similar to: non-finite finite-difference value

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

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

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]]

Hello, I'm trying to use fitdistr() from the MASS package to fit a gamma distribution to a set of data. The data set is too large (1167 values) to reproduce in an email, but the summary statistics are: Min. 1st Qu. Median Mean 3rd Qu. Max. 116.7 266.7 666.7 1348.0 1642.0 16720.0 The call I'm trying to make is: fitdistr(x,"gamma") and the error is: Error in optim(x =

Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is

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-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

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 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 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 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 <-

Hi all! I'm running some simulations and I need to estimate some paramaters with optim( ), in some cases optim stops with the next message: "L-BFGS-B needs finite values of 'fn' " I would like to know how to include and "if" condition when this happen, could it be something like: myfun <- optim(....) # run my function

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 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

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 All, I am trying to solve the optimization problem below, but I am always getting the following error: Error in optim(rep(20, nvar), f, gr, method = "L-BFGS-B", lower = rep(0, : L-BFGS-B needs finite values of 'fn' Any ideas? Thanks in advance, Paul ----------------------------------------------- k <- 10000 b <- 0.3 f <- function(x) { n <- length(x)

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

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