similar to: Usng MCMCpack,error is \\\"initial value in vmmin is not finite\\\"

Error in optim(method = "BFGS", c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : initial value in 'vmmin' is not finite I am running a logit model with latent class segments. I successfully got estimates for 2 segments. However when I tried to increase the no. of segments, I got this error message at the end. I checked my code again but can't find anything wrong. Is this error

Dear r-helpers, I'm experiencing some problems in fitting a maximum likelihood binomial model to some of my data. The error is in optim, which founds: Error in optim(par = c(0.2, 0.5), fn = function (p) : initial value in 'vmmin' is not finite Yes, I know it's a common problem, and I've carefully searched and readed all the posts about the issue, But, I can't find a

Hi all. I'm just starting to explore ordinal multinomial regression. My dataset is 300,000 rows, with an outcome (ordinal factor from 1 to 9) and five independent variables (all continuous). My first stab at it was this: pomod <- polr(Npf ~ o_stddev + o_skewness + o_kurtosis + o_acl_1e + dispersal, rlc, Hess=TRUE) And that worked; I got a good model fit. However, a variety of other

Dear r helpers, I'm trying to run the models of the BIOMOD package and got this message: * Error in nnet.default (x, y, w,...): **initial value in 'vmmin' is not finite* What does this mean? and how can I correct it? Thank you *Andreia* [[alternative HTML version deleted]]

I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error: > lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr) Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, : initial value in 'vmmin' is not

Dear r helpers I run the following code for nested logit and got a message that Error in optim(c(0, 0, 0, 0, 0.1, -2, -0.2), fr, hessian = TRUE, method = "BFGS") : initial value in 'vmmin' is not finite What does this mean? and how can I correct it? Thank you June > yogurt = read.table("yogurtnp.csv", header=F,sep=",")> attach(yogurt)>

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

This kind of error seems to surprise R users. It surprises me that it doesn't happen much more frequently. The "BFGS" method of optim() from the 1990 Pascal version of my book was called the Variable Metric method as per Fletcher's 1970 paper it was drawn from. It really works much better with analytic gradients, and the Rvmmin package which is an all-R version that adds bounds

Hi list! I'm getting an error message when trying to fit an accelerated failure time parametric model using the aftreg() function from package eha: > Error in optim(beta, Fmin, method = "BFGS", control = list(trace = > as.integer(printlevel)), : > non-finite finite-difference value [2] This only happens when adding four specific covariates at the same time in the

Hi, When I run the following code, r <- c(3,4,4,3,5,4,5,9,8,11,12,13) n <- rep(15,12) x <- c(0, 1.1, 1.3, 2.0, 2.2, 2.8, 3.7, 3.9, 4.4, 4.8, 5.9, 6.8) x <- log10(x) fr <- function(c, alpha, beta) { P <- c + (1-c) * pnorm(alpha + beta * x) P <- pmax(pmin(P,1),0) -(sum(log(choose(n,r))) + sum(r * log(P)) + sum((n -r)* log(1-P))) } fit <- mle((fr), start = list(c

Dear R users, When I run the code below, I get the error "Error in integrate(integrand, 0, Inf) : non-finite function value". The code works if the function returns only "sum(integ)". However, I want to add "cmh" to it. When I add "cmh" I get that error. I can't figure out why this is happening because my integrate function has nothing to do with

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)

Dear R users, I have a question about numerical integration in R. I am facing the 'non-finite function value' error while integrating the function xf(x) using 'integrate'. f(x) is a probability density function and assumed to follow the three parameter (min = 0) beta distribution for which I have estimated the parameters. The function is integrated

Hi guys, I'm trying to use the the integral function to estimate the area under a PDF and a crossing curve. first I stated the function with several vectors in it: fn=function(a,b,F,mu,alpha,xi) { x<-vector() fs<-function(x) { c <- (mu+(alpha*(1-(1-F)^xi)/xi)) tmp <- (1 + (xi * (x - mu))/alpha) ((as.numeric(tmp > 0) * (tmp^(-1/xi - 1) *

Dear R-helpers, running a zeroinflated model of the following type: zinb = zeroinfl(count=response ~., x = ~ . - response, z = ~. - response, dist = "negbin", data = t.data, trace = TRUE) generates the following message: Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 359 52 7 3 generating

Hello All, I'm trying to run a one-dimenional irt model using the packages MCMC and coda on a rather large set of roll-call voting data with many missing observations. Here's a sample of the code: Post10<- MCMCirt1d (Italy10, burnin = 1000, mcmc=50000, thin=100, verbose=TRUE, theta.constraints = list(V549=1, V443=-1)) The MCMCirt1d command seems to work fine, but when I try to

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 all, I am using the optim() function which it stops with the following error messagge: error in optim(...) non-finite finite-difference value I was wondering if somebody might suggest me a way to fix it please. Thanks in advance to all of you. Kind regards, Tom __________________________________________________ [[alternative HTML version deleted]]

Dear all, when applying the optim function the following error occured "non-finite finite-difference value" Therefore I would like to ask how one can try to handle such a problem and which strategies have proven useful. (There is only litte guidance on the help list for this kind of problem.) Thank you in advance for your efforts! Best, Martin --