Re: [R] maximum likelihood estimation
I need to perform maximum likelihood estimation on R, but I am not sure which command to use. I searched on google, and found an example using the function mlogl, but I couldn't find the package on R. Is there such function? Or how should i perform my mle? http://www.mayin.org/ajayshah/KB/R/documents/mle/mle.html might help. -- Ajay Shah http://www.mayin.org/ajayshah [EMAIL PROTECTED] http://ajayshahblog.blogspot.com *(:-? - wizard who doesn't know the answer. __ R-help@stat.math.ethz.ch 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] maximum likelihood estimation
Hello! I need to perform maximum likelihood estimation on R, but I am not sure which command to use. I searched on google, and found an example using the function mlogl, but I couldn't find the package on R. Is there such function? Or how should i perform my mle? Thank you very much. -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-tf4103791.html#a11670424 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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.
Re: [R] maximum likelihood estimation
On Wed, Jul 18, 2007 at 08:08:50AM -0700, rach.s wrote: Hello! I need to perform maximum likelihood estimation on R, but I am not sure which command to use. I searched on google, and found an example using the function mlogl, but I couldn't find the package on R. Is there such function? Or how should i perform my mle? ^^^ :) library(stats4) ?mle G. [...] -- Csardi Gabor [EMAIL PROTECTED]MTA RMKI, ELTE TTK __ R-help@stat.math.ethz.ch 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] maximum likelihood estimation of 5 parameters
Hi Guys, it would be great if you could help me with a MLE problem in R.
I am trying to evaluate the maximum likelihood estimates of theta = (a1,
b1, a2, b2, P) which defines a mixture of a Poisson distribution and two
gamma prior distributions (where the Poisson means have a gamma
distribution, actually 2 gammas and P is the mixing factor). The likelihood
function for theta is L(theta) = Pi,j{P f(Nij; a1, b1, Eij) + (1 – P) f(Nij;
a2, b2, Eij),}
The maximum likelihood estimate of theta is the vector that maximizes the
above equation (the values of N and E are given). The authors of the article
I read say that the maximization involves an iterative search in the five
dimensional parameter space, where each iteration involves computing
log[L(theta)] and its first and second-order derivatives. In test runs it is
suggested that the maximization typically takes between 5 and 15 iterations
from the starting point theta = (a1 = 0.2, b1 = 0.1, a2 = 2, b2 = 4, P =
1/3).
Now I have done maximization of a gamma-poisson mixture before (1 poisson, 1
gamma) successfully and I could determine correctly alpha (a) and beta(a).
But this one above is giving me ridiculously large unusable values (for
example P should not be above 1 and sometimes I get values of 500!) or even
negative values! I know the values I should be obtaining with my samples
shouldn't be far from the staring points. Is there a way to help me solve
this issue? Thanks.
--
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http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.html#a8177473
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Re: [R] maximum likelihood estimation of 5 parameters
Franco,
You can provide lower and upper bounds on the parameters if you use optim
with method=L-BFGS-B.
Hth, Ingmar
From: francogrex [EMAIL PROTECTED]
Date: Fri, 5 Jan 2007 04:54:50 -0800 (PST)
To: r-help@stat.math.ethz.ch
Subject: [R] maximum likelihood estimation of 5 parameters
Hi Guys, it would be great if you could help me with a MLE problem in R.
I
am trying to evaluate the maximum likelihood estimates of theta = (a1,
b1,
a2, b2, P) which defines a mixture of a Poisson distribution and two
gamma
prior distributions (where the Poisson means have a gamma
distribution,
actually 2 gammas and P is the mixing factor). The likelihood
function for
theta is L(theta) = Pi,j{P f(Nij; a1, b1, Eij) + (1 P) f(Nij;
a2, b2, Eij),}
The maximum likelihood estimate of theta is the vector that maximizes
the
above equation (the values of N and E are given). The authors of the
article
I read say that the maximization involves an iterative search in the
five
dimensional parameter space, where each iteration involves
computing
log[L(theta)] and its first and second-order derivatives. In test
runs it is
suggested that the maximization typically takes between 5 and 15
iterations
from the starting point theta = (a1 = 0.2, b1 = 0.1, a2 = 2, b2 =
4, P =
1/3).
Now I have done maximization of a gamma-poisson mixture before
(1 poisson, 1
gamma) successfully and I could determine correctly alpha (a)
and beta(a).
But this one above is giving me ridiculously large unusable
values (for
example P should not be above 1 and sometimes I get values of
500!) or even
negative values! I know the values I should be obtaining with my
samples
shouldn't be far from the staring points. Is there a way to help me
solve
this issue? Thanks.
--
View this message in context:
http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.
html#a8177473
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Nabble.com.
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thz.ch mailing list
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read the posting guide http://www.R-project.org/posting-guide.html
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commented, minimal, self-contained, reproducible code.
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Re: [R] maximum likelihood estimation of 5 parameters
Franco, You can provide lower and upper bounds on the parameters if you use optim with method=L-BFGS-B. Hth, Ingmar Thanks, but when I use L-BFGS-B it tells me that there is an error in optim(start, f, method = method, hessian = TRUE, ...) : L-BFGS-B needs finite values of 'fn' -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.html#a8180120 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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.
Re: [R] maximum likelihood estimation of 5 parameters
On Fri, 5 Jan 2007, francogrex wrote: [quoting Ingmar Vissar without attribution, contrary to the posting guide.] Franco, You can provide lower and upper bounds on the parameters if you use optim with method=L-BFGS-B. Hth, Ingmar Thanks, but when I use L-BFGS-B it tells me that there is an error in optim(start, f, method = method, hessian = TRUE, ...) : L-BFGS-B needs finite values of 'fn' It sounds as if you have ignored the advice to scale the problem via control options 'fnscale' and 'parscale'. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@stat.math.ethz.ch 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.
Re: [R] maximum likelihood estimation of 5 parameters
Franco, Is it possible that you have failed to provide the negative of loglikelihood to optim, since optim, by default, minimizes a function? If you want to do this withput redefining the log-likelihood, you should set fnscale= -1 (as hinted by Prof. Ripley). This would turn the problem into a maximization problem. If this doesn't work, you should provide more details (a reproducible code with actual error message). Ravi. --- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of francogrex Sent: Friday, January 05, 2007 10:42 AM To: r-help@stat.math.ethz.ch Subject: Re: [R] maximum likelihood estimation of 5 parameters Franco, You can provide lower and upper bounds on the parameters if you use optim with method=L-BFGS-B. Hth, Ingmar Thanks, but when I use L-BFGS-B it tells me that there is an error in optim(start, f, method = method, hessian = TRUE, ...) : L-BFGS-B needs finite values of 'fn' -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf292536 4.html#a8180120 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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-help@stat.math.ethz.ch 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.
Re: [R] maximum likelihood estimation of 5 parameters
Using the inverse logistic transform to replace p by exp(xp)/(1+exp(xp)) allows
unconstrained fitting of xp. There may still be problems where xp tends to + or
- infinity depending on starting values.
francogrex [EMAIL PROTECTED] 01/05/07 11:54 PM
Hi Guys, it would be great if you could help me with a MLE problem in R.
I am trying to evaluate the maximum likelihood estimates of theta = (a1,
b1, a2, b2, P) which defines a mixture of a Poisson distribution and two
gamma prior distributions (where the Poisson means have a gamma
distribution, actually 2 gammas and P is the mixing factor). The likelihood
function for theta is L(theta) = Pi,j{P f(Nij; a1, b1, Eij) + (1 * P) f(Nij;
a2, b2, Eij),}
The maximum likelihood estimate of theta is the vector that maximizes the
above equation (the values of N and E are given). The authors of the article
I read say that the maximization involves an iterative search in the five
dimensional parameter space, where each iteration involves computing
log[L(theta)] and its first and second-order derivatives. In test runs it is
suggested that the maximization typically takes between 5 and 15 iterations
from the starting point theta = (a1 = 0.2, b1 = 0.1, a2 = 2, b2 = 4, P =
1/3).
Now I have done maximization of a gamma-poisson mixture before (1 poisson, 1
gamma) successfully and I could determine correctly alpha (a) and beta(a).
But this one above is giving me ridiculously large unusable values (for
example P should not be above 1 and sometimes I get values of 500!) or even
negative values! I know the values I should be obtaining with my samples
shouldn't be far from the staring points. Is there a way to help me solve
this issue? Thanks.
