similar to: NLMINB() produces NaN!

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 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

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,

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 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

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

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

Hi Jim, I have tried "-fno-builtin" but it didn't help. Thanks, David On Tue, Aug 5, 2014 at 12:12 AM, Jim Grosbach <grosbach at apple.com> wrote: > Hi David, > > "-fno-builtin” is probably what you want. > > -Jim > > On Aug 4, 2014, at 2:19 AM, David Sela <sela.david at gmail.com> wrote: > > Clang optimizes code by replacing some parts

Hi Anton, Thanks for the answer. The link you provided doesn't answer the question. It lists the ways to overcome the linking error if the inline was not successful. The only condition that is found there to make the compiler not inline is "when compiling without optimization". I asked the question in the forum because I think that this info is not documented and most chances that

I have two straightforward questions about linking in the man pages for R packages: First, is it possible to link from within parts of the documentation that are not the \seealso section? For example, I would like to have something like: \arguments{ \item{correlation}{an optional \code{corStruct} object describing the within-group correlation structure; the available classes are given in

I am writing my first R package, and I have been getting the following series of errors when I run R CMD check: * checking S3 generic/method consistency ... WARNING Error: package/namespace load failed for 'REEMtree' Call sequence: 2: stop(gettextf("package/namespace load failed for '%s'", libraryPkgName(package)), call. = FALSE, domain = NA) 1: library(package,

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

I just built a package in R (version 2.1.1 on a Linux machine) and then moved it to my computer (R version 2.8.1 on a Windows machine). When I loaded the package and tried to open help for one of the commands, I got the following warning: Warning message: In print.help_files_with_topic("F:/R/R-2.8.1/library/REEMtree/chm/RMSE") : No CHM help for 'RMSE' in package

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

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

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

I won't add to the quite long discussion about the vagaries of nlminb, but will note that over a long period of software work in this optimization area I've found a number of programs and packages that do strange things when the objective is a function of a single parameter. Some methods quite explicitly throw an error when n<2. It seems nlminb does not, but that does not mean that

Hi, I was wondering if someone could give a brief, big picture overview of the difference between the two optimization functions nlm and nlminb. I'm not familiar with PORT routines, so I was hoping someone could give an explanation. Thanks, Angelo _________________________________________________________________ Instantly invite friends from Facebook and other social networks to join yo

#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 =

Hi! I want to convert S-Plus 6.2 code to R 2.1.0. Instead of the function nlminb I use the function optmin optmin(start,fn,gr,method="L-BFGS-B", lower, upper, hess,...) But then I get the Error in optmin ...: L-BFGS-B needs finite values of fn Then I used optmin(start,fn,gr,method="BFGS", hess, ...) But then I get the Error in optmin ...: initial value in vmmin is not