similar to: Repeating an MLE experiement

Hi, Actually I am facing a similar problem. I would like to fit both an ordinary (symmetric) and a non-central t distribution to my (one-dimensional) data (quite some values.. > 1 mio.). For the symmetric one, fitdistr or funInfoFun (using fitdistr) from the qAnalyst package should do the job, and for the non-central one.. am I right to use gamlss(x ~ 1, family=GT()) ? Anyway, I am a little

Hi All I have a data frame called "nbd" with two columns (x and T). Based on this dataset I want to find the parameters of a distribution with the following log-liklihood function and with r and alpha as its parameters: log(gamma(nbd$x+r))-log(gamma(r))+r*log(alpha)-(r+nbd$x)*log(nbd$T+alpha) the initial value for both parameters is 1. I would be thankful if you could help me

Please how can one go about this one? I don't know how to go about it. [[alternative HTML version deleted]]

I'm trying to calculate the maximum likelihood estimate for a binomial distribution. Here is my code: y <- c(2, 4, 2, 4, 5, 3) n <- length(y) binomial.ll <- function (pi, y, n) { ## define log-likelihood output <- y*log(pi)+(n-y)*(log(1-pi)) return(output) } binomial.mle <- optim(0.01, ## starting value binomial.ll,

Dear Sir, I am using R to estimate two parameters in Normal distribution. I generated 100 normal distributed numbers, on which to estimate the parameter. The syntax is: >fn<-function(x)-50*log((y)^2)+50*log(2*pi)-(1/2*(z^2))*(sum((x-y)^2)) >out<-nlm(fn, x, hessian=TRUE) but it does not work. Could you please help me to compose the syntax for the purpose that find maximum

Thank you very much. now, i call mle(minuslogl=loglik, start=start, method <<- method, fixed=list()) in the mle.wrap() function, and the profile.mle() worked. however, it created a variable named "method" in user workspace. if there had been a variable with same name, then the value of that variable would be destroyed. Is there a way to avoid that happen? Thanks again.

A while back, I wrote to the list/engaged in some debate with Peter Dalgaard about the mle() function in the stats4 package -- in particular, I wanted it to have a data= argument so that parameters could be estimated for different sets of data with the same minuslogl function: Peter disagreed, suggesting that a function-defining-function (e.g. something like minusloglfun <- function(data) {

I learned R & MLE in the last few days. It is great! I wrote up my explorations as http://www.mayin.org/ajayshah/KB/R/mle/mle.html I will be most happy if R gurus will look at this and comment on how it can be improved. I have a few specific questions: * Should one use optim() or should one use stats4::mle()? I felt that mle() wasn't adding much value compared with optim, and

I sent this in first on 30 July. Now that UseR! is over I'm trying again (slightly extended version from last time). With R 2.5.1 or R 2.6.0 (2007-08-04 r42421) "L-BFGS-B" behaves differently from all of the other optim() methods, which return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. This is not a bug in

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Full_Name: S?bastien Villemot Version: 2.4.0 OS: Debian testing Submission from: (NULL) (62.212.121.128) Hi, In the mle() function of the stats4 package, there is a bug in the ordering of the variables given in the 'start' argument. By just changing the order of the variables listed in the 'start' list (the initialization values), it is possible to obtain different estimation

Hi, I am looking for some help regarding the use of the mle function. I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that have been defined in the the log-likelihood equation as theta0=theta[1], theta1=theta[2] and theta2=theta[3]. My R code for mle is: mle(Poisson.lik, start=list(theta=c(20,1,1), method="Nelder-Mead", fixed=list(w=w, t1=t1, t2=t2)) But I keep

With the exception of "L-BFGS-B", all of the other optim() methods return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. I don't think this is a "bug" in L-BFGS-B (more like a response to an undefined condition), but it leads to a bug in stats4::mle -- a spurious error saying that a better fit has been

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 neg. log likelihood function mll <- function(mu,logsigma) {

Hello fellow R users, Below are two cases using the "mle" operation from the stats4 package. In CASE 1 the code runs fine, in CASE 2 errors occur: CASE 1 x, alpha_current, s, and n are vectors of the same length. ll_beta<-function(b0=0,b1=0) -sum(s*b0+s*b1*x+s*alpha_current-n*log(1+exp(b0+b1*x+alpha_current))) fit_beta<-mle(ll_beta) CASE 2 The error message is as follows

I made one typo in my previous mail.   May I ask one statistics related question please? I have one query on MLE itself. It's property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is "Consistent" as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it

Dear Mr. Matthieu Stigler i so excited for your package 'tsDyn'. firstly introduce myself, i student at Gadjah Mada University,Indonesia. i'am new user of R and applying it for solving Bi-Variate ( interest rate and inflation ) with threshold vector error correction model. now, i writing my final examination about threshold vector error correction model and i use refference from paper

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) { loglik.fun <- match.fun(FUN)

Dear All, There are two things about mle() that I wasn't so sure. 1) can mle() handle vector based parameter? say ll<-function(theta=rep(1,20)){..............} I tried such function, it worked for "optim" but not for "mle". 2) is there a general suggestion for the maximum number of parameters allowed to use in mle() or optim()? Thank you. Regards, MJO

Hi, Assuming I have a time series on which I will perform rolling-window MLE. In other words, if I stand at time t, I'm using points t-L+1 to t for my MLE estimate of parameters at time t (here L is my rolling window width). Next, at t+1, I'll do the same. My question is that is there anyway to avoid performing MLE each time like does the above. My impression is that rolling from point t