Re: [R] mle question
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$freq)
cat(x in lapply, x,\n)
fit - mle(ll)
coef(fit)
})
Can anybody help?
Antje
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] mle question
Hello,
is there somebody who can help me with my question (see below)?
Antje
On 1 February 2011 09:09, Antje Niederlein niederlein-rs...@yahoo.de wrote:
Hello,
I tried to use mle to fit a distribution(zero-inflated negbin for
count data). My call is very simple:
mle(ll)
ll() takes the three parameters, I'd like to be estimated (size, mu
and prob). But within the ll() function I have to judge if the current
parameter-set gives a nice fit or not. So I have to apply them to
observation data. But how does the method know about my observed data?
The mle()-examples define this data outside of this method and it
works. For a simple example, it was fine but when it comes to a loop
(tapply) providing different sets of observation data, it doesn't work
anymore. I'm confused - is there any way to do better?
Here is a little example which show my problem:
# R-code -
lambda.data - runif(10,0.5,10)
ll - function(lambda = 1) {
cat(x in ll(),x,\n)
y.fit - dpois(x, lambda)
sum( (y - y.fit)^2 )
}
lapply(1:10, FUN = function(x){
raw.data - rpois(100,lambda.data[x])
freqTab - count(raw.data)
x - freqTab$x
y - freqTab$freq / sum(freqTab$freq)
cat(x in lapply, x,\n)
fit - mle(ll)
coef(fit)
})
Can anybody help?
Antje
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] mle question
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
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] mle() question
Is there a way to code the mle() function in library stats4 such that it switches optimizing methods midstream (i.e. BFGS to Newton and back to BFGS, etc.)? Thanks, Stephen Collins, MPP | Analyst Health Benefits | Aon Consulting [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
