similar to: Checking if a matrix exists/is defined

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, everyone! I picked up R several months ago and have adopted it as my choice for statistical programming. Coming from a Java background, I can honestly say that R is not only free, it is better tha S-plus: the lexical scope in R makes it very simple to simulate Java's object model. For this, I encourage everyone to read the artcle: Robert Gentleman and Ross Ihaka (2000) "Lexical

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.

Hi I'm having problems getting the "factorized" argument in solve.QP (part of the quadprog library) to work as expected. The helpfile states that when the factorized argument is set to TRUE, then the function requires the inverse of a square-root factor of the Hessian instead of the Hessian itself. That is, when factorized=TRUE, the Dmat argument should be a matrix R^(-1), such

Hello expeRts, I need generate symbolize the autocovariances matrix of a Gaussian ARMA(1,1), for derivate it and evaluate. I try this codes, but whitout sucess vacv<-NULL vacv[1]<-1-2*phi*theta-theta^2 vacv[2]<-(1-phi*theta)*(phi-theta) vacv[3:n]<-acv[2]*(phi^(1:(n-2))) facv<-list() for(i in 1:2)

Hello R gurus, I have the code below for which i need help and pointers to make it run in parallel on a dual core win7 computer with R 2.13.x, using foreach, iterators,doMC. library(scatterplot3d) # Loads 3D library. library(fields) library(MASS) library(ROCR) library(verification) library(caret) library(gregmisc) ##simulated data d=replicate(9, rnorm(40)+10)

Given a vector : pvec=(p1,p2,.... p J) with sum(pvec)=1, all the elements are non-negative, that is, they are probabilities a matrix A ( N* J ), with the elements alpha(ij) are 0 or 1 I want to MAXIMIZE THE RESULT RESULT= product( i=1, to N [ sum ( alpha(ij)* pj , j =1,to J ) ] ) thus, I need to get pvec. how should I do ? for example

Hi, I'm using maxlik with functions specified (L, his gradient & hessian). Now I would like determine some robust standard errors of my estimators. So I 'm try to use vcovHC, or hccm or robcov for example but in use one of them with my result of maxlik, I've a the following error message : Erreur dans terms.default(object) : no terms component Is there some attributes

Dear participants to the list, this is my problem: I want to obtain an expression that represents the second derivative of one function. With "deriv3" (package "stats") it is possible to evaluate the second derivative, but I do not know how I can get the (analytical) expression of this derivative. For example: Suppose that I have a function of this form:

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,

OK Thanks. Your response made me think. Here (the last line) is what I need: set.seed(76543211) w<-1:10; w a<-matrix(rpois(20,2),nrow=10); a t(w*a)%*%a On 6/8/2017 12:09 PM, Jeff Newmiller wrote: > Is this a question? You seem to have three possible calculations, have already implemented two of them (?) and it is unclear (to me) what you think the right answer for any of them is

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi, R-folks: I have been tryin many combination of parameter to make Matern variogram to work, but I can't find the available one. I'm near to be crazy. I tiped: A?o2003Selg.lf<-likfit(A?o2003Selg,cov.model="matern",ini.cov.pars=c(1.5,14),kappa=2.5,fix.kappa=FALSE,nugget=0.08,lambda=0.008,fix.lambda=FALSE,hessian=TRUE) the hessian shows: $hessian [,1]

In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); if (*info == 0){ F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job); ........ This usually works OK, but with an ill-conditioned data

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

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

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

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?

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