Hello,
I tried to use mle to fit a distribution(zero-inflated negbin for
count data). My call is very simple:
mle(ll)
ll() takes the three parameters, I'd like to be estimated (size, mu
and prob). But within the ll() function I have to judge if the current
parameter-set gives a nice fit or not. So I have to apply them to
observation data. But how does the method know about my observed data?
The mle()-examples define this data outside of this method and it
works. For a simple example, it was fine but when it comes to a loop
(tapply) providing different sets of observation data, it doesn't work
anymore. I'm confused - is there any way to do better?
Here is a little example which show my problem:
# R-code ---------------------------------
lambda.data <- runif(10,0.5,10)
ll <- function(lambda = 1) {
cat("x in ll()",x,"\n")
y.fit <- dpois(x, lambda)
sum( (y - y.fit)^2 )
}
lapply(1:10, FUN = function(x){
raw.data <- rpois(100,lambda.data[x])
freqTab <- count(raw.data)
x <- freqTab$x
y <- freqTab$freq / sum(freqTab$freq)
cat("x in lapply", x,"\n")
fit <- mle(ll)
coef(fit)
})
Can anybody help?
Antje
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