similar to: NA return to NLM routine

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 UseRs, I wrote the following function to use MLE. --------------------------------------------- mlog <- function(theta, nx = 1, nz = 1, dt){ beta <- matrix(theta[1:(nx+1)], ncol = 1) delta <- matrix(theta[(nx+2):(nx+nz+1)], ncol = 1) sigma2 <- theta[nx+nz+2] gamma <- theta[nx+nz+3] y <- as.matrix(dt[, 1], ncol = 1) x <- as.matrix(data.frame(1,

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)

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)))))

Hello, I am trying to use nlm to estimate the parameters that minimize the following function: Predict<-function(M,c,z){ + v = c*M^z + return(v) + } M is a variable and c and z are parameters to be estimated. I then write the negative loglikelihood function assuming normal errors: nll<-function(M,V,c,z,s){ n<-length(Mean) logl<- -.5*n*log(2*pi) -.5*n*log(s) -

Hi R community I have written a loglikelihood function which I am minimizing using nlm(). nlm() is giving me no results...I mean, I am getting initial values as estimates. No iteration. I have tried many initials value close to true values and far away from tru values. But every time I am getting initial values as estimates and no iteration. Anybody can guide why this happens. Thank You

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(

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 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 listers, I posted this problem several days ago but it seems nobody answered. I use nlm to optimize a given function ,but it always generates the following warnings " Error in nlm(foo, theta.start) : non-finite value supplied by 'nlm' " I don't know why ,can anybody give me some hints ?? thanks in advance. regards .

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)))

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

I''m looking for a R function which can maximise this logliklihood function, under the constraits a>0 e b>0 f<-function(param){ a<-param[1] b <-param[2] log(prod)-(a*s2)-(b*s)-n*log(1-((0.5*b/sqrt(a))*(exp((b^2)/(4*a)))*((sqrt(pi ))*(1-pnorm(-b/(2*sqrt(a)), mean=0, sd=1)))))} I''ve tried maxlik constrOptim e donlp2 but without success. Thanks so

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

Dear all, I am trying to optimize a logistic function using optim, inside the following functions: #Estimating a and b from thetas and outcomes by ML IRT.estimate.abFromThetaX <- function(t, X, inits, lw=c(-Inf,-Inf), up=rep(Inf,2)){ optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, gr=IRT.gradZL, lower=lw, upper=up, t=t, X=X)

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 >

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, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l<- function(m1,m2,b) -sum( d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) ) here d and t are given, "sum" means sum over these two vairables. the parameters