similar to: Gradient problem in nlm

Dear R-users, I am trying to find the MLEs for a loglikelihood function (loglikcs39) and tried using both optim and nlm. fredcs39<-function(b1,b2,x){return(exp(b1+b2*x))} loglikcs39<-function(theta,len){ sum(mcs39[1:len]*fredcs39(theta[1],theta[2],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],c(8:(7+len))))) } theta.start<-c(0.1,0.1) 1. The output from using optim is

Out of curiosity, I have tried, without success, to use the new facility in nlm to specify the gradient and hessian. (It is many years since I had a problem simple enough to make analytic derivation of these worthwhile.) The help now says that the function must have attributes with these names but gives no indication as to what should be in the attributes. The online example and demo do not use

<FONT face="Default Sans Serif, Verdana, Arial, Helvetica, sans-serif" size=2><DIV>Dear list members,</DIV><DIV>&nbsp;</DIV><DIV>I am trying to use "nlm" function to maximize a mixture likelihood of beta densities. There are five unknown parameters in the likelihood. Since I can get the analytic gradient, I attach the "gradient"

Hello No worries anymore. Figured it out. Thanks for everything. Luz ---------- Forwarded message ---------- Date: Sun, 18 Jun 2006 23:12:25 -0600 (MDT) From: Luz Maria Palacios Derflingher <palacios at math.ucalgary.ca> To: r-help at lists.R-project.org Cc: r-help at stat.math.ethz.ch. Subject: help on nlm (gradient) Hello. I am having some trouble using nlm in R for windows version

At present the documentation for nlm refers the reader to Dennis and Schnabel for details on the algorithms. It also states that the function is liable to change. Can anyone tell me if the current version of nlm uses only function values or if it can use gradients and Hessians when they are available? I would like to get an idea of how difficult it would be to port the development versions of

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Dear useRs, I am analysing the behaviour of MLE for the two parameters of a kind of exponential distribution, leaving as initial values the estimators moments produced by the variation coefficient. I do using simulations, giving them an accountant, r. But running my codes remains a problem with the nlm function. To review details wearing On one of the lines put status

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 <-

Hi, Where can I find the source code for nlm()? I dowloaded the R2.8.1.tar.gz file and looked at all the .c and .f files, but couldn't find either nlm.c or nlm.f There is an nlm.r file, but that is not useful. Thanks for any help, Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging

Hi, I tried to compute maximum likelihood under gamma distribution, using nlm function. The code is this: __BEGIN__ vsamples<- c(103.9, 88.5, 242.9, 206.6, 175.7, 164.4) mlogl <- function(alpha, x) { if (length(alpha) > 1) stop("alpha must be scalar") if (alpha <= 0) stop("alpha must be positive") return(- sum(dgamma(x, shape = alpha, log = TRUE)))

Dear R users, I am trying to find the mle that involves integration. I am using the following code and get an error when I use the nlm function d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2) h<-matrix(runif(20,0,1),10) integ<-matrix(c(0),nrow=10, ncol=2) ll<-function(p){ for (k in 1:2){ for(s in 1:10){ integrand<-function(x)

Hi R-Gurus, I've been cutting along quite nicely with nlm, until I threw in the following condition in the function that nlm is minimising: if (((term*bexp) < 0.0001)) { #warning(term*bexp, "=term*bexp",psi,"=psi") theta<-2000 } Now when I run this function anywhere else, there is no problem, whether or the if's condition is met. When

Hi all, I have been trying the nlm function but received an error message which reads: Error in nlm(intensities ~ f, c(epsilon.spec.start, epsilon.unspec.start, : invalid function value in 'nlm' optimizer The message is generated somewhere in the compiled part, apparently within the function static void fcn(int n, const double x[], double *f, function_info *state) where a jump

I'm looking for an example of a simple R script that impliments a contrained nonlinear function using nlm or optim. I'm not exactly sure how to impliment the constraints within the objective function that is passed to nlm/optim. obj.func <- function( p ) { x(p) <- unconstrained obj function value if( constraint1 > something ) { obj.func <- x(p) } else {

Dear all, I use the Andersen plot to check for proportional hazards assumption for a factor (say x) in the Cox regression model and obtained a straight line that pass through the origin. However, the formal test done by the R-function cox.zph, which is based on the plot of Schonefeld residuals against time, indicates that proportional hazards assumption is violated. Further, a plot of the score

Hi I am trying to use nlm with an additional call to nlm within the function but after the first pass, the parameters to the outer call are being passed to the inner call. The inner call is a very trivial problem. ie: test.outer<-function(param.outer){ slope<-nlm(test.inner,param.inner) ... loglikelihood<-sum(...) return(-loglikelihood) } and nlm(test.outer,param.outer) on the

Dear R People: Here is a function to minimized: >mfun1 function(x,a) { x[1] <- a[1]*x[2] + a[3] - a[2]*(a[1]-a[2])*a[3] x[2] <- a[1]*x[1] - a[2]*a[3] return(x) } Here is my first try: >nlm(mfun1,c(1,1)) Error in f(x, ...) : Argument "a" is missing, with no default > >nlm(mfun1,c(1,1),a=c(0.8,0.5,1)) Error in nlm(mfun1, c(1, 1), a = c(0.8, 0.5, 1)) :

I am trying to use the function "nlm" to find the mle. I want to use a generic function for the likelihood which would require me to use both the parameters and the data as arguments. But nlm requires the function to have only the parameters as arguments for this function (see example below). > testfun <- function(x,y) sum((x-y)^2) # x - parameters, y - data >

I am using nlm to maximize a likelihood function. When I call the likelihood function (garchLLH) via nlm however, nlm returns the wrong value of the function. When I test the likelihood function manually I get the correct answer. I'm probably doing something really stupid, maybe someone can point it out for me. ###############this is the function i am trying to minimize ############

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),