similar to: [R-pkgs] New versions of Matrix and lme4 packages for R-2.4.0

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,

Crossed random effects: > lmer( y ~ (1 | i1) + (1|i2) ,data=dta) Linear mixed-effects model fit by REML Formula: y ~ (1 | i1) + (1 | i2) Data: dta AIC BIC logLik MLdeviance REMLdeviance 91.18 94.84 -42.59 85.2 85.18 Random effects: Groups Name Variance Std.Dev. i1 (Intercept) 0.068224 0.26120 i2 (Intercept) 0.463112

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 While I'm not an R expert, I have used R on Windows XP. Now I've moved to Ubuntu (Intrepid), and I'm trying to configure R to work with the Gelman and Hill _Data Analysis Using Regression and Multilevel/Hierarchical Models_. So far, it's not working. I start by following the instructions for installing arm and BRugs at

Hello all, I just started working with the lme4 package to estimate a multilevel logistic regression and am planning to use this package for a cross-classification / multiple-membership model. I haven't found many worked examples and am trying to figure out how to add variables to the higher-level part of the model. Consider the following example: test.1 <- lmer(y ~ b1 + b2 + b3 +

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

I am making a little GUI for lme4, and I was wondering if there is a function that automatically detects on which level every variable exists. Furtheremore I got kind of confused about what a random effects model actually calculates. I have some experience with commercial software packages for multilevