similar to: random slope models with lme --> failured to converge

Dear all, Using the data set and code below, I am interested in modelling how egg temperature (egg.temp) is related to energy expenditure (kjday) and clutch size (treat) in incubating birds using the lme-function. I wish to generate the F-distribution for my model, and have tried to do so using the anova()-function. However, in the resulting anova-table, the parameter kjday has gone from being

Dear all, Using the data set and code below, I am interested in modelling how egg temperature (egg.temp) is related to energy expenditure (kjday) and clutch size (treat) in incubating birds using the lme-function. I wish to generate the F-distribution for my model, and have tried to do so using the anova()-function. However, in the resulting anova-table, the parameter kjday has gone from

Hello R users, I'm trying to do simulations for comparing cox and weibull I have come across this problem: Warning messages: 1: In survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : Ran out of iterations and did not converge 2: In survreg.fit(X, Y, weights, offset, init = init, controlvals = control, : Ran out of iterations and did not converge what i did is

Hello All,   I¢m  kinda new to R language and any help that I can get is greatly appreciated.   Basically, I want to find the values of the two parameters that will maximize the function and I¢m currently using the optim function to find these values.  My R code works fine but not all the time.  Sometimes the solution converges, sometimes not.   Since I¢m planning to do this 5000 times, is there a

Dear all, I need to know in which number of iterations the kmeans converge each time I run it. Any idea how to do it? Thank you for your attention, Rui

Help! Being a bit of a novice, please bear with me if this is a stupid question! I am trying to fit a saturated model to some count data that I have: model<-glm(COUNT~SP*LOC*COL*TIME*TREAT,poisson) but R keeps on crashing and coming up with (occasionally before crashing) an error that states: Algorithm did not converge in: (if(is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y,

After running a model for a while and seeing that it did not converge yet, how can I continue to run, ie not starting anew, the model? I know if I manually/interactively use winbugs, this is possible anytime, but how can I do this in r2winbugs, so that my existing sim$sims.array and other stuff in the object that bugs() returns gets extended? Thanks Toby

When I ran a Gamma regression in SAS, the algorithm converged. When I ran it in R, it keeps uncoverged even if I used 10000 iterations. What was wrong? I used the following code in R: glm(y ~ x1 x2 x3, control=glm.control(maxit=10000), data, family=Gamma(link="log")) [[alternative HTML version deleted]]

Hi All, I run function "kmeans" to cluster a matrix. But when the matrix size is big enough, it keeps saying "did not converge in 10 iterations". Could you explain what it means and if the result is wrong? And the interesting thing is sometimes this warning happens when the sample size is around 51200 x 6, sometimes it happens around 30000 x 6. Does the warning related

Running the Chapter 4 examples in Pinheiro & Bates' "Mixed-Effects Models in S and S-PLUS" (2000), I get a message that the default optimizer doesn't converge, but using "optim" for the optimizer results in convergence: > > library(nlme) > > fm1IGF.lis <- lmList(IGF) > > fm1IGF.lme <- lme(fm1IGF.lis) > Error in lme.formula(fixed =

I have a set of functions: Probability <- function(N, f, w, b, l, n, q) { #N is the number of lymph nodes #f is the fraction of Dendritic cells (in the correct node) that have the antigen #w is time in terms of hours #b is the starting position (somewhere in the node or somewhere in the gap between nodes. It is a number between 1 and (x+t)) #q is the length of the time step #l is the LN

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

I am using the Ben bolker's R package "bbmle" to estimate the parameters of a binomial mixture distribution via Maximum Likelihood Method. For some data sets, I got the following warning messages: *Warning: optimization did not converge (code 1: ) There were 50 or more warnings (use warnings() to see the first 50)* Also, warnings() results the following: *In 0:(n - x) : numerical

Hi, So for awhile I have been seeing this issue but it hasn''t been painful. However, recently I have been deploying a new module that has made it much more annoying. file { cdh3_config: recurse => true, ignore => ".svn", checksum => md5, notify => Exec[hadoop_alternatives], require

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 :

Hello R users, I am trying to minimize two functions with genoud. It is actually one function with two sets of data, each of them having two unknown variables (called Vcmax and gi) which have the same value in each of the function. They are called f.1 and f.2 in the code below. My objective to minimize the functions in order to get the two variables equal in each of the functions. Furthermore, I

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->