similar to: NLMINB convergence codes

I tried to use LME (on a fairly large dataset, so I am not including it), and I got this error message: Error in lme.formula(formula(paste(c(toString(TargetName), "as.factor(nodeInd)"), : nlminb problem, convergence error code = 1 message = false convergence (8) Is there any way to get more information or to get the potentially wrong estimates from LME? (Also, the page in the

I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo datasets, and a few different (but very similar) likelihood functions. (They are complicated, but I can send them

I am having the same problem as one Rebecca Sela(see bellow). On 21/12/2007 12:07 AM, Rebecca Sela wrote: >* I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo

I am writing a program in which I would like to take in a formula, change the response (Y) variable into something else, and then pass the formula, with the new Y variable to another function. That is, I am starting with formula <- Y~X1+X2+X3 and I'd like to do something like Y <- formula$Y newY <- f(Y) lm(newY~X1+X2+X3) So far, it seems that my

Good day, Here I was trying to write a code for Garch(1,1) . As garch problem is more or less an optimization problem I also tried to get the algorithm for "nlminb" function. What I saw that if use this function 'nlminb" I can easyly get the estimate of parameters. But any other function is not working. I tried to write my own code for optimization using Quasi-Newton

I am trying to estimate models with subsets using the NLME package. However, I am getting an error in the case below (among others): > subset <- c(rep(TRUE, 107), FALSE) > fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1, subset=subset) Error in xj[i] : invalid subscript type 'closure' > fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1,

When I use a model fit with LME, I get an error if I try to use "predict" with a dataset consisting of a single line. For example, using this data: > simpledata Y t D ID 1 -1.464740870 1 0 1 2 1.222911373 2 0 1 3 -0.605996798 3 0 1 4 0.155692707 4 0 1 5 3.849619772 1 0 2 6 4.289213902 2 0 2 7 2.369407737 3 0 2 8 2.249052533 4 0 2 9 0.920044316 1

I am in the process of submitting a package to CRAN. R CMD check ran successfully on the package on my local computer, using R version 2.1.1. However, on the computers for CRAN (with version 2.10.0), the following errors occurred: Warning in parse_Rd("./man/predict.Rd", encoding = "unknown") : ./man/predict.Rd:28: unknown macro '\dquote' *** error on file

I am creating an R package. I ran R CMD check on the package, and everything passed until it tried to run the examples. Then, the result was: * checking examples ... ERROR Running examples in REEMtree-Ex.R failed. The error most likely occurred in: > ### * AutoCorrelationLRtest > > flush(stderr()); flush(stdout()) > > ### Name: AutoCorrelationLRtest > ### Title: Test for

Hi, I was trying to do a multi-level modeling of the data using the nlme package. I encounter the following message: > addRandomSlopeRange<- lme(FAN.range ~ CHN.range + Age + Block.T, data=data.block, random = ~CHN.range|CHI, method = "ML", na.action=na.omit) Error in lme.formula(FAN.range ~ CHN.range + Age + Block.T, data = data.block, : nlminb problem, convergence error code

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

Hello I want to use the nlminb function but I have the objective function like characters. I can summarize the problem using the first example in the nlminb documentation. x <- rnbinom(100, mu = 10, size = 10) hdev <- function(par) -sum(dnbinom(x, mu = par[1], size = par[2], log = TRUE)) nlminb(c(9, 12), objective=hdev) With the last instructions we obtain appropriate results. If I have

Hallo together, I am trying to estimate parameters by means of QMLE using the nlminb optimizer for a tree-structured GARCH model. I face two problems. First, the optimizer returns error[8] false convergence if I estimate the functions below. I have estimated the model at first with nlm without any problems, but then I needed to add some constraints so i choose nlminb.

Dear R developers, I tried to use nlminb instead of optim for a current problem (fitting parameters of a differential equation model). The PORT algorithm converged much better than any of optim's methods and the identified parameters are plausible. However, it took me a while before spotting the reason of a technical problem that nlminb, in contrast to optim, does not pass names of the

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

Hello I'm trying to solve two sets of equations (each set has four equations and all of them share common parameters) with nlminb procedure. I minimize one set and use their parameters as initial values of other set, repeating this until their parameters become very close to each other. I have several parameters (say,param1, param2) and their constraints are given as inequality and depend

>>>>> Kasper Kristensen via R-devel >>>>> on Mon, 28 Jan 2019 08:56:39 +0000 writes: > I've noticed unstable behavior of nlminb on some Linux > systems. The problem can be reproduced by compiling > R-3.5.2 using gcc-8.2 and running the following snippet: > f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) > opt

#optim package estimate<-optim(init.par,Linn,hessian=TRUE, method=c("L-BFGS-B"),control = list(trace=1,abstol=0.001),lower=c(0,0,0,0,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf,-Inf),upper=c(1,1,1,1,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf,Inf)) #nlminb package estimate<-nlminb(init.par,Linn,gr=NULL,hessian=TRUE,control =

This is a follow-up question for PR#15052 <http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15052> There is another thing I would like to discuss wrt how nlminb() should proceed with NAs. The question is: What would be a successful way to deal with an evaluation point of the objective function where the gradient and the hessian are not well defined? If the gradient and the hessian both

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(