similar to: NaN as a parameter in NLMINB optimization

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 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 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 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 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

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

According to the R documentation for NLMINB, the returned value of convergence is 0 for successful convergence. When I got another code (1), I looked up the PDF that linked from the documentation (http://netlib.bell-labs.com/cm/cs/cstr/153.pdf), which said that a return code under 3 was impossible. Is there other documentation that gives the correct meanings of the NLMINB convergence codes in

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

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 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

#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 =

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

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(

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 :

Dear R-users I am trying to find a value for the risk free rate minimizing the difference between a BS call value with impl. volatilities minus the market price of a call (assuming this is just the average bid ask price) Here is my data: http://r.789695.n4.nabble.com/file/n3747509/S%26P_500_calls%2C_jan-jun_2010.csv S%26P_500_calls%2C_jan-jun_2010.csv S0 <- 1136.03 q <- 0.02145608 S0

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

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

Hello all, I am trying to get the variance-covariance (VCOV) matrix of the parameter estimates produced from the nlminb minimizing function, using vcov.nlminb, but it seems to have been expunged from the MASS library. The hessian from nlminb is also producing NaNs, although the estimates seems to be right, so I can't VCOV that way either. I also tried using the vcov function after minimizing

I am trying to optimize custom likelyhood with nlminb() Arguments h and f are meant to be fixed. example.R: compute.hyper.log.likelyhood <- function(a, h, f) { a1 <- a[1] a2 <- a[2] l <- 0.0 for (j in 1:length(f)) { l <- l + lbeta(a1 + f[j], a2 + h - f[j]) - lbeta(a1, a2) } return(l) } compute.optimal.hyper.params <- function(start, limits, h_, f_) { result