are not correct.
>
> can anyone tell me what i'm doing wrong. maybe my starting values are
> too far off ? thanks.
>
> make.negloglik <- function(data, fixed=c(FALSE,FALSE)) {
> op <- fixed
> function(p) {
> op[!fixed] <- p
> shape <- exp(op[1])
> scale <- exp(op[2])
> a <- length(data)*shape*log(scale)
> b <- (shape-1)*sum(log(data))
> c <- -1.0*scale*sum(data)
> -(a + b + c)
> }
> }
>
> vsamples<- c(14.7, 18.8, 14, 15.9, 9.7, 12.8)
> nLL <- make.negloglik(vsamples)
> temp <- optim(c(scale=1,shape=1), nLL, method="BFGS")[["par"]]
> estimates <- log(temp)
> print(estimates)
>
> check <- estimates[1]/mean(vsamples)
> print(check)
>
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