On Tue, 24 Jun 2008, Jinsong Zhao wrote:
Hi,
When I run the following code,
r <- c(3,4,4,3,5,4,5,9,8,11,12,13)
n <- rep(15,12)
x <- c(0, 1.1, 1.3, 2.0, 2.2, 2.8, 3.7, 3.9, 4.4, 4.8, 5.9, 6.8)
x <- log10(x)
So x[1] = -Inf
fr <- function(c, alpha, beta) {
P <- c + (1-c) * pnorm(alpha + beta * x)
P <- pmax(pmin(P,1),0)
-(sum(log(choose(n,r))) + sum(r * log(P)) + sum((n -r)* log(1-P)))
}
fit <- mle((fr), start = list(c =0.2, alpha = 0, beta =0.1), method =
"L-BFGS-B")
I got:
Error in optim(start, f, method = method, hessian = TRUE, ...) :
L-BFGS-B needs finite values of 'fn'
When I change "L-BFGS-B" to "BFGS" or anything else that optim()
support, there's no any error message.
What's wrong?
What the message says. Your fr() function is returning non-finite values.
Look at what happens if P is 0 or 1 for example. You seem to need 0 < c <
1, and you have not imposed that constraint. Adding a trace tp print
c(c, alpha, beta) I got
[1] 0.2 0.0 0.1
[1] 0.201 0.000 0.100
[1] 0.199 0.000 0.100
[1] 0.200 0.001 0.100
[1] 0.200 -0.001 0.100
[1] 0.200 0.000 0.101
[1] 0.200 0.000 0.099
[1] -0.63692943 -0.54093515 0.01670358
Error in optim(start, f, method = method, hessian = TRUE, ...) :
L-BFGS-B needs finite values of 'fn'
and other methods also give negative 'c' (and backtrack). There are also
problems if beta < 0 (as c + (1-c) * pnorm(alpha + beta * x) = 1 for x =
-Inf). So
fit <- mle((fr), start = list(c =0.2, alpha = 0, beta =0.1),
method = "L-BFGS-B", lower=c(0, -Inf, 0), upper = c(1, Inf, Inf))
works.
There is no point in using "L-BFGS-B" in a 3-parameter problem unless you
do impose constraints.
Also, dbinom() will give a more stable way to compute a binomial
log-likelihood.
Any suggestions will be appreciated.
Jinsong
--
Brian D. Ripley, [EMAIL PROTECTED]
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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