[R] Maximum likelihood estimation
Prof Brian Ripley
ripley at stats.ox.ac.uk
Thu Sep 4 12:00:01 CEST 2008
>From ?optim
fn: A function to be minimized (or maximized), with first
argument the vector of parameters over which minimization is
to take place. It should return a scalar result.
I think you intended to optimize over c(a,b,p, lambda), so you need to
specify them as a single vector.
You may be making life unnecessarily hard for yourself: see function mle()
in package stats4.
Showing your code without a verbal description of what you are doing nor
the error message you got is less helpful than we need.
On Wed, 3 Sep 2008, toh wrote:
>
> Hi R-experts,
> I'm new to R in mle. I tried to do the following but just couldn't get it
> right. Hope someone can point out the mistakes. thanks a lot.
>
> t <- c(1:90)
> y <-
> c(5,10,15,20,26,34,36,43,47,49,80,84,108,157,171,183,191,200,204,211,217,226,230,
>
> 234,236,240,243,252,254,259,263,264,268,271,277,290,309,324,331,346,367,375,381,
>
> 401,411,414,417,425,430,431,433,435,437,444,446,446,448,451,453,460,463,463,464,
>
> 464,465,465,465,466,467,467,467,468,469,469,469,469,470,472,472,473,473,473,473,
> 473,473,473,475,475,475,475)
> fr <- function(a, b, p, lambda){
> l <- 0.5*(lambda + b*p + (1-p)*(lambda-b))
> l^2 > lambda*b*p
> delta <- sqrt(abs(l^2 - b*p*lambda))
> mt <- a/p*(1-exp(-l*t)*cosh(delta*t)-(l-b*p)*exp(-t)*sinh(delta*t)/delta)
> logl <- sum(diff(y)*log(diff(mt))-diff(mt)-lfactorial(diff(y)))
> return(-logl)
> }
> optim(c(15,0.01,0.5,0.01),fr, method="L-BFGS-B",
> lower = c(0.002, 0.002, 0.002, 0.002), upper = c(Inf, Inf, 0.999,
> Inf),control=list(fnscale=-1))
>
> --
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>
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>
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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