similar to: EM covergence problem

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

Hello, I am writing a meta-analysis on the complication and reoperation rates after 5 treatment modalities of a distal radius fracture. I have a code to compare the complication and reoperation rates. Currently it is using a mixed effects model. Is it possible to change the code so a random effects model is used? Thank you very much, Jay R code library(meta) library(readxl) All <-

The code in your mail in a mangled mess, since you posted in HTML. Please configure your email client to send emails in plain text. Could you explain what exactly you mean by "Currently it is using a mixed effects model. Is it possible to change the code so a random effects model is used?" Best, Wolfgang >-----Original Message----- >From: R-help [mailto:r-help-bounces at

hi Jay, Consult a local statistician. Statistics is not you think is (namely simple computations, R and probably plotting..). regards, vito Jay Zola <jayjay.1988 at hotmail.nl> ha scritto: > Hello, > > > I am medical student, writing a meta-analysis on complication and > reoperation rates after the five most common treatments of distal > radius fractures. I have

Hello, I am medical student, writing a meta-analysis on complication and reoperation rates after the five most common treatments of distal radius fractures. I have been busy with the statistics for months by my self, but find it quite hard since our classes were very basic. Now I want to compare the treatment modalities to see if there are significant differences. Using R I was able to

Dear Vito, Thank you for your reply. I tried to contact the statistics departement numerous times, but did not receive any reply. That is why I started to look on the internet for help. Yours sincerely, Jay Verstuurd vanaf mijn iPhone > Op 26 jun. 2017 om 22:05 heeft Vito Michele Rosario Muggeo <vito.muggeo at unipa.it> het volgende geschreven: > > hi Jay, > Consult a local

Hi all, I am using optim() to get estimate for parameter 'a' which will minimize the output of lprob.catch(a) and I got an error message: > optim(2,lprob.catch(x, freq,1, 0.5,0.5, a,- 0.7, 1, -0.7,0.1, 1,0.2,2,1,data.sf.mont)) Error in catch2.fun(k, f1, f2, a, b, A, B, R, n0, av, index.n) : Object "a" not found Why wasn't the initial value of a=2 passed to

hello, please can anyone help me out. Am a new user of R program. Am having problem with this code below, not getting the expected results. 1. Each m, the cumulative sum should be 1.000 but the 2nd and 3rd m returned 2.000 and 3.000 instead of 1.000. 2. to get the LCL(m) and UCL(m) for each m base on these instructions if out.cum > 0.025 then LCL(m)= y-1 if out.cum >0.975

Hello, I'm trying to estimate a cox model with a frailty variable and time-dependent covariate (below there is the statement I use and the error message). It's seems to be impossible, because every time I add the time-dependent covariate the model doesn't converge. Instead, if I estimate the same model without the time-dependent covariate it's converge. I'd like knowing if

Dear R users: I am using survreg for modeling left censored longitudinal data. When I am using the following code for fitting the tobit model I am getting some output with an warning message(highlighted with red color): > survreg(Surv(y, y>=0, type='left')~x + frailty(id), cytokine.data, weight=w, dist='gaussian', scale=1) Call: survreg(formula = Surv(y, y >= 0, type

Dear R users, I'm trying to fit a gamma-frailty model on a simulated dataset, with 6 covariates, and I'm running into some results I do not understand. I constructed an example from my simulation code, where I fit a coxph model without frailty (M1) and with frailty (M2) on a number of data samples with a varying degree of heterogeneity (I'm running R 2.3.1, running takes ~1 min).

Hi R users, I am using the optim funciton to maximize a log likelihood function. My code is as follows: p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031, -0.00245, 3.366, 0.5885, -0.00008, 0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049, 0.1856, 0.00394, -0.00193, -0.889, 0.5379, -0.000063, 0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,