similar to: pass data to log-likelihood function

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

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

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) {

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string

So, I've embarked on my threatened modifications to the mle subset of the stats4 package. Most of what I've done so far has *not* been adding the slick formula interface, but rather making it work properly and reasonably robustly with real mle problems -- especially ones involving reasonably complex fixed and default parameter sets. Some of what I've done breaks backward

Hi I am doing several mle and want to store them in a list (or whatever is the right construct) to be able to analyse them later. at the moment I am doing: f <- list() f$IP <- mle(...) f$NE <- mle(...) but when I say: > summary(f) I get: Length Class Mode IP 0 mle list NE 0 mle list I don't get the output I would have, i.e. the one from >

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

my problem actually arised with fitting the data to the weibulldistribution, where it is hard to see, if the proposed parameterestimates make sense. data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 how am I supposed to know what starting values i have to take? i get different

I have an issue using mle in versions of 32 bits. I am writing a package which I want to submit to the CRAN. When doing the check, there is an example that has an error running in the 32 bits version. The problem comes from the mle function, using it with a lower constrain. In 64 bits version it works fine but when I put it in the R 32 bits it fails. (same numbers, all equal!) The call is:

[posted to R-devel, no discussion: resubmitting it as a bug, just so it gets logged appropriately] Sorry to report further difficulties with nls and profiling and constraints ... the problem this time (which I didn't check for in my last round of testing) is that the nls profiler doesn't seem to respect constraints that have been set when using the port algorithm. See test code

Hi the list, I would need some advice on something that looks like a FAQ: the possibility of providing vectors to optim() function. Here is a stupid and short example summarizing the problem: -------------------------------- example 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <-

Hi, When I run the following code, r <- c(3,4,4,3,5,4,5,9,8,11,12,13) n <- rep(15,12) x <- c(0, 1.1, 1.3, 2.0, 2.2, 2.8, 3.7, 3.9, 4.4, 4.8, 5.9, 6.8) x <- log10(x) fr <- function(c, alpha, beta) { P <- c + (1-c) * pnorm(alpha + beta * x) P <- pmax(pmin(P,1),0) -(sum(log(choose(n,r))) + sum(r * log(P)) + sum((n -r)* log(1-P))) } fit <- mle((fr), start = list(c

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.

> On May 27, 2018, at 10:31 PM, francesc badia roca <fbr600 at gmail.com> wrote: > > I have an issue using mle in versions of 32 bits. > > I am writing a package which I want to submit to the CRAN. > When doing the check, there is an example that has an error running in the > 32 bits version. > > The problem comes from the mle function, using it with a lower

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

hello my data is data2:2743 4678 21427 6194 10286 1505 12811 2161 6853 2625 14542 694 11491 14924 28640 17097 2136 5308 3477 91301 11488 3860 64114 14334 by calculating shape<-(mean(data2))^2/var(data2) scale<-var(data2)/mean(data2) i get the idea what the parameters of the gammadistribution would be. but if i try using the method mle() i get stock and i don't know, how to

Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me. thank you for you atention. Bernardo. > erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE) > head(erizo) EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5 > TAN <-

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Hi there, I ran the following code: vols=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Swaption vols.csv" , header=TRUE, sep=",") X<-ts(vols[,2]) #X dcOU<-function(x,t,x0,theta,log=FALSE){ Ex<-theta[1]/theta[2]+(x0-theta[1]/theta[2])*exp(-theta[2]*t) Vx<-theta[3]^2*(1-exp(-2*theta[2]*t))/(2*theta[2]) dnorm(x,mean=Ex,sd=sqrt(Vx),log=log) }

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