_nico_ wrote:
Hello everyone,
I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):
# Build some fake binormally distributed data, the procedure fails also with
real data, so the problem isn't here
data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3))
# Just to check it's bimodal
plot(density(data))
f = function(m, s, m2, s2, w)
{
-log( sum(w*dnorm(data, mean=m, sd=s)) + sum((1-w)*dnorm(data, mean=m2,
sd=s2)) )
}
res = mle(f, start=list("m"=3, "s"=0.5, "m2"=5, "s2"=0.35, "w"=0.6))
This gives an error:
Error in optim(start, f, method = method, hessian = TRUE, ...) :
non-finite finite-difference value [2]
In addition: There were 50 or more warnings (use warnings() to see the first
50)
And the warnings are about dnorm producing NaNs
So, my questions are:
1) What does "non-finite finite-difference value" mean? My guess would be
that an Inf was passed somewhere where a finite number was expected...? I'm
not sure how optim works though...
2) Is the log likelihood function I wrote correct? Can I use the same type
of function for 3 modes?
I think it is correct but it is difficult to fit as others have pointed out.
As an alternative, you may discretise your variable so the mixture is
fit to counts on a histogram using a multinomial likelihood.
HTH
Rubén
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