Hello,
is there somebody who can help me with my question (see below)?
Antje
> On 1 February 2011 09:09, Antje Niederlein <niederlein-rs...@yahoo.de> wrote:
>> 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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