similar to: Optim function & Hessian matrix

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?

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

Hi, Is there any way to get the hessian matrix from the "constrOptim" function without supplying gradient function? Thanks. --------------------------------- Bring words and photos together (easily) with [[alternative HTML version deleted]]

I am new to 'R' and also new to the concept of a 'Hessian' with non-linear optimization. I would like to avoid going through all of the reference articles given with ?optim as access to a library is not handy. Would someone be able to elighten me on what is in the Hessian matrix if 'hessian = TRUE' with optim? The documentation indicates that this is a numerically

Hi, R developers. when running mle inside a loop I found a nasty behavior. From time to time, my model had a degenerate minimum and the loop just crashed. I tracked it down to "vcov <- if (length(coef)) solve(oout$hessian)" line, being the hessian singular. Note that the minimum reached was good, it just did not make sense to calculate the covariance matrix as the inverse of a

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

Hello all useRs, I am using the OPTIM function with particular interest in the method L-BFGS-B, because it is a box-constraint method. I have interest in the errors estimates too. I make: s.e. <- sqrt( diag( solve( optim(...,method='L-BFGS-B', hessian=TRUE)$hessian ))) but in help say: "Note that this is the Hessian of the unconstrained problem even if the box constraints

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

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

Dear R experts, In R-intro, under the 'Nonlinear least squares and maximum likelihood models' there are ttwo examples considered how to use 'nlm' function. In 'Least squares' the Standard Errors obtained as follows: After the fitting, out$minimum is the SSE, and out$estimates are the least squares estimates of the parameters. To obtain the approximate standard

Dear all R users, Is there any way to calculate hessian matrix of a given function at any given point? Regards [[alternative HTML version deleted]]

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]]

Dear R-users, In the function constrOptim there is an option to get an approximation to the hessian of the surrogate function R at MLE by declaring hessian=TRUE in the calls to the function optim. I would like to ask if it is advisable to get an approximate hessian for the funcrion f as follows: f''(theta)=R''(theta|theta_k)-B''(theta) where

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

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",

Hello R users, I am trying to get the Std. errors of some estimators using the optim function. But when computing them using sqrt(diag(solve(ans$hessian))) I get an error that says... Lapack routine dgesv: system is exactly singular. Or sometimes I also get that system is computationally singular. When I check the Hessian matrix it has at least one column&row of mostly zeros. My

Hi! I have three models. In the first model, everything is fine. However, in the second and third models, I have NA's for standard errors: The hessians also have NaN's (same for m2 and m3). What should I do about it? It there a way to obtain the hessian without transforming my variables? I will greatly appreciate your help! -- View this message in context:

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 I need to find the Hessian matrix for a complicated function from a certain kind of data but i keep getting this error Error in f1 - f2 : non-numeric argument to binary operator the data is given by U<-runif(n) Us<-sort(U) tau1<- 2 F1tau<- pgamma((tau1/theta1),shape,1) N1<-sum(Us<F1tau) X1<- Us[1:N1]

Dear Helper, I used "constrOptim.nl" and got the value of par. The estimations looks good even if the number of iterations is only 16. But the values of hessian and convergence are both "NULL". I tested the objective function and gradient function by "optim" and didn't see any problem there. With these functions, "optim" gives the convergence value