similar to: unexpected behavior of <<- in nlm (lazy?)

Dear R users, an improved version of package deSolve (version 1.3) is now available on CRAN. deSolve, the successor of R package odesolve, is a package to solve initial value problems (IVP) of: - ordinary differential equations (ODE), - differential algebraic equations (DAE) and - partial differential equations (PDE). The implementation includes stiff integration routines based on the ODEPACK

Dear R users, an improved version of package deSolve (version 1.3) is now available on CRAN. deSolve, the successor of R package odesolve, is a package to solve initial value problems (IVP) of: - ordinary differential equations (ODE), - differential algebraic equations (DAE) and - partial differential equations (PDE). The implementation includes stiff integration routines based on the ODEPACK

Karline, Thank you for your help. I discovered that in addition to including clim, I needed to omit breaks. This code uses one of your other examples as a starting point and works as intended: persp3D(z = volcano, zlim = c(-60, 200), phi = 20, colkey = list(length = 0.2, width = 0.4, shift = 0.15, cex.axis = 0.8, cex.clab = 0.85), lighting = TRUE, lphi = 90, clab =

Hi, I am trying to solve a model that consists of rather stiff ODEs in R. I use the package ODEsolve (lsoda) to solve these ODEs. To speed up the integration, the jacobian is also specified. Basically, the model is a one-dimensional advection-diffusion problem, and thus the jacobian is a tridiagonal matrix. The size of this jacobian is 100*100. In the original package

Dear R experts, In R-intro, under the 'Nonlinear least squares and maximum likelihood models' there are ttwo examples considered how to use 'nlm' function. In 'Least squares' the Standard Errors obtained as follows: After the fitting, out$minimum is the SSE, and out$estimates are the least squares estimates of the parameters. To obtain the approximate standard

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 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

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

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

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

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)

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 ############

<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"

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

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 >

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, 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 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

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