similar to: nlm() problem : extra parameters

Dear all, I have found a bug in nlm() and would like to submit a report on this. Since nlm() is in the stats-package, which is maintained by the R Core team, bug reports should be submitted to R's Bugzilla. However, I'm not a member of Bugzilla. Could anyone be so kind to add me to R's Bugzilla members or let me know to whom I should send the bug report? Thank you in advance. Kind

I can't seem to get the demo(nlm) to run under R version 0.99.0 Anyone know a solution? > fgh <- function(x) { gr <- function(x1, x2) { c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1), 200 * (x2 - x1 * x1)) } h <- function(x1, x2) { a11 <- 2 - 400 * (x2 - x1 * x1) + 800 * x1 * x1 a21 <- -400 * .... [TRUNCATED] > nlm(fgh,

Hello: I am sure this question was dealt with several years ago. Is the function vmmin() available from Rmath Standalone? If not is it possible to call optim() or nlm() from Rmath in C. Thank you. Mervyn

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",

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

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

>>>>> "DougB" == Douglas Bates <bates@stat.wisc.edu> writes: DougB> ....... DougB> ....... { time comparisons in testing lme(..) for R } DougB> ....... DougB> This can be expected to run faster when a version of nlm that accepts DougB> gradients and Hessians is available. --- Doug, do I understand properly that it won't

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

Admittedly I am using an old version 1.7.1, but can anyone tell if this is or was a problem. I can only get nlm (nonlinear minimization) to adjust the first three components of function variable. No gradient or hessian is supplied. E.G.; fnoise function(y) { y[5]/(y[4]*sp2) * exp(-((x[,3]-y[1]-y[2]*x[,1]-y[3] *x[,2])/y[4])^2/2) + (1-y[5])/(y[9]*sp2) * exp(-((x[,3]-y[6]-y[7]*x[,1]-y[8]

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

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, I'm new to this and am trying to teach myself some R by plotting biological data. The growth curve in question is supposed to be fitted to the Verhulst equation, which may be transcribed as follows: f(x)=a/(1+((a-0.008)/0.008)*exp(-(b*x))) - for a known population density (0.008) at t(0). I am trying to rework the example from "An Introduction to R" (p. 72) for my case and

Hi, I had a hard time in learning nlm in R and appreciate any help. I encounted the following error message from time to time when I tried different starting parameter values (three parameter values in this case) in nlm(f=SS.fun,p=c(0.1/40,0.1,2),hessian = FALSE,N.measure=object,h=20) Error in f(x, ...) : only 0's may mix with negative subscripts Basically I know the three parameter

OK I figured it out. The attributes have to be on the value returned, not on the function itself. I tried a linear (not log linear) regression with Poisson, using only the gradient, not the hessian. It took one extra iteration (13 instead of 12) and gave twice as many warnings (28 instead of 16) compared to not supplying the gradient. The answers are identical to 5 decimal places. Sorry to

I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print( names( theta ) ) # returns NULL ## get the values of the parameters for( i in 1:length( theta ) )

Dear R programming folks: I'm trying to finish off a package for non-linear simultaneous system estimation and I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print(

Hola a todos, Necesito estimar dos parametros utilizando la función nlm; fit<-nlm(hood2par,c(x01[i],x02[j]),iterlim=300, catch=x[,c(3,4,5)],sp=.5) donde hood2par es una logística modificada. Pero en mi caso, la convergencia de nlm depende de los valores iniciales de dichos parámetros. Para buscar dichos valores iniciales de manera automática, genero dos vectores de valores iniciales

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

Hello: Why is deriv3() functioning differently in R from that in Splus using library(MASS) ? For example deriv3(~(t1*log(t2)+lgamma(t1)+(1-t1)*log(y)+y/t2),c("t1","t2"),function(y,t1,t2)NULL) complains of lgamma. Mervyn

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