similar to: Hessian in box-constraint problem - concern OPTIM function

Hi I am trying to recover the hessian of a problem optimised with box-constraints. The problem is that in some cases, my estimates are very close to the boundary, which will make optim(..., hessian=TRUE) or optimHessian() fail, as they do not follow the box-constraints, and hence estimate the function in the unfeasible parameter space. As a simple example (my problem is more complex though,

hello all, when I start up the R and I execute o follow code: > ls() character(0) > x=123 > assign("test_x", x, envir = .GlobalEnv ) > ls() [1] "test_x" "x" > setwd('C:\\R\\etc') > save.image('TEST.RData') > q('no') I have two different behaviours: (a) - when I start up R again by "double click" in

R users; A question about optimization within R. I've been using both optim() and nlminb() to estimate parameters and all seems to be working fine. For context (but without getting into specifics - sorry), I'm working with a problem that is known to have correlated parameters, and parameter estimation can be difficult. I have a question on optim() - I'm using

Hi, my name is Marcel R. Lopes. My problem is, I made a code to calculate the estimates of a Cox model with random effects. Used to optimize the R command for this. The estimates were calculated correctly, but the Hessian matrix does not have good values. The same thing was done in SAS and gave good results for the Hessian Matrix. Where is the problem in R? As the Hessian is calculated?. How

Hi R-help! I'm trying to understand how R's nlm function updates its estimate of the Hessian matrix. The Dennis/Schnabel book cited in the references presents a number of different ways to do this, and seems to conclude that the positive-definite secant method (BFGS) works best in practice (p201). However, when I run my code through the optim function with the method as "BFGS",

Hi, I would like to get all X iterations in optim output in matrix form. I know about the follow approach: sink("reportOptim") optim( ......., control=list( trace=6,..........) ) sink() all_iterOptim <- readLines("reportOptim") unlink("reportOptim") all_iterOptim <- all_iterOptim[ grep( '^X', all_iterOptim ) ] ### TODO: the rest !!! :-) But it is very

hello all, where I find a example or tutorial of RGtk2 package? I would like to know about the gladeXML functions in R. thanks in advance Cleber Borges

Dear R Users Hi, I have very emergency problems in my programming about finding MLE with optim command. I reproduced it with real data. I guess that my function object in optim is very sensitive because it has power function . Then optim give me lower or initial values for estimates with these warnings for Hessian matrix computation: 1: In log(B2 * (C2^(y + v))) : NaNs produced 2: In log(B3

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

Hello! Looking on how people use optim to get MLE I also noticed that one can use returned Hessian to get corresponding standard errors i.e. something like result <- optim(<< snip >>, hessian=T) result$par # point estimates vc <- solve(result$hessian) # var-cov matrix se <- sqrt(diag(vc)) # standard errors What is actually Hessian representing here?

Hi, I am working or arima. I think arima uses non-linear optimisation for parameter optimisation. The standard error for parameters are computed from hessian matrix. When I use arima model, how can I see the finial hessian got from non-linear optimisation (BFGS for example). Any help is appreciated. Many thanks. Di [[alternative HTML version deleted]]

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

Dear All, I am trying to find MLE by using "OPTIM" function. Difficult in differentiating some parameter in my objective function, I would like to use the returned hessian matrix to yield an estimate of Fisher's Information matrix. My question: Since the hessian is calculated by numerical differentiate, is it a reliable estimate? Otherwise I would have to do a lot of work to

This is a very basic question, but I am a bit confused with optim. I want to get the MLEs using optim which could replace the newton-raphson code I have below which also gives the MLEs. The function takes as input a vector x denoting whether a respondent answered an item correctly (x=1) or not (x=0). It also takes as input a vector b_vector, and these are parameters of test items (Rasch estimates

Hi, I tried to use MethComp library and this library make use of the WinBUGS by R2WinBuGUS, but I get the follow error in bugs.run: *Error in bugs.run(n.burnin, bugs.directory, WINE = WINE, useWINE = useWINE, : * Look at the log file and try again with 'debug=TRUE' to figure out what went wrong within Bugs. Anyone can help-me, please? Thanks Cleber library( MethComp ) library(

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,

Hi, dear R users I am a newbie in R and I wantto use the method of meximum likelihood to fit a Weibull distribution to my survival data. I use "optim" as follows: optim(c(1, 0.25),weibull.like,mydata=mydata,method="L-BFGS-B",hessian = TRUE) My question is: how do I setup the constraints so that the two parametrs of Weibull to be pisotive? Or should I use other function

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

Dear R experts! I try to minimize a function with external C fitting function. I get the hessian matrix. Here it is: [,1] [,2] [,3] [,4] [1,] 1.8816631 0 0.8859803 0 [2,] 0.0000000 0 0.0000000 0 [3,] 0.8859803 0 0.4859983 0 [4,] 0.0000000 0 0.0000000 0 Second and fourth rows/columns have zero values only. That's OK, because that ones related

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can