search for: neglls

Hi, I'm trying to find the maximum (likelihood) of a function. Therefore, I'm trying to minimize the negative likelihood function: # params: vector containing values of mu and sigma # params[1] - mu, params[2]- sigma # dat: matrix of data pairs y_i and s_i # dat[,1] - column of y_i , dat[,2] column of s_i negll <- function(params,dat,constant=0) { for(i in 1:length(dat[,1])) {

Dear R-Users, I am trying to use mle() to optimize two (or more) parameters, but I want to specify those parmeters in a data frame rather than having to spell them out separately in the "start" variable of mle(). My call is > mle(negll, start=list(aps=init), fixed=list(measphot=newphot, formod=formod, Nbands=Nbands), method="BFGS") where negll is a function I have

This message is about a curious difference in timing between two ways of computing the same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to be a factor of >1000. The code is below. We would be grateful if anyone can point out any egregious bad practice in our code, or enlighten us on why one approach is so much slower than the other. The problem

Dear helpers, I am using R version 2.5.1 to estimate a multinomial logit model using my own maximum likelihood function (I work with share data and the default function of R cannot deal with that). However, the computer (I have an Athlon XP 3200+ with 512 GB ram) takes quite a while to estimate the model. With 3 categories, 5 explanatory variables and roughly 5000 observations it takes 2-3 min.

Hi All, a colleague wants to calculate the Fisher information matrix for a model he wrote (not in R). He can easily get the neg-log-likelihood and the best fit parameters at the minimum. He can also get negLLs for other parameter values too. Given these data, is there a way in R to calculate the Fisher information matrix? Best, Federico -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 7594 1602...

Hi Gregg, Below I try to address 1) The sum constraint would apply for each set ?? and ?? i.e. sum(??) = sum(??) = 1.60 2) Just like 1) the lower and upper bounds will be applied for individual set i.e. individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) and individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) I hope that I am

Hello Christofer, Just writing with a detailed follow-up. Attached is a script I was able to get running with a bit of work. I did not include the script in the ext of this email. It is only attached. Optimization Progress We were initially aiming to solve the dual-slope constrained ordinal model using nloptr's SLSQP algorithm (NLOPT_LD_SLSQP), since it supports: ? Box constraints (per-?