similar to: (no subject)

Mzabalazo Ngwenya wrote: > Hi everyone ! > > I'm am trying to fit a kriging model to a set of data. When I just run > the "likfit" command I can obtain the results. However when I try to > pass additional arguements to the optimization function "optim" I get > errors. That is I want to obtain the hessian matrix so matrix > (hessian=TRUE). >

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, 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

Hi everyone !   I'm am trying to fit a kriging model to a set of data. When I just run the "likfit" command I can obtain the results. However when I try to pass additional arguements to the optimization function "optim" I get errors. That is I want to obtain the hessian matrix so matrix (hessian=TRUE).   Heres a little example( 1-D). Can anyone shed some light?  Where am I

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?

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

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

Hi all, I am trying to use mle() to find a self-defined function. Here is my function: test <- function(a=0.1, b=0.1, c=0.001, e=0.2){ # omega is the known covariance matrix, Y is the response vector, X is the explanatory matrix odet = unlist(determinant(omega))[1] # do cholesky decomposition C = chol(omega) # transform data U = t(C)%*%Y WW=t(C)%*%X beta = lm(U~W)$coef Z=Y-X%*%beta

Dear R users, I am trying to carry out MLE of the time-varying CAPM using the FKF package. My approach so far has been to try and adapt the example given in the help file found using ?fkf which demonstrates the MLE of an ARMA(2,1) model. When I attempt to run my R code (given below) I get the following error: Error in fkf(a0 = sp$a0, P0 = sp$P0, dt = sp$dt, ct = sp$ct, Tt = sp$Tt, : Some of

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 have a model with 5 parameters that I am optimising where the (best) value of the objective function is negative. I would like to use the Hessian matrix (from genoud and/or optim functions) to construct the covariance and correlation matrices. This is the code that I am using: est <- out$par # Parameter estimates H <- out$hessian # Hessian V <-

Hi there, I am trying to run the following code: > 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) + } > OU.lik<-function(theta1,theta2,theta3){ + n<-length(X) + dt<-deltat(X) +

Hi all, I would like to use optim() to estimate the equation by the log-likelihood function and gradient function which I had written. I try to use OPG(Out Product of Gradient) to calculate the Hessian matrix since sometime Hessian matrix is difficult to calculate. Thus I want to pick the Gradient matrix from the gradient function. Moreover, could R show the process of calculation on gradient

Hello again Max. A correction to my response from yesterday. Things were better than they seemed. I thought it over, checked Arellano's panel book and Driscoll and Kraay (Rev. Econ. Stud. 1998) and finally realized that vcovSCC does what you want: in fact, despite being born primarily for dealing with cross-sectional correlation, 'SCC' standard errors are robust to "both

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,

I care a lot about R's speed. So I decided to give REvolution's R (http://revolution-computing.com/) a try, which bills itself as an optimized R. Note that I used the free version. My machine is a Intel core 2 duo under Windows XP professional. The code I run is in the end of this post. First, the regular R 1.9. It takes 2 minutes and 6 seconds, CPU usage 50% Next, REvolution's R.

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

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

Good Afternoon, I am relatively new to R and have been trying to figure out how to estimate regression coefficients using the MLE method. Some background: I am trying to examine scenarios in which certain estimators might be preferred to others, starting with MLE. I understand that MLE will (should) produce the same results as Ordinary Least Squares if the assumption of normality holds. That

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is: