similar to: Trouble with optim

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

I'm trying to fit a zero-inflated poisson model using zeroinfl() from the pscl library. It works fine for most models I try, but when I include either of 2 covariates, I get an error. When I include "PopulationDensity", I get this error: Error in solve.default (as.matrix(fit$hessian)) : system is computationally singular: reciprocal condition number = 1.91306e-34 When I

Hi all, I am running the following model: > glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn =

Hello, I have a data frame with 1425 observations, 539 of which are zeros. I am trying to fit the following ZINB: f3<-formula(Nbr_Abs~ Zone * Year + Source) ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData, offset=log(trans.area), trace=TRUE) Zone is a factor with 4 levels, Year a factor with 27 levels, and Source a factor with 3 levels. Nbr_Abs is counts

Dear all When I used the method, L-BFGS-B, in OPTIM, I've got the following message. --------------------------------------------------------------------- $par [1] 0.176166426835580 $value [1] 1322.17600079332 $counts function gradient 8 8 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [1,]

hi all i dont understand the error message that is produced by the optim function. can anybody help??? ie: [[1]]$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" can anyone help? ########################################################################### SK.FIT(XDATA=a,XDATAname="a",PHI1=1,v=5,vlo=2,vhi=300,phi2lo=.01) [[1]] [[1]]$par [1] -0.01377906

Hi there, I am trying to use linear regression to solve the following equation - y <- c(0.2525, 0.3448, 0.2358, 0.3696, 0.2708, 0.1667, 0.2941, 0.2333, 0.1500, 0.3077, 0.3462, 0.1667, 0.2500, 0.3214, 0.1364) x2 <- c(0.368, 0.537, 0.379, 0.472, 0.401, 0.361, 0.644, 0.444, 0.440, 0.676, 0.679, 0.622, 0.450, 0.379, 0.620) x1 <- 1-x2 # equation lmFit <- lm(y ~ x1 + x2) lmFit Call:

Dear R-help group, I have to maximize a likelihood with 40 parameters and I want to compare the MLE given by "nlminb" (Splus2000, on Windows) with those given by "optim" (R, on Unix). 1) On Splus, The algorithm "nlminb" seems to converge (the parameters stabilize) , it stops after several iterations ( around 400) with the message :"FUNCTION EVALUATION LIMIT

R-help, I'm just trying to find the ML (maximum likelihood) estimates of the mean and standard deviation of a set of observations: >xx=c(2.5,3.5,4,6,6.5,7.5) fn<-function(params,x=xx) { media<-params[1] st <-params[2] pdf=-sum(dnorm(log(xx),log(media),st,TRUE)) return(pdf) } optim(c(mu,stdev),fn,method="L-BFGS-B",lower=c(0.001, 0.001) ,upper = rep(Inf, 2),

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^b

R users; A question about optimization within R. I've been using both optim() and nlminb() to estimate parameters and all seems to be working fine. For context (but without getting into specifics - sorry), I'm working with a problem that is known to have correlated parameters, and parameter estimation can be difficult. I have a question on optim() - I'm using

Hi! I'm trying to figure out how to use optim... I get some really strange results, so I guess I got something wrong. I defined the following function which should be minimized: errorFunction <- function(localShifts,globalShift,fileName,experimentalPI,lambda) { lambda <- 1/sqrt(147) # error <- abs(errHuber(localShifts,globalShift, #

Dear R-devel During the last half a year I have several times encountered the following problem with optim() when using method= "L-BFGS-B". The function return a value which is clearly not the maximum (seen from printing the value each time the function is called). Some output is shown below. A few things I have observed (as I remember it): a. The problem seems to occur when the

Dear all, I am trying to optimize a logistic function using optim, inside the following functions: #Estimating a and b from thetas and outcomes by ML IRT.estimate.abFromThetaX <- function(t, X, inits, lw=c(-Inf,-Inf), up=rep(Inf,2)){ optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, gr=IRT.gradZL, lower=lw, upper=up, t=t, X=X)

Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. Cheers, Paul El El s?b, 19 de ago. de 2023 a la(s) 3:35 p. m., J C Nash < profjcnash at gmail.com> escribi?: > Why bother. nlsr can find a solution from very crude start. > > Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15, > 26, 8, 22) > x1 <- c(69.98, 72.5,

The cautions people have given about starting values are worth heeding. That nlxb() does well in many cases is useful, but not foolproof. And John Fox has shown that the problem can be tackled very simply too. Best, JN On 2023-08-19 18:42, Paul Bernal wrote: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El

Dear friends, Hope you are all doing well and having a great weekend. I have data that was collected on specific gravity and spectrophotometer analysis for 26 mixtures of NG (nitroglycerine), TA (triacetin), and 2 NDPA (2 - nitrodiphenylamine). In the dataset, x1 = %NG, x2 = %TA, and x3 = %2 NDPA. The response variable is the specific gravity, and the rest of the variables are the predictors.

Hello, S-plus and R helpers,(sorry for cross-post) I wrote some simple C code for one likelihood to be optimized (using optim(MASS)). I use same function, same data, same starting points and same DLL in R and S-plus for comparison. (I compiled it with 'Rcmd SHLIB likelihood.c' and the header files of it include only R.h and math.h). While it works quite fine in S-plus, it forever returns

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

Hello, I have been trying for weeks to compile a 64-bit Rblas. I started with ATLAS where I have had success in the past, but 64 bit was not behaving, and as each compilation takes between 9 and 12 hours, "test, check, and revise" was not going to be really viable. I therefore switched to OpenBLas (OPBL). I was successful in compiling R-3.0.1 and an OPBL-based BLAS for Windows 64bit