Thanks a lot to everybody who answered!
Sorry for not mentioning the package I used (I am aware of this fact
but I simply forgot to put it to the example code....)
Thanks a lot for explaning the problem to me. I was pretty sure that
it ist something like this but I thought, I've made a mistake in how
to use mle() correctly.
I think, I'll look into Bens mle2() method and figure out whether this
is a more elegant way :-)
Ciao,
Antje
On 7 February 2011 21:39, Ben Bolker <bbol...@gmail.com> wrote:
> Antje Niederlein <niederlein-rstat <at> yahoo.de> writes:
>
>>
>> A few day ago, I was looking for an answer to my question but didn't
>> get one. Anybody who can help now?
>>
>> Hello,
>>
>> I tried to use mle to fit a distribution(zero-inflated negbin for
>> count data). My call is very simple:
>>
>
> I will point out that this is one of the reasons I wrote mle2
> (in the bbmle package), which differs from mle in taking an explicit
> 'data' argument. I *think* the following does what you want (although
> I admit I haven't looked at the output closely):
>
> library(plyr)
> library(bbmle)
> 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 <- mle2(ll,data=data.frame(x,y))
>
> coef(fit)
> })
>
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