This is what I might do:
> y <- rchisq( 1000, df=10, ncp=2 )
> library( stats4 )
> res <- mle( function(x,z) -sum( dchisq(y, x, z , log=TRUE ) ), start=list(
> x=5, z=5 ) )
> coef(res)
x z
10.355711 1.586123
>
> ## or just to keep clear of boundary constraints:
>
> res <- mle(
Hi, I have written out the log-likelihood function to fit some data I have
(called ONES20) to the non-central chi-squared distribution.
>library(stats4)
>ll<-function(lambda,k){x<-ONES20;
25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*
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