similar to: Problem with Crawley book example

Dear James, Thanks for the input. Even increasing from 5 to 10, the amount of times to miss the password and lock the account (after changing, I wheeled a gpupdate / force), if you miss 3 times the account is locked. I changed smb.conf log level to 9. I tried to unlock the account using the samba-tool command line, but without success, because I can only unlock using the RSAT. I get these

Dear, I've been noticing that constantly, some user accounts has been repeatedly blocked. We use DC Samba 4.4.5. Here we have a blocking policy, if the wrong password is entered more than 5 times. But most users say they do not screwed up the password. They perform the log at the beginning of working hours. After a while, they block the session and when they try to log on again, the

http://bugzilla.mindrot.org/show_bug.cgi?id=752 Summary: Lack of Message file and Eventlog Application Source registry entries result in event log errors Product: Portable OpenSSH Version: -current Platform: All OS/Version: Cygwin on NT/2k Status: NEW Severity: minor Priority: P2

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

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 all. I'm doing a simulation study where I will be making use of the 'nlme' package. I want to loosen up the convergence criteria so that I increase the likelihood of convergence (potentially at the cost of obtaining slightly less than ideal results). The parameters in the function nlmeControl() control the convergence criteria. These default values can be modified to make

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Dear R-ers, I am having trouble understanding why I am getting an error using glmmPQL (library MASS). I am getting the following error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 The long story: I have data from an experiment on pairwise comparisons between 3 treatments (a, b, c). So a typical run of an experiment

Hi all, I'm having some difficulty with lme. I am currently trying to run the following simple model anova(lme(x ~ f1 + f2 + f1:f2, data=m, random=~1|r1)) Which is currently producing the error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 x is a numeric vector containing 194 observations. f1 is a factor vector containing two levels, and

I have the following code: ----------------------------------------------------------------------------------------------------- result <- matrix(NA, nrow=1, ncol=5) for(i in 1:(nsnp-1)) { for(j in (i+1):nsnp){ tempsnp1 <- data.lme[,i] tempsnp2 <- data.lme[,j] fm1 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2+tempsnp1*tempsnp2, random=~1|famid, na.action=na.omit) fm2 <-

Hi, I have a simple random-coefficients model for m subjects: y = b0 + b1 x + r0 + r1 x + e where b0 and b1 are fixed parameters, r0 and r1 are random, e ~ N(0,s2 I) and R' = [r0, r1] ~ N(0,T). I try to obtain the EM-estimates of s2 and the elements of T by lme(y~x,data=mydata,random= list(group=~x), control=lmeControl(maxIter = 0, niterEM=100,msVerbose = TRUE)) Does

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

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)"

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

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)