Spencer Graves <[EMAIL PROTECTED]> writes:
> I have not used "nlm", but that happens routinely with function
> minimizers trying to test negative values for one or more
> component of x. My standard approach to something like this is
> to parameterize "llfunc" in terms of log(shape) and log(scale),
> as follows: llfunc <- function (x) {
> -sum(dweibull(AM,shape=exp(x[1]),scale=exp(x[2]), log=TRUE))}
>
> Have you tried this? If no, I suspect the warnings will
> disappear when you try this.
This works. I've got some more questions, though:
(1) Does it introduce bias to work with the logs like this?
(2) My original data set had zero values. I added .5 experimentally,
which is how I got to this data set. This procedure doesn't work
on the original data set.
Instead I get (with the numbers below being the values
that caused problems):
[1] 0.41 3.70 1.00
[1] 0.41 3.70 1.00
[1] 0.410001 3.700000 1.000000
[1] 0.410000 3.700004 1.000000
[1] 0.410000 3.700000 1.000001
Warning messages:
1: NA/Inf replaced by maximum positive value
2: NA/Inf replaced by maximum positive value
3: NA/Inf replaced by maximum positive value
4: NA/Inf replaced by maximum positive value
Thanks,
-Ekr
--
[Eric Rescorla [EMAIL PROTECTED]
http://www.rtfm.com/
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