similar to: glm convergence warning

Hi, I need to do several matrix multiplications with the corresponding matrices forming two 3-dimentional arrays. To illustrate my problem, let's say I have the following 3-dimensional arrays: array1 <- array(1:30,dim=c(3,2,5)) array2 <- array(1:20,dim=c(2,2,5)) I know that I can get what I want with the following computation : result <- array(dim=c(dim(array1)[1],

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

Full_Name: Daniel R Jeske Version: 1.8.1 OS: Windows 2000 Submission from: (NULL) (138.23.228.79) We have just noticed that when you use glm() it seems the logical output 'converged' is always TRUE. The same data set that shows FALSE in version 1.7.1 shows TRUE in 1.8.1. And I know that FALSE is the correct answer...so it seems like we cannot trust the 'converged' flag for

Dear list, I do have a problem with nls. I use the following data: >test time conc dose 0.50 5.40 1 0.75 11.10 1 1.00 8.40 1 1.25 13.80 1 1.50 15.50 1 1.75 18.00 1 2.00 17.00 1 2.50 13.90 1 3.00 11.20 1 3.50 9.90 1 4.00 4.70 1 5.00 5.00 1 6.00 1.90 1 7.00 1.90 1 9.00 1.10 1 12.00 0.95 1 14.00

Hello, all, I am running some simulations to estimate power for a complicated epidemiological study, and am using lme and lmer to get these estimates. I have to run a few thousand iterations, and once in a great while, an iteration will create fake data such that the model won't converge. I see from Google searches that this is not an uncommon situation. My question: is there a way to

Hello Anyone that now of any function in R that can calculate copulas? Or if anyone have any code avaible I would be more than interested. Thank you in advance /Thomas ______________________________________________ R-help at stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help

Dear R-mailing list, I would like to overlay to two quantreg coefficients plots. I have plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata))) plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata1))) Is there a possibility to display the two in the same graph? Thank you so much!!! Christian [[alternative HTML version deleted]]

Dear all, I'm trying to fit a pure additive model of the following formula : fit <- gam(y~x1+te(x2, x3, bs="cr")) ,with the smoothing parameter estimation method "magic"(default). Regarding this, I have two questions : Question 1 : In some cases the value of "mgcv.conv$fully.converged" becomes "FALSE", which tells me that the method stopped with a

Full_Name: Sophie Smout Version: 1.5.0 OS: windows 2000, pc Submission from: (NULL) (138.251.190.17) I'm finding that nls iterates to the end of its iteration setting and does not jump out to report convergence even when it has clearly converged. It is also mis-reporting the number of iterations completed. These may not be unconnected... This is an example: [note - details of my computer,

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

What kind of special magic does glm have? I'm working on a logistic regression solver (L-BFGS) in c and I've been using glm to check my results. I came across a data set that has a very high condition number (the data matrix transpose the data matrix) that when running my solver does not converge, but the same data set with glm was converging ( I love R :) ). I noticed that glm using

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

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]]

hie.. how can i write a loop that makes algorithm keeps repeating until a solution is converged?do i use a for loop? i know that we can use for loop to ask for a number of repetitions, but how to use it to ask the algorithm to keep repeating until a solution is converged? Thanks -- View this message in context: http://www.nabble.com/convergence-tf3606834.html#a10076822 Sent from the R help

Full_Name: Bonnie Version: 1.6.1 OS: Windows Submission from: (NULL) (160.129.25.106) glm() seems to converge, even when it shouldn't. I am trying to fit a model where $converge=FALSE and I am fitting models that do not converge in SAS, but they seem to converge in R ... Thank you.

Hello, When I run the following glm model: modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial) I get the following warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred What I am trying to do is model my response variable (CID: correct bird identification) as a function of the predictor variables weather state (WS), sea

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

First thanks for Jinsong's suggestions I would like to do a bootstrap in a nonlinear model. But it fails to converge in most of time. (it did converge if I just use nls without boot). Thus, I use "try" function to resolve my problem. This following code is from Jinsong's suggestion. h1a.nls<-nls(density~nmf(time, alpha, delta, psi, tau, gamma),data=h1a,

Hello, I got a question with the R function constrOptim. >From the R help, it says that the return values of "constrOptim" are the same as "optim". For the return value "convergence" of the function "optim", the values should be 0, 1, 10, 51 and 52. See http://www.maths.lth.se/help/R/.R/library/stats/html/optim.html When I use constrOptim, I get

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,