similar to: error in lme4 for R 2.4.0 (PR#9297)

I am trying to replicate the repeated measures example from Dr.Faraway's book (Extending the linear model with R) as follows: data(vision) vision$npower <- rep(1:4,14) mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision) When I look at the fixed effects p-value, it is missing. Am I missing something here? Thanks. Fixed effects: Estimate Std. Error t value

Dear all, I try to run the example of lmer and get the following error message. > library(lme4) > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) [[1]] Error in get(x, envir, mode, inherits) : variable "as.dpoMatrix" was not found This error message is similar to what I get with other models. It looks like the mer class has a slightly

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

Hello Everyone I'm looking into the best way to have locked version repos for my CentOS systems. The systems are all set up with Chef and have a couple different recopies/roles. I'd like to have locked version repos for each role with tested RPMs. Then perhaps quarterly apply any updates. It would be nice to have something showing which updates are available for these locked repos.

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

Can anyone comment on their experiences with librarian-puppet or using git superproject with per puppet module repositories? We are in the midst of determining which route is optimal for our environment. It seems like using git superprojects would mean one less new tool for everyone to learn. What then would be the advantages of librarian-puppet? Thanks, Ryan Bowlby -- You received this

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

I work on the Pulp Project (http://www.pulpproject.org/), and we are trying to fully implement the forge API so we can create new repositories that can be reached via a command like: $ sudo puppet module install --module_repository http://localhost/pulp/puppet/myforge/ someone/theirmodule The problem I''ve encountered is that the install command includes hard-coded absolute paths to

Dear R-users, I have an outcome variable and I'm unsure about how to treat it. Any advice? I have spending data for each county in the state of California (N=58). Each county has been allocated money to spend on any one of the following four categories: A, B, C, and D. Each county may spend the money in any way they see fit. This also means that the county need not spend all the money that

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

Hi, I am setting up a local forge for some weird political reasons. I came to know that puppetlabs is working on such a feature. But, in the meantime I wanted to try out setting up the repo and use --modules_repository flag in "puppet module install: command. For that, I need to mimic the puppet forge repo format (meaning the directory structure of the modules). Can somebody point me

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

I have been struggling all day to import a particular function/method combination (ranef(), which extracts the random effects from a mixed model fit) from the nlme package into another package ... so far without success. The NAMESPACE for nlme contains the following lines: export(..., ranef, ...) S3method(ranef, lme) ranef is defined as a standard S3 generic, function (object, ...)

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

Dear List: I am having difficulties with the fitted values at different levels of a multilevel model. My data set is a series of student test scores over time with a total of 7,280 observations, 1,720 students nested witin 60 schools. The data set is not balanced. The model was fit using eg.model.1<-lme(math~year, random=~year|schoolid/childid, data=single). When I call the random

R múltiple Estoy pensando en un problema que tendré que solucionar pero aún no comencé a escribirlo, por lo tanto no hay código en R como para compartir, sin embargo no tengo idea de cómo realizarlo desde R. El planteo es el siguiente: Los datos son en una cantidad necesaria para que el procesamiento estadístico demore (minutos, horas). Supongamos dos variables (serían más), la A y la B,

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

Hi all! How is it possible to estimate standard errors for coef obtained from lme? Is there sth like se.coef() for lmer or what is the anaytical solution? Thank you!