David Winsemius comcast.net> writes:
>
>
> On Nov 10, 2012, at 9:22 PM, mmosalman wrote:
>
> > I want to find ML estimates of a model using mle2 in bbmle package. When I
> > insert new parameters (for new covariates) in model the log-likelihood value
> > does not change and the estimated value
On Nov 10, 2012, at 9:22 PM, mmosalman wrote:
> I want to find ML estimates of a model using mle2 in bbmle package. When I
> insert new parameters (for new covariates) in model the log-likelihood value
> does not change and the estimated value is exactly the initial value that I
> determined. Wha
I want to find ML estimates of a model using mle2 in bbmle package. When I
insert new parameters (for new covariates) in model the log-likelihood value
does not change and the estimated value is exactly the initial value that I
determined. What's the problem? This is the code and the result:
As
Dear R-helper,
I am trying to do maximum likelihood estimation in R. I use the "optim"
function. Since I have no prior information on the true values of the
parameters, I just randomly select different sets of starting values to feed
into the program. Each time, I get the following error
Thank you!
Best Regards
Henrik
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h
Abhishek:
Thank you!
Thomas:
that worked out well, thank you again!
I also tried to use lm, and as expected in this case, I almost got the same
estimates of the parameters as in the MLE-case.
Best Regards
Henrik
--
View this message in context:
http://r.789695.n4.nabble.com/Maximum-L
that worked out well, thank you again!
I also tried to use lm, and as expected in this case, I almost got the same
estimates of the parameters as in the MLE-case.
Best Regards
Henrik
--
View this message in context:
http://r.789695.n4.nabble.com/Maximum-Likelihood-Estimation-in-R-tp201882
Henrik-
A coding solutions may be
... + (1/(2*stdev*stdev))*sum( ( y-(rev/12)- c(0,y[-n]) *exp(-lap/12) )^2
)
where n is the number of observations in y.
Personally, I would use lm. Your model can be written as a linear function.
Let x=c(0,y[-n]). Then run lm(y~x). The parameter estimat
Thank you Thomas.
(a) an embarrassing mistake by me. Of course it should be squared. Thank you
for pointing that out.
(b) Do you possibly have any suggestions on how to solve this issue? I
presume that there is no reason in trying to create a lagged "vector"
manually?
Best Regards
Henrik
--
Hey Henrik
I dont do MLE myself but this recent blog might be helpful.
http://www.johnmyleswhite.com/notebook/2010/04/21/doing-maximum-likelihood-estimation-by-hand-in-r/
-A
On Wed, Apr 21, 2010 at 10:02 AM, Thomas Stewart wrote:
> Two possible problems:
>
> (a) If you're working with a normal
Two possible problems:
(a) If you're working with a normal likelihood---and it seems that you
are---the exponent should be squared. As in:
... + (1/(2*stdev*stdev))*sum( ( y-(rev/12)-lag(y)*exp(-lap/12) )^2 )
(b) lag may not be working like you think it should. Consider this silly
example:
y<
Dear R-Help,
I also send the following post by e-mail to you, however I try to post it
here aswell. My name is Henrik and I am currently trying to solve a Maximum
Likelihood optimization problem in R. Below you can find the output from R,
when I use the "BFGS" method:
The problem is that the p
Try survreg(), in the survival package.
-thomas
On Fri, 13 Jun 2008, Bluder Olivia wrote:
Hello,
I'm trying to calculate the Maximum likelihood estimators for a dataset
which contains censored data.
I started by using the function "nlm", but isn't there a separate method
for doing
Le ven. 13 juin à 13:55, Ben Bolker a écrit :
Bluder Olivia k-ai.at> writes:
Hello,
I'm trying to calculate the Maximum likelihood estimators for a
dataset
which contains censored data.
I started by using the function "nlm", but isn't there a separate
method
for doing this for e.g. t
Bluder Olivia k-ai.at> writes:
>
> Hello,
>
> I'm trying to calculate the Maximum likelihood estimators for a dataset
> which contains censored data.
>
> I started by using the function "nlm", but isn't there a separate method
> for doing this for e.g. the "weibull" and the "log-normal" distri
Hello,
I'm trying to calculate the Maximum likelihood estimators for a dataset
which contains censored data.
I started by using the function "nlm", but isn't there a separate method
for doing this for e.g. the "weibull" and the "log-normal" distribution?
Thanks,
Olivia
[[a
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