similar to: NLM OUTPUT

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 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

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 >

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)))

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 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 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

Hello, I am trying to understand a small quirk I came across in R. The following code results in an error: k <- c(2, 1, 1, 5, 5) f <- c(1, 1, 1, 3, 2) loglikelihood <- function(theta,k,f){ if( theta<1 && theta>0 ) return(-1*sum(log(choose(k,f))+f*log(theta)+(k-f)*log(1-theta))) return(NA) } nlm(loglikelihood ,0.5, k, f ) Running this code results in: Error

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

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'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, I'm doing some simulation work and it takes me a lot of time to do it in R. So I try to implement it in C code, but I want to use some R functions directly for my lazy and the robustness of code. For example, I will use lm and nlm in my program. How could I use R's lm and nlm function directly? I thinks these functions are not included in the R's include directory. Do I need

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

Hello everyone! I am having some trouble supplying the gradient function to nlm in R for windows version 2.2.1. What follows are the R-code I use: fredcs39<-function(a1,b1,b2,x){return(a1+exp(b1+b2*x))} loglikcs39<-function(theta,len){ value<-sum(mcs39[1:len]*fredcs39(theta[1],theta[2],theta[3],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],theta[3],c(8:(7+len)))))

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 there I am having trouble figuring out how to get an nlm function to report estimates for two parameter values in an estimation. The way I've got it goes something like this: f <- function (q, r) { here, I have a second loop which uses q, r to give me values for c, d below. a and b are already specified; this loop is a mass-balance function where I am trying to find values of q,

I am trying to do a MLE fit of the weibull to some data, which I attach. fitweibull<-function() { rt<-scan("r/rt/data2/triam1.dat") rt<-sort(rt) plot(rt,ppoints(rt)) a<-9 b<-.27 fn<-function(p) -sum( log(dweibull(rt,p[1],p[2])) ) cat("starting -log like=",fn(c(a,b)),"\n") out<-nlm(fn,p=c(a,b), hessian=TRUE)

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

Dear R Gurus, I am having a little difficulty with nlm. I've searched the archives and found nothing that tells me why this is occuring -- though there are some slightly similar issues. A simple example: lev2<-function(aaa,bbb,ccc,ddd,eee){ res<-aaa+bbb+ccc+ddd+eee res } nlm(lev2,p=c(32,4,5),ddd=45,eee=23) Error in f(x, ...) : argument "bbb" is missing, with no default