similar to: Setting max. iterations for lmer

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Hello, I'm running a very long for loop that usually takes hours. For my own piece of mind, it would be nice if I could check periodically and see which iteration the loop is on. A line of code that told R to print the iteration number every 100 or 200 iterations would be perfect. Does anyone know something like this? I've never known how to print anything within a for loop before the

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Hello, I created an array to hold the results of a series of simulations I'm running: d.eta <- array(0,dim=c(3,3,200)) <simulation goes here and populates the array but it's not important> Then I tried to save the results using this: save(d.eta,file="D:/Simulation Results/sim 9-23-11 deta") When I later tried to reload them using this: d.eta <-

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hello, I have a longitudinal dataset where each individual has a different number of entries. Thus, it is of the following structure: x <- runif(12) id.var <- factor(c(rep("D1",4),rep("D2",2),rep("D3",3),rep("D4",3))) dat <- as.data.frame(x) dat$id.var <- id.var dat > dat x id.var 1 0.9611269 D1 2 0.6738606 D1 3

On starting an R session, I get the messages: Fatal errir: unable to restore save data in .RData Error in fun(...): couldn't find function "assignInNamespace" Error: .onLoad failed in 'loadNamespace' for 'Matrix' The only object in my .RData is last.warning, thus: > last.warning $"optim or nlminb returned message false convergence (8)"

Hello, Is there a way to get a single TRUE or FALSE statement from comparing two vectors? For example, c(1,2,3) == c(1,2,3) produces TRUE TRUE TRUE where I would like it to produce only TRUE for use in an if statement. Likewise, when two vectors are not exactly identical (in all elements) I would like a single FALSE result, as opposed to c(1,2,3) == c(1,2,5) TRUE TRUE FALSE Any ideas?

Hello, I'm clearly confused about the merge function. In the following r <- merge(x,y,all.x=T,all.y=F) my y vector has only unique values (no duplicates). So I don't understand how this can ever generate an r which is of greater length than x. I thought the default behavior was only matching rows are included, but that using all.x=T included rows with unmatched x's as well. If

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

Hello, I'm attempting to install the splm package from R-Forge. https://r-forge.r-project.org/R/?group_id=352 The page says, "In order to successfully install the packages provided on R-Forge, you have to switch to the most recent version of R..." It later says "To install this package directly within R type: install.packages("splm",

Dear R Users, I was hoping for some help with a recurrent error message in lmer. I am trying to model the effect of temperature on metabolic rate in animals (response = int.length) at different temperatures (mean.sst), with repeated measurements on the same individuals (random effect = female). Ideally I would make a random slope and intercept model where the rate can change differently with

Hello! One note/question hier about specification of control-parameters in the lme(...,control=list(...)) function call: i tried to specify tne number of iteration needed via lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE)) but every time i change the defualt values maxIter (e.g. maxIter=1, niterEM=0) on ones specified by me, the call returns all the iterations needed until

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

Hello everybody, I am using glmmADMB and when I run some models, I recieve the following message: Erro em glmmadmb(eumencells ~ 1 + (1 | owners), data = pred3, family = "nbinom", : The function maximizer failed (couldn't find STD file) Furthermore: Lost warning messages: Command execution 'C:\Windows\system32\cmd.exe /c

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect