[R] MLE Function

I am just trying to teach myself how to use the mle function in R because it is much better than what is provided in MATLAB. I am following tutorial material from the internet, however, it gives the following errors, does anybody know what is happening to cause such errors, or does anybody know

Re: [R] MLE Function

Terence Broderick wrote: I am just trying to teach myself how to use the mle function in R because it is much better than what is provided in MATLAB. I am following tutorial material from the internet, however, it gives the following errors, does anybody know what is happening to cause such

[R] MLE for Student's t-distribution

Hi is there an easy way/ R-function to calculate the numerical maximum likelihood estimators for a Student's t-distribution? I searched the mailing list archive the last 30mins but didn't find an answer. Regards Ben __ R-help@stat.math.ethz.ch mailing

Re: [R] MLE for Student's t-distribution

On Wed, 15 Nov 2006, Benjamin Dickgiesser wrote: Hi is there an easy way/ R-function to calculate the numerical maximum likelihood estimators for a Student's t-distribution? I searched the mailing list archive the last 30mins but didn't find an answer. See fitdistr() in MASS. MLE of what,

Re: [R] MLE for Student's t-distribution

I need to estimate all parameters (except maybe df). Thank you for pointing me into a direction, I will have a look. The aim is to use a fat-tail distribution to calculate the Value At Risk instead of using the Normal distribution. Ben On 11/15/06, Prof Brian Ripley [EMAIL PROTECTED] wrote: On

[R] MLE Methods

Greetings Forum, I am new to R and and writing in hopes of getting some help. Our MLE results from a home grown software do not match with that of R. We are using a censored sample and will really appreciate if you could give us any pointers as to which MLE method is used in R... to my

Re: [R] MLE Methods

nand kumar nksathyar at yahoo.com writes: Greetings Forum, I am new to R and and writing in hopes of getting some help. Our MLE results from a home grown software do not match with that of R. We are using a censored sample and will really appreciate if you could give us any pointers

Re: [R] MLE and QR classes

I don't understand your question. First, I'm not familiar with the 'wls' package; I found no such package by that name via www.r-project.org - CRAN - (select a local mirror) - Packages. The 'qr' function in the 'quandreg' package looks straightforward to me. Have you

[R] MLE and QR classes

Hi, I load my data set and separate it as folowing: presu - read.table(C:/_Ricardo/Paty/qtdata_f.txt, header=TRUE, sep=\t, na.strings=NA, dec=., strip.white=TRUE) dep-presu[,3]; exo-presu[,4:92]; Now, I want to use it using the wls and quantreg packages. How I change the data classes for mle

[R] MLE and QR classes

Hi, I load my data set and separate it as folowing: presu - read.table(C:/_Ricardo/Paty/qtdata_f.txt, header=TRUE, sep=\t, na.strings=NA, dec=., strip.white=TRUE) dep-presu[,3]; exo-presu[,4:92]; Now, I want to use it using the wls and quantreg packages. How I change the data classes for mle

[R] MLE maximum number of parameters

Hi All, I would like to know, is there a *ballpark* figure for how many parameters the minimisation routines can cope with? I'm asking because I was asked if I knew. Cheers, Federico -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St. Mary's Campus

Re: [R] MLE maximum number of parameters

Federico Calboli f.calboli at imperial.ac.uk writes: Hi All, I would like to know, is there a *ballpark* figure for how many parameters the minimisation routines can cope with? I think I would make a distinction between theoretical and practical limits. A lot depends on how fast

Re: [R] MLE maximum number of parameters

It really depends on how well-behaved your objective function is, but I've been able to fit a few models with 10--15 parameters. But I felt like I was stretching the limit there. -roger Federico Calboli wrote: Hi All, I would like to know, is there a *ballpark* figure for how many

Re: [R] MLE maximum number of parameters

Applications with lots of parameters also tend to have parameters in a relatively small number of families, and each of these few families could be considered to have a distribution. Splines, for example, have lots of parameters -- sometimes more parameters than observations (as do

Re: [R] MLE maximum number of parameters

Seagulls have a very different perspective to ballparks than ants. Nonetheless, there is something that can be said. There are several variables in addition to the number of parameters that are important. These include: * The complexity of the likelihood * The number of observations in the

Re: [R] MLE maximum number of parameters

I regularly optimize functions of over 1000 parameters for posterior mode computations using a variant of newton-raphson. I have some favorable conditions: the prior is pretty good, the posterior is smooth, and I can compute the gradient and hessian. albyn On Mon, Jun 19, 2006 at 06:53:00PM

[R] mle package

Hi ! There used to be a package called mle for maximum likelihood estimation. I couldn't find it when I tried to get the package. Is this still available? Perhaps under another package? I'd appreciate any suggestion on this. Alex __

Re: [R] mle package

/337015 Web: http://www.med.kuleuven.be/biostat/ http://www.student.kuleuven.be/~m0390867/dimitris.htm - Original Message - From: Alexander Nervedi [EMAIL PROTECTED] To: r-help@stat.math.ethz.ch Sent: Tuesday, May 02, 2006 9:20 AM Subject: [R] mle package Hi ! There used

Re: [R] MLE

vkatoma at cs.uct.ac.za writes: Hi , I want to compute the MLE for a simple sample of data, say 45,26,98,65,25,36,42,62,28,36,15,48,45, of which I obviously have the mean and the sd. Is there a way of calling the log normal and already diffrentiated formula other than entering the

[R] MLE

Hi , I want to compute the MLE for a simple sample of data, say 45,26,98,65,25,36,42,62,28,36,15,48,45, of which I obviously have the mean and the sd. Is there a way of calling the log normal and already diffrentiated formula other than entering the whole formula. Victor

[R] mle.

hi all, suppose I have: r[i] ~ N(0, h[i]) h[i] = a + b*r[i-1] + c*h[i-1] for all i=2..n I want to get estimates of a, b, c by mle. Can you tell me how to do that? thanks in advance, Arun [[alternative HTML version deleted]] __

Re: [R] mle.

Arun Kumar Saha arun.kumar.saha at gmail.com writes: hi all, suppose I have: r[i] ~ N(0, h[i]) h[i] = a + b*r[i-1] + c*h[i-1] for all i=2..n I want to get estimates of a, b, c by mle. Can you tell me how to do that? (1) can you convince us this isn't a homework

[R] MLE

Dear All, Can somebody tell me how to do Maximum Likelihood Estimation in R for Non-linear function? My function is non-linear and it has four parameters, only one explanatory variable. If possible Please tell me the source so that I can write my own code for above. Thanks, GS

[R] MLE with optim

Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the bug is? test = rlnorm(100,5,3) logL= function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start= list(meanlog=5, sdlog=3)

Re: [R] MLE with optim

Carsten Steinhoff wrote: Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the bug is? test = rlnorm(100,5,3) logL= function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start=

Re: [R] MLE with optim

- From: Carsten Steinhoff [EMAIL PROTECTED] To: r-help@stat.math.ethz.ch Sent: Wednesday, June 29, 2005 5:19 PM Subject: [R] MLE with optim Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the bug is? test

[R] MLE for two random variables

Hello, I've the following setting: (1) Data from a source without truncation (x) (2) Data from an other source with left-truncation at threshold u (xu) I have to fit a model on these these two sources, thereby I assume that both are drawn from the same distribution (eg

Re: [R] MLE for two random variables

Just to make sure, do you have any information on events when xu u, i.e. do you know how many such events and you know u for those events? If yes, then that's called censoring, not truncating. For that, the survival package seems pretty good. I found the information in Venables and

[R] MLE: Question

Hi R users! I have a likelihood ratio statistic that depends on a parameter delta and I am trying to get confidence intervals for this delta using the fact that the likelihood ratio statistic is approx. chi-squared distributed. For this I need to maximize the two likelihoods (for the ratio

Re: [R] MLE: Question

[EMAIL PROTECTED] writes: Hi R users! I have a likelihood ratio statistic that depends on a parameter delta and I am trying to get confidence intervals for this delta using the fact that the likelihood ratio statistic is approx. chi-squared distributed. For this I need to maximize the

[R] mle() and with()

I'm trying to figure out the best way of fitting the same negative log-likelihood function to more than one set of data, using mle() from the stats4 package. Here's what I would have thought would work: -- library(stats4) ## simulate values r = rnorm(1000,mean=2) ## very basic

Re: [R] MLE, precision

Dear Spencer: My problem get solved by using Matlab. It runs pretty quick(less than 5 seconds)and the result is stable with respect to the initial values. I was amaized. Here my t and are as long as 2390, sum the functions over t and d, the function becomes daunting. But I still like to try

Re: [R] MLE, precision

1. Don't use t as a variable name. It is the name of the matrix transpose function. In most but not all contexts, R is smart enough to tell whether you want the system function or the local object. 2. I can't tell from your question what you want. PLEASE do read the posting

[R] MLE, precision

Hi, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l- function(m1,m2,b) -sum(d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) )

Re: [R] MLE, precision

Have you considered estimating ln.m1, ln.m2, and ln.b, which makes the negative log likelihood something like the following: l.ln- function(ln.m1,ln.m2,ln.b){ m1 - exp(ln.m1); m2 - exp(ln.m2); b - exp(ln.b) lglk - d*( ln.m1 + ln.m2 + log1p(-exp(-(b+m2)*t) +

[R] mle in the gamma model

Dear [R]-list, I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the scale parameters of gamma data. I have a vector of iid gamma rv : data=rgamma(100,shape=10,scale=3) and a vector of their weights:

Re: [R] mle in the gamma model

library(MASS) ?fitdistr You can also use survreg (survival) and glm (with some help from functions in MASS for the shape). On Mon, 24 Nov 2003, Dominique Couturier wrote: I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the