similar to: Optimization in R

Hi all, I wrote a function called relist, which is an inverse to the existing unlist function: http://www.econ.upenn.edu/~clausen/computing/relist.R Some functions need many parameters, which are most easily represented in complex structures. Unfortunately, many mathematical functions in R, including optim, nlm, and grad can only operate on functions whose domain is a vector. R has a

Hi all, I wrote a symbollic differentiation function in R, which can be downloaded here: http://www.econ.upenn.edu/~clausen/computing/Deriv.R http://www.econ.upenn.edu/~clausen/computing/Simplify.R It is just a prototype. Of course, R already contains two differentiation functions: D and deriv. However, these functions have several limitations. They can probably be fixed, but

The R function "optim" fails when a function requires additional arguments, if the option "hessian=T" is specified. I am using R Version 1.0.0 on Windows 98. Here is an example of the Rosenbrock Banana function from the optim help example, with the function and gradient modified to take an additonal argument. Note that the call to optim works fine unless a hessian is

In the documentation for 'optim' it gives the following function: fr <- function(x) { ## Rosenbrock Banana function x1 <- x[1] x2 <- x[2] 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 } optim(c(-1.2,1), fr) When I run this code I get: $par [1] 1.000260 1.000506 I am sure I am missing something but why isn't 1,1 a better answer? If I plug 1,1 in the function it seems

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the

While working on 'random walk' applications, I got interested in optimizing noisy objective functions. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another

Dear Friends, I'm using the optim command to maximize a likelihood function. My optim command is as follows estim.out <- optim(beta, loglike, X=Xmain, Y=Y, hessian=T, method="BFGS", control=c(fnscale=-1, trace=1, REPORT=1)) Setting the report=1, gives me the likelihood function value (if i'm correct) at each step. The output from running this is as follows initial value

I was wondering if it was possible to get a Samba server that was acting as an AD member server to also be able to authenticate local users, or is stuck just serving AD users? -- Aaron Clausen mightymartianca at gmail.com

It's been passed to me from on high that one of our file servers needs to be encrypted. I'm considering either whole-disk encryption or folder encryption. I like the latter since, well, it's less work. Is there any particular folder encryption systems out there that the folks around here can recommend? -- Aaron Clausen mightymartianca at gmail.com

I'm running a Samba domain (Samba 3.4.7) with OpenLDAP. I also have an Server 2003 AD domain, and want to set up an external trust so that AD users can access resources on the Samba domain, but not visa versa (I believe this is called a one-way incoming external trust). I'm not finding a lot of information out there that makes sense. Does anybody have any hints? -- Aaron Clausen

Has anybody got imq running on iptables 1.2.7a. The home page for imq only seems to have a patch for 1.2.6a. -- Aaron Clausen _______________________________________________ LARTC mailing list / LARTC@mailman.ds9a.nl http://mailman.ds9a.nl/mailman/listinfo/lartc HOWTO: http://lartc.org/

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

Having worked with optim() and related programs for years, it surprised me that I haven't noticed this before, but optim() is inconsistent in how it deals with bounds constraints specified at infinity. Here's an example: # optim-glitch-Ex.R x0<-c(1,2,3,4) fnt <- function(x, fscale=10){ yy <- length(x):1 val <- sum((yy*x)^2)*fscale } grt <- function(x, fscale=10){ nn

Hi, Mark et al.: Thanks, Mark. Three comments: 1. Rvmmin was one of the methods I tried after Ravi directed me to optimx. It returned NAs for essentially everything. See my email of this subject stamped 4:43 PM Central time = 21:43 UTC. 2. It would be interesting to know if the current algorithm behind optim and optimx with

Hello: The development version of Ecdat on R-Forge contains a vignette in which optim(?, method=?L-BFGS-B?) stops with an error message while violating the lower bound. To see all the details, try the following: install.packages("Ecdat", repos="http://R-Forge.R-project.org") Then do "help(pac=Ecdat)" -> "User guides, package

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

Hi all, As long as I've used R, add=TRUE hasn't worked in contexts like this: f <- function(x) x^2 X <- seq(0, 1, by=1/4) plot(f, col="blue") plot(X, f(X), col="red", type="l", add=TRUE) I attached a fix for version 2.6.2. Cheers, Andrew -------------- next part -------------- diff --git a/src/library/graphics/R/plot.R

Dear kind R-experts. Does anybody have an experience to use optim function? If yes, what is the main difference between two method "BFGS" vs "L-BFGS-B"? I used "BFGS" method and got what I wanted. But when I used "L-BFGS-B" the error message said that "L-BFGS-B needs finite values of fn". So that means "BFGS" method can handle even if fn

Hi everyone, I have two questions: I would like to get confidence intervals on the coefficients derived from the optim() function. I apply optim() to a given function f > res <- optim(c(0.08,0.04,1.),f,NULL,method="L-BFGS-B",lower=c(0.,0.,0.)) And I would like to get the p-value and confidence intervals associated with > res$par My second question deals with error message. I

Dear R-help list, I am working on an optimization with R by evaluating a likelihood function that contains lots of Gamma calculations (BGNBD: Hardie Fader Lee 2005 Management Science). Since I am forced to implement lower bounds for the four parameters included in the model, I chose the optim() function mith L-BFGS-B as method. But the likelihood often returns inf-values which L-BFGS-B