--
View this message in context:
http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.html#a8177473
Sent from the R help mailing list archive at Nabble.com.
__
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Re: [R] maximum likelihood estimation of 5 parameters
No I have not forgotten to use a negative fnscale to optimize, so as you
suggest I will post some parts of the code I am running to show you the
errors:
n
[1] 3 1 4 54 6 58 20 14 3 14 4 65 1 7 9 10 2 4
66
[20] 5 9 7 12 7 55 105 2 5 10 55 5 28 1 1 6 2 1
30
[39] 6 49 7 21 8 7
e
[1] 21.763201 1.209070 4.836270 32.644798 19.546600 24.584400 30.226700
[8] 6.045340 14.010100 3.113350 21.015100 12.583100 15.826200 19.458401
[15] 3.891690 1.329970 0.241814 3.143580 13.057900 0.725441 18.136000
[22] 2.187660 6.319900 1.701510 29.654900 36.460999 7.292190 1.215370
[29] 3.209070 19.995001 11.972300 3.455920 0.138539 0.113350 1.360200
[36] 1.889170 1.518890 18.226700 4.050380 27.340099 1.181360 16.370300
[43] 20.589399 25.314899
fr-function(a1,b1,a2,b2,p){
+
+ w-((gamma(a1+n)))/((gamma(a1)*factorial(n))*(1+(e/b1)^a1)*(1+(b1/e)^n))
+ z-((gamma(a2+n)))/((gamma(a2)*factorial(n))*(1+(e/b2)^a2)*(1+(b2/e)^n))
+
+ sum (log( (p*w)+ ((1-p)*z) ))
+
+ }
mle((fr),
start=list(a1=0.2,b1=0.1,a2=2,b2=4,p=0.33),method=BFGS,control=list(fnscale=-1))
Error in optim(start, f, method = method, hessian = TRUE, ...) :
non-finite finite-difference value [2]
And with the L-BFGS-B:
Error in optim(start, f, method = method, hessian = TRUE, ...) :
L-BFGS-B needs finite values of 'fn'
AND WITH NELDER-MEAD it doesn't work either (same error), but when I change
intial parameters (though I shouldn't, it gives something very weird
(negatives or sometimes huge values).
Call:
mle(minuslogl = (fr), start = list(a1 = 1, b1 = 1, a2 = 10, b2 = 10,
p = 0.9), method = Nelder-Mead, control = list(fnscale = -1))
Coefficients:
a1 b1 a2 b2 p
-2.5035823 0.6236359 26.5562988 12.9604112 -0.1383767
Thanks
Ravi Varadhan wrote:
Franco,
Is it possible that you have failed to provide the negative of
loglikelihood
to optim, since optim, by default, minimizes a function? If you want to
do this withput redefining the log-likelihood, you should set fnscale= -1
(as hinted by Prof. Ripley). This would turn the problem into a
maximization problem.
If this doesn't work, you should provide more details (a reproducible code
with actual error message).
Ravi.
---
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: [EMAIL PROTECTED]
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of francogrex
Sent: Friday, January 05, 2007 10:42 AM
To: r-help@stat.math.ethz.ch
Subject: Re: [R] maximum likelihood estimation of 5 parameters
Franco,
You can provide lower and upper bounds on the parameters if you use optim
with method=L-BFGS-B.
Hth, Ingmar
Thanks, but when I use L-BFGS-B it tells me that there is an error in
optim(start, f, method = method, hessian = TRUE, ...) : L-BFGS-B needs
finite values of 'fn'
--
View this message in context:
http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf292536
4.html#a8180120
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Re: [R] maximum likelihood estimation of 5 parameters
] maximum likelihood estimation of 5 parameters Franco, You can provide lower and upper bounds on the parameters if you use optim with method=L-BFGS-B. Hth, Ingmar Thanks, but when I use L-BFGS-B it tells me that there is an error in optim(start, f, method = method, hessian = TRUE, ...) : L-BFGS-B needs finite values of 'fn' -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf292536 4.html#a8180120 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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-help@stat.math.ethz.ch 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. -- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.html#a8186869 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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. Charles C. Berry(858) 534-2098 Dept of Family/Preventive Medicine E mailto:[EMAIL PROTECTED] UC San Diego http://biostat.ucsd.edu/~cberry/ La Jolla, San Diego 92093-0717 __ R-help@stat.math.ethz.ch 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.
Re: [R] Maximum likelihood estimation of Regression parameters
I'm working with lm for some time, so I know the function. The problem was that I purchased the book to learn more about linear models and couldn't find the equivalent for the maximum likelihood in R, I used lm, mle, and a few others, but it never allow you to set a variance parameter. But after reading some further, I should be aware of posting to quick, sorry for that. I have read the Practical regression and anova in R by Faraway some month ago, and I know that the Venable and Ripley book should be the one to buy, but I think I can't find it for about 10$ as I did with the Applied linear statistical models. As I'm not in the position to spend a lot of money an buying books, I should do my work with the contributed documents, and the wrong book. Thanks for your help and time both of you Bart - Original Message - From: Spencer Graves [EMAIL PROTECTED] To: [EMAIL PROTECTED] Cc: Bart Joosen [EMAIL PROTECTED]; r-help@stat.math.ethz.ch Sent: Sunday, June 11, 2006 5:20 PM Subject: Re: [R] Maximum likelihood estimation of Regression parameters Have you looked at lm? I think that's what you want. Also, have you reviewed the Documentation list at www.r-project.org? Neter, Kutner, nachtsheim Wasserman has had a long and successful run having first appeared in 1974 and having gone through several editions since then. However, it apparently has not kept up with the R revolution, and I would not recommend it today. Beyond this, if you don't have Venables and Ripley (2002) Modern Applied Statistics with S (Springer), I recommend you look at it first. It has numerous index entries on regression and is all around my favorite book on R generally. Also, have you checked the Documentation briefly outlined at 'www.r-project.org', including the Contributed Documentation on CRAN, and Practical Regression and Anova using R by Faraway in particular? hope this helps. Spencer Graves Xiaoting Hua wrote: mle(stats4)Maximum Likelihood Estimation is it list above what you want? On 6/10/06, Bart Joosen [EMAIL PROTECTED] wrote: Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book Applied linear statistical models from Neter, Kutner, nachtsheim Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give a fixed variance to work with, but I couldn't find a function where I can estimate the predictor and where I have to give the variance. Or isn't this neccesairy? Also they calculate likelihood values for the different values, used to estimate the parameters (like a normal probability curve), is it possible to do this with R? Kind regards Bart [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation of Regression parameters
Have you looked at lm? I think that's what you want. Also, have you reviewed the Documentation list at www.r-project.org? Neter, Kutner, nachtsheim Wasserman has had a long and successful run having first appeared in 1974 and having gone through several editions since then. However, it apparently has not kept up with the R revolution, and I would not recommend it today. Beyond this, if you don't have Venables and Ripley (2002) Modern Applied Statistics with S (Springer), I recommend you look at it first. It has numerous index entries on regression and is all around my favorite book on R generally. Also, have you checked the Documentation briefly outlined at 'www.r-project.org', including the Contributed Documentation on CRAN, and Practical Regression and Anova using R by Faraway in particular? hope this helps. Spencer Graves Xiaoting Hua wrote: mle(stats4)Maximum Likelihood Estimation is it list above what you want? On 6/10/06, Bart Joosen [EMAIL PROTECTED] wrote: Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book Applied linear statistical models from Neter, Kutner, nachtsheim Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give a fixed variance to work with, but I couldn't find a function where I can estimate the predictor and where I have to give the variance. Or isn't this neccesairy? Also they calculate likelihood values for the different values, used to estimate the parameters (like a normal probability curve), is it possible to do this with R? Kind regards Bart [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Maximum likelihood estimation of Regression parameters
Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book Applied linear statistical models from Neter, Kutner, nachtsheim Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give a fixed variance to work with, but I couldn't find a function where I can estimate the predictor and where I have to give the variance. Or isn't this neccesairy? Also they calculate likelihood values for the different values, used to estimate the parameters (like a normal probability curve), is it possible to do this with R? Kind regards Bart [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation of Regression parameters
mle(stats4)Maximum Likelihood Estimation is it list above what you want? On 6/10/06, Bart Joosen [EMAIL PROTECTED] wrote: Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book Applied linear statistical models from Neter, Kutner, nachtsheim Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give a fixed variance to work with, but I couldn't find a function where I can estimate the predictor and where I have to give the variance. Or isn't this neccesairy? Also they calculate likelihood values for the different values, used to estimate the parameters (like a normal probability curve), is it possible to do this with R? Kind regards Bart [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- 此致 敬礼！ 华孝挺 Kenneth Hua 浙江大学核农所 杭州，中国 310029 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum Likelihood Estimation
Andrej Kastrin wrote: Uwe Ligges wrote: [EMAIL PROTECTED] wrote: Hi, I would like to know how to configure R so that I can enter some values and compute the Muximum likelihood estimation of my data. Maximum likelihood estimation of what? I do not know the definition of Maximum likelihood estimation of [...] data. Uwe Ligges Thanks Victor. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Google for Fitting Distributions with R. Excellent intro to your question... Yes, thanks. I know maximum likelihood estimation, but I do not know what the original questioner means with compute the Muximum likelihood estimation of my data. I tried to point out that we need some more details - maybe using a too subtle way in this case ... Uwe Ligges HTH, Andrej __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Maximum Likelihood Estimation
Hi, I would like to know how to configure R so that I can enter some values and compute the Muximum likelihood estimation of my data. Thanks Victor. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum Likelihood Estimation
[EMAIL PROTECTED] wrote: Hi, I would like to know how to configure R so that I can enter some values and compute the Muximum likelihood estimation of my data. Maximum likelihood estimation of what? I do not know the definition of Maximum likelihood estimation of [...] data. Uwe Ligges Thanks Victor. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum Likelihood Estimation
Uwe Ligges wrote: [EMAIL PROTECTED] wrote: Hi, I would like to know how to configure R so that I can enter some values and compute the Muximum likelihood estimation of my data. Maximum likelihood estimation of what? I do not know the definition of Maximum likelihood estimation of [...] data. Uwe Ligges Thanks Victor. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Google for Fitting Distributions with R. Excellent intro to your question... HTH, Andrej __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Maximum Likelihood Estimation
hi all, Can anyone tell me how to do Maximum Likelihood Estimation in R? Thanks in advance Arun -- [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum Likelihood Estimation
Arun Kumar Saha arun.kumar.saha at gmail.com writes: hi all, Can anyone tell me how to do Maximum Likelihood Estimation in R? Unfortunately this question is ***way*** too vague for us to answer adequately. A short answer is that R provides general-purpose minimization functions (optim, nlm, nlminb) that you can use to minimize a negative log-likelihood function; the mle() function in the stats4 package is a wrapper for this function. You have to define your own likelihood function: http://www.mayin.org/ajayshah/KB/R/documents/mle/mle.html may be helpful. Ben Bolker __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] maximum likelihood estimation
Roger == Roger D Peng [EMAIL PROTECTED]
on Thu, 14 Oct 2004 17:06:25 -0400 writes:
Roger What lead you to believe that mle() is defunct? It's
Roger still in the `stats4' package in my installation of
Roger R.
yes indeed. Just to clarify possible confusions:
The 'mle' *package* has been merged into the new 'stats4'
package {for 1.9.0 already}. The mle() function has always been
available and there were even discussions on extension /
generalization of its functioning and the methods working on
mle-class objects (produced by mle(...).
Martin
Roger -roger
Roger T. Murlidharan Nair wrote:
Since mle is defunct is there anyother function I can use
for maximum likelihood estimation ? Thanks ../Murli
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[R] maximum likelihood estimation
Since mle is defunct is there anyother function I can use for maximum likelihood estimation ? Thanks ../Murli __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] maximum likelihood estimation
What lead you to believe that mle() is defunct? It's still in the `stats4' package in my installation of R. -roger T. Murlidharan Nair wrote: Since mle is defunct is there anyother function I can use for maximum likelihood estimation ? Thanks ../Murli __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Maximum likelihood estimation in R
Hi, -Original Message- From: [EMAIL PROTECTED] [SMTP:[EMAIL PROTECTED] Sent: Sunday, February 15, 2004 10:24 PM To: [EMAIL PROTECTED]; [EMAIL PROTECTED] Subject: Re: [R] Maximum likelihood estimation in R Hello, Use x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) mean sd 2.9331 0.99673982 (0.09967398) (0.07048015) For me, this example does not work. As it looks like copy+paste, I guess this something platform dependent? At least I have to specify some starting values as shown below. But maybe I did something wrong? Best, Roland (I am using at the moment R 1.8.1 on WinNT) x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) Error in fitdistr(x, normal) : 'start' must be a named list fitdistr(x, normal, start=list(mean=1, sd=0.4)) mean sd 3.03167815 1.06637851 (0.10663786) (0.07539617) + This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation in R
Hello, Excellent, also the book: Pawitan, Yudi (2001). In all Likelihood: Statistical Modelling and Inference using Likelihood, Clarendon Press, Oxford. Is very good and the associated Web Site is full of MLE using R. Hope this also helps. /oal __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation in R
I got the same error message in R 1.8.1 and S-Plus 6.1. Then I read the error message: 'start' must be a named list. Then I read help(fitdistr) and learned that fitdistr required 3 arguments, the third of which was start: A named list giving the parameters to be optimized with initial values. This can be omitted for some of the named distributions (see Details). The normal distribution was NOT one of the named distributions. When I supplied a named list for start, it worked: fitdistr(x, normal, list(mean=0, sd=1)) mean sd 2.97093013 0.88852969 (0.08885297) (0.06281083) hope this helps. spencer graves Rau, Roland wrote: Hi, -Original Message- From: [EMAIL PROTECTED] [SMTP:[EMAIL PROTECTED] Sent: Sunday, February 15, 2004 10:24 PM To: [EMAIL PROTECTED]; [EMAIL PROTECTED] Subject:Re: [R] Maximum likelihood estimation in R Hello, Use x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) mean sd 2.9331 0.99673982 (0.09967398) (0.07048015) For me, this example does not work. As it looks like copy+paste, I guess this something platform dependent? At least I have to specify some starting values as shown below. But maybe I did something wrong? Best, Roland (I am using at the moment R 1.8.1 on WinNT) x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) Error in fitdistr(x, normal) : 'start' must be a named list fitdistr(x, normal, start=list(mean=1, sd=0.4)) mean sd 3.03167815 1.06637851 (0.10663786) (0.07539617) + This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation in R
normal is in the list in the current versions of MASS, but not that released with R 1.8.1. Try update.packages() in R. On Mon, 16 Feb 2004, Spencer Graves wrote: I got the same error message in R 1.8.1 and S-Plus 6.1. Then I read the error message: 'start' must be a named list. Then I read help(fitdistr) and learned that fitdistr required 3 arguments, the third of which was start: A named list giving the parameters to be optimized with initial values. This can be omitted for some of the named distributions (see Details). The normal distribution was NOT one of the named distributions. When I supplied a named list for start, it worked: fitdistr(x, normal, list(mean=0, sd=1)) mean sd 2.97093013 0.88852969 (0.08885297) (0.06281083) hope this helps. spencer graves Rau, Roland wrote: Hi, -Original Message- From: [EMAIL PROTECTED] [SMTP:[EMAIL PROTECTED] Sent: Sunday, February 15, 2004 10:24 PM To: [EMAIL PROTECTED]; [EMAIL PROTECTED] Subject:Re: [R] Maximum likelihood estimation in R Hello, Use x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) mean sd 2.9331 0.99673982 (0.09967398) (0.07048015) For me, this example does not work. As it looks like copy+paste, I guess this something platform dependent? At least I have to specify some starting values as shown below. But maybe I did something wrong? Best, Roland (I am using at the moment R 1.8.1 on WinNT) x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) Error in fitdistr(x, normal) : 'start' must be a named list fitdistr(x, normal, start=list(mean=1, sd=0.4)) mean sd 3.03167815 1.06637851 (0.10663786) (0.07539617) + This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Maximum likelihood estimation in R
Dear Sir, I am a new user of R and I am doing a tast, which is: find the maximum likelihood estimate of the parameter of Gaussian distribution for generated 100 numbers by using x=rnorm(100, mean=3, sd=1). I tried to use following Maximum Likelihood function fn-function(x) (-50*log((sd(x))^2))-50*log(sqrt(2*pi))-(1/2*((mean(x))^2))*(sum((x-(mean(x))^2)), but it did not work. I am looking for the complete syntax to finish my target task. Thanks for your help. Best regards. Edward Sun Germany __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation in R
Hello, Use x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) mean sd 2.9331 0.99673982 (0.09967398) (0.07048015) Hope this helps, Shrieb __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Maximum likelihood estimation in R
If, however, you are more interested in general methods for maximizing a likelihood function, I suggest you look at optim, work the examples on the help page, etc. hope this helps. spencer graves [EMAIL PROTECTED] wrote: Hello, Use x=rnorm(100, mean=3, sd=1) library(MASS) fitdistr(x, normal) mean sd 2.9331 0.99673982 (0.09967398) (0.07048015) Hope this helps, Shrieb __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Maximum likelihood estimation in R
Or:
library(mle)
?mle
(which, BTW, uses optim() underneath.)
Also, for those not aware of it, fitdistr(x, normal) just computes mean(x)
and (n-1)/n * var(x) and return them. (I can't imagine any reason to do
otherwise for normal distribution.)
Best,
Andy
From: Spencer Graves
If, however, you are more interested in general methods for
maximizing a likelihood function, I suggest you look at optim, work
the examples on the help page, etc.
hope this helps. spencer graves
[EMAIL PROTECTED] wrote:
Hello,
Use
x=rnorm(100, mean=3, sd=1)
library(MASS)
fitdistr(x, normal)
mean sd
2.9331 0.99673982
(0.09967398) (0.07048015)
Hope this helps,
Shrieb
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[R] Maximum Likelihood Estimation and Optimisation
Hello, I want to calculate a maximum likelihood funktion in R in order to solve for the parameters of an estimator. Is there an easy way to do this in R? How do I get the parameters and the value of the maximum likelihood funktion. More, I want to specify the algorithm of the optimisation above: BHHH (Berndt Hall Hall Hausman). Is this possible? Thanks a lot for your help and best regards Marc - Marc Fohr, CFA Equity Portfolio Manager First Private Investment Management Neue Mainzer Strasse 75 D-60311 Frankfurt/Main Phone: ++49 - 69 - 2607 5424 Fax: ++49 - 69 - 2607 5440 Email: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
RE: [R] Maximum Likelihood Estimation and Optimisation
Well, lm() produces an OLS solution, which are also MLE solutions for the fixed effects. I think this is an easy way, although maybe not the best. BHHH is a numerical approximation that can be used when a closed form solution is not available. It is less sophisticated than Newton-Raphson. Is this helpful? -- Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria, Virginia 22314 703.647.1628 -Original Message- From: Fohr, Marc [AM] [mailto:[EMAIL PROTECTED] Sent: Thursday, July 10, 2003 10:17 AM To: [EMAIL PROTECTED] Subject: [R] Maximum Likelihood Estimation and Optimisation Hello, I want to calculate a maximum likelihood funktion in R in order to solve for the parameters of an estimator. Is there an easy way to do this in R? How do I get the parameters and the value of the maximum likelihood funktion. More, I want to specify the algorithm of the optimisation above: BHHH (Berndt Hall Hall Hausman). Is this possible? Thanks a lot for your help and best regards Marc - Marc Fohr, CFA Equity Portfolio Manager First Private Investment Management Neue Mainzer Strasse 75 D-60311 Frankfurt/Main Phone: ++49 - 69 - 2607 5424 Fax: ++49 - 69 - 2607 5440 Email: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
FW: [R] Maximum Likelihood Estimation and Optimisation
Have a look at ?optim. I don't think it has the BHHH algorithm as an option, though. === David Barron Jesus College University of Oxford -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Harold Doran Sent: 10 July 2003 15:43 To: Fohr, Marc [AM]; [EMAIL PROTECTED] Subject: RE: [R] Maximum Likelihood Estimation and Optimisation Well, lm() produces an OLS solution, which are also MLE solutions for the fixed effects. I think this is an easy way, although maybe not the best. BHHH is a numerical approximation that can be used when a closed form solution is not available. It is less sophisticated than Newton-Raphson. Is this helpful? -- Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria, Virginia 22314 703.647.1628 -Original Message- From: Fohr, Marc [AM] [mailto:[EMAIL PROTECTED] Sent: Thursday, July 10, 2003 10:17 AM To: [EMAIL PROTECTED] Subject: [R] Maximum Likelihood Estimation and Optimisation Hello, I want to calculate a maximum likelihood funktion in R in order to solve for the parameters of an estimator. Is there an easy way to do this in R? How do I get the parameters and the value of the maximum likelihood funktion. More, I want to specify the algorithm of the optimisation above: BHHH (Berndt Hall Hall Hausman). Is this possible? Thanks a lot for your help and best regards Marc - Marc Fohr, CFA Equity Portfolio Manager First Private Investment Management Neue Mainzer Strasse 75 D-60311 Frankfurt/Main Phone: ++49 - 69 - 2607 5424 Fax: ++49 - 69 - 2607 5440 Email: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Maximum Likelihood Estimation and Optimisation
It is not obvious to me what parameters in what model you want to fit.
Function optim does very well with many different kinds of problems.
If you just want to estimate parameters of a probability distribution,
function fitdistr in library(MASS) will do that. A couple of days
ago, I needed to fit a Pareto distribution of the first kind. A
search of www.r-project.org - search - R site search uncovered
functions for a Pareto distribtion of a different kind. So, I wrote the
following and used them to check fitdistr and then to actually fit the
distribution to data.
hope this helps. spencer graves
#
dpareto -
function(x, shape, x0, log=FALSE){
dp - if(log) (log(shape-1)-shape*log(x/x0)-log(x0))
else ((shape-1)*((x0/x)^shape)/x0)
dp[xx0] - 0
dp
}
ppareto -
function(q, shape, x0, lower.tail = TRUE, log.p=FALSE){
q - pmax(x0, q)
if(log.p){
if(lower.tail){
return(log(ppareto(q, shape, x0)))
}
else return((shape-1)*log(x0/q))
}
else{
S.q - (x0/q)^(shape-1)
if(lower.tail)return(1-S.q)
else return(S.q)
}
}
qpareto -
function(p, shape, x0, lower.tail=TRUE){
if(lower.tail) p - (1-p)
x0*exp(-log(p)/(shape-1))
}
rpareto -
function(n, shape, x0)
qpareto(runif(n), shape, x0, lower.tail=FALSE)
fitdistr(rpareto(1, 3, 1), dpareto, list(shape=2.5), x0=1)
Harold Doran wrote:
Well, lm() produces an OLS solution, which are also MLE
solutions for the fixed effects. I think this is an easy
way, although maybe not the best.
BHHH is a numerical approximation that can be used when
a closed form solution is not available. It is less
sophisticated than Newton-Raphson.
Is this helpful?
--
Harold C. Doran
Director of Research and Evaluation
New American Schools
675 N. Washington Street, Suite 220
Alexandria, Virginia 22314
703.647.1628
-Original Message-
From: Fohr, Marc [AM] [mailto:[EMAIL PROTECTED]
Sent: Thursday, July 10, 2003 10:17 AM
To: [EMAIL PROTECTED]
Subject: [R] Maximum Likelihood Estimation and Optimisation
Hello,
I want to calculate a maximum likelihood funktion in R in
order to solve for the parameters of an estimator. Is there
an easy way to do this in R? How do I get the parameters and
the value of the maximum likelihood funktion.
More, I want to specify the algorithm of the optimisation
above: BHHH (Berndt Hall Hall Hausman). Is this possible?
Thanks a lot for your help and best regards
Marc
-
Marc Fohr, CFA
Equity Portfolio Manager
First Private Investment Management
Neue Mainzer Strasse 75
D-60311 Frankfurt/Main
Phone: ++49 - 69 - 2607 5424
Fax: ++49 - 69 - 2607 5440
Email: [EMAIL PROTECTED]
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[R] Maximum likelihood estimation of a mixture of two weibull distribution
Hi, I would like to estimate the parameters of a mixture of two Weibull distributions by the maximum likelihood method. Is it possible to do it with fitdist? Thanks IF [[alternate HTML version deleted]] __ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
[R] Maximum likelihood estimation of a mixture of two weibull distribution
Hi, I would like to estimate the parameters of a mixture of two Weibull distributions by the maximum likelihood method. Is it possible to do it with fitdist? Thanks IF [[alternate HTML version deleted]] __ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
