similar to: *** caught segfault *** address 0x18, cause 'memory not mapped'

It would be really nice if nlm took a set of "..." optional arguments that were passed through to the objective function. This level of hacking is probably slightly beyond me: is there a reason it would be technically difficult/inefficient? (I have a vague memory that it used to work this way either in S-PLUS or in some previous version of R, but I could easily be wrong.) Here's

I've set voicemail almost successfully, only a minor detail remains :-) I can't get the dates in my local language (spanish). In sip.conf, zapata.conf and voicemail.conf, I've set: language=es and my locale is "es" also. However, the days and months names still appear in english in the emails!!! Thursday 11 de May de 2006, 18:49:34. instead of Martes 11 de mayo de

Hi everyone. I have a problem that I have been unable to determine either the best way to proceed and why the methods I'm trying to use sometimes fail. I'm using the pf() function in an optimization function to find a noncentrality parameter that leads to a specific value at a specified quantile. My goal is to have a general function that returns the noncentrality parameter that

Hi all, When upgrading to 2.11.1 recently I noticed different results being produced by my code. After much digging I have finally narrowed it to a call to nlm(). This can be replicated by: FixedRemovals<-1836180125888 AbStageInitial<-2223033830403 Rates<- 0.3102445 nlm(function(rootM,Abund,Loss,OtherM) {(Loss-(rootM/(rootM+OtherM)* (1-exp(-(rootM+OtherM)))*

I am trying to view the source code of the function nlm in the stats package of R 2.4.1. I downloaded the source from CRAN and opened nlm.R, and it calls a .Internal function: .Internal(nlm(function(x) f(x, ...), p, hessian, typsize, fscale, msg, ndigit, gradtol, stepmax, steptol, iterlim)) This is the same thing I saw when entering the function name at the R command

I find it slightly surprising, that ifelse(TRUE, character(0), "") returns NA instead of character(0). [WNT 2.6.2 Patched] -- Heikki Kaskelma

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Hi Everyone, Is there a simple way to force nlm to take larger initial steps? Setting print.level = 2 allows me to inspect the step size at each iteration, but I appear not to have made any appreciable impact on it by changing values of typsize, fscale, steptol or stepmax. The steps repeatedly come out tiny, 1e-9 typically, and the algorithm is terminating not because the gradient is zero (it

Hi Apart from nlm() with the stepmax= argument, is there any other >1 dimensional minimisation function where I can limit the step size dynamically. With the NAG routine E04NBF (I think) I used to bound the size of the next step using a*arctan(x/a) tricks where a function was particularly difficult near a boundary and thus 'steer' the result within legal limits. The function I am

> A customer of ours is having randomly some kernel panics. Via some forum > posts I might have seen it could have something to do with Samba: > > They're running: RHEL 5.1 : Linux krulep03.verhaert.com 2.6.18-8.el5 #1 > SMP Fri Jan 26 14:15:21 EST 2007 i686 i686 i386 GNU/Linux > > rpm -qa | grep samba > samba-client-3.0.23c-2 > samba-3.0.23c-2 >

Dear all, I have to minimize a (real) function in a loop (say i in (1:1000)) and store its ``$estimate'', via l2estim<-nlm(f.minimo,c(-.01,0.1))$estimate into a vector for further analisys. Since the function's behaviour is quite peculiar (in the sense that in the simulation study it may not have a minumum), sometimes I get the the warning Error in nlm(minimo, c(-0.01, 0.1),

In the course of revising a paper I have had occasion to attempt to maximize a rather complicated log likelihood using the function nlm(). This is at the demand of a referee who claims that this will work better than my proposed use of a home- grown implementation of the Levenberg-Marquardt algorithm. I have run into serious hiccups in attempting to apply nlm(). If I provide gradient and

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