similar to: Lme4 Package Help!

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively

I am modeling the effects of an environmental variable (X) on fish recruitment (Y) for several, ecologically related species (i) using a mixed-effects model. The linear relationship between X and Y includes a fixed effect that is common across all species and random effects that vary by species. In the lmer() notation from the package lme4: Model<- lmer(Y ~ X +(X|i)) Because the residuals of

Hello all, Using Ubuntu 9.04 and R 2.8.1. For a project I need to use the Zelig package, which in turn wants to use the lme4 package. When trying to use Zelig and it tries to its required packages I get the following error message. Error in dyn.load(file, DLLpath = DLLpath, ...) : function 'cholmod_start' not provided by package 'Matrix' Error in loadModelDeps(model) :

Dear All I am trying to include sampling weights in multilavel regression analysis using packege lme4 using following codes print(fm1 &lt;- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE) print(fm2 &lt;- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE,weights=sweight) The problem is both the

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

Dear all, For a project I need to calculate the conditional AIC of a mixed effects model. Luckily, I found a reference in the R help forum for a function to be used: CAIC <- function(model) { sigma <- attr(VarCorr(model), 'sc') observed <- attr(model, 'y') predicted <- fitted(model) cond.loglik <- sum(dnorm(observed,

This may sound like an April Fool's joke but it isn't really. I just uploaded version 0.95-1 of the lme4 package for R. This is the package with the new versions of the lme (linear mixed-effects) function for R and the GLMM (generalized linear mixed models) function. One distinguishing feature of the 0.95 series of releases of this package is the absence of the lme and GLMM

This may sound like an April Fool's joke but it isn't really. I just uploaded version 0.95-1 of the lme4 package for R. This is the package with the new versions of the lme (linear mixed-effects) function for R and the GLMM (generalized linear mixed models) function. One distinguishing feature of the 0.95 series of releases of this package is the absence of the lme and GLMM

Hello, I am trying to use lmLists in the lme4 package and copying over very standard code from the nlme package given in 'Mixed-Effects Models in S and S-Plus'. It appears to not accept an 'I(age-11)' in the formula, though it will accept the formula with out the subtraction of 11 from age. This seems like it would be a bug, since this is standard formula syntax, unless

I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000): > m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar

Full_Name: Dale Barr Version: 2.5.1 (patched) OS: Ubuntu linux x86_64 Submission from: (NULL) (138.23.70.108) When RMySQL is loaded in before lme4, the summary() function for lmer objects in the lme4 packages produces the following error: Error in printMer(object) : no slot of name "status" for this object of class "table" When RMySQL is loaded AFTER lme4, however, no such

Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on CRAN for use with R version 2.4.0 or later. Purpose of the packages: The Matrix package provides S4 classes and methods for sparse and dense matrices. The lme4 package provides functions for fitting and assessing linear or generalized linear mixed effects models (also called multilevel models). Like the Matrix package,

Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on CRAN for use with R version 2.4.0 or later. Purpose of the packages: The Matrix package provides S4 classes and methods for sparse and dense matrices. The lme4 package provides functions for fitting and assessing linear or generalized linear mixed effects models (also called multilevel models). Like the Matrix package,

Hello list, I'm trying to figure out how exactly the specification of nested random effects works in the lmer function of lme4. To give a concrete example, consider the rat-liver dataset from the R book (rats.txt from: http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/ ). Crawley suggests to analyze this data in the following way: library(lme4) attach(rats) Treatment <-

Using lmer() on my data results in an error. The problem, I think, is my model specification. However, lm() works ok. I recreated this error with a more simple dataset. (See code below.) # word and letter recognition data # two within factors: # word length: 4, 5, 6 letters # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in 6-letter words) # one dependent variable: #

> On Nov 14, 2017, at 5:13 AM, Fix Ace via R-help <r-help at r-project.org> wrote: > > Dear R Community, > My data have 3 conditions and each condition has 6 replicates. I am trying to fit my data for a linear mixed model using the lmer function from lme4 package to find the random effects of the replicates; Better venue for this question might be SIG-mixed-models. See the link

Hi all, I have installed R on SUSE 11.1 and if I tried to add some packages e.g. lme4 I got a message: ________________________________________________________________________________________ * installing *source* package ?lme4? ... ** libs gcc -std=gnu99 -I/usr/lib64/R/include -I/usr/local/include -I"/usr/lib64/R/library/Matrix/include"

Hello, I've run 7 candidate models using mixed-effects logistic regression with the lmer function from the lme4 package, and I'm getting the following error for 5 of those models: Warning message: In mer_finalize(ans : false convergence (8). The candidate models all run with the same data, just slightly different formulas (for AIC model selection). Can anyone explain this error to

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1