similar to: nlm is

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)

Hi R-users,   I have this code here: library(numDeriv)   fprime <- function(z) { alp  <- 2.0165;   rho  <- 0.868;   # simplified expressions   a      <- alp-0.5   c1     <- sqrt(pi)/(gamma(alp)*(1-rho)^alp)   c2     <- sqrt(rho)/(1-rho)   t1     <- exp(-z/(1-rho))   t2     <- (z/(2*c2))^a   bes1   <- besselI(z*c2,a)   t1bes1 <- t1*bes1   c1*t1bes1*t2 }   ## Newton

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 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

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

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

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, 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)))

#bug 1: besselI() for nu<0 and expon.scaled=TRUE #tested with R-devel (2007-06-17 r41981) x <- 2.3 nu <- -0.4 print(paste(besselI(x, nu, TRUE), "=", exp(-x)*besselI(x, nu, FALSE))) #fix: #$ diff bessel_i_old.c bessel_i_new.c #57c57 #< bessel_k(x, -alpha, expo) * ((ize == 1)? 2. : 2.*exp(-x))/M_PI #--- #> bessel_k(x, -alpha, expo) * ((ize == 1)? 2. :

Hi, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

<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"

The besselK() function knows about overflows/underflows internally; there is a constant xmax_BESS_K in src/nmath/bessel.h (and referred to only in bessel_k.c), equal to 705.342, which is checked if expon.scaled is FALSE. (The equivalent number for bessel_i.c is 709, defined as exparg_BESS in bessel.h.) However, besselK(x) silently returns +Inf if x>705.342. This behavior is reasonable for

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,

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 <-

Dear R listers, I have a very annoying problem using nlm(). I want to find the minimizer of my target function, if written in \LaTeX is f(\mu1,\mu2,\sigma1,\sigma2) = \sum_i^n( w_ig_t(z_i) ), where g_t(z) is a pdf of bivariate normal distribution and z_i is my samples. I cannot get the estimation result generated by nlm(), and I got the following errors " Error in

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),

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 .

Dear R users, I have looked in the reference Schnabel, R. B., Koontz, J. E. and Weiss, B. E. (1985) A modular system of algorithms for unconstrained minimization. _ACM Trans. Math. Software_, *11*, 419-440. cited in the nlm help. This article says that the algorithm permits the use of step selection (line search, dogleg and optimal step), analytic or finite diference gradient

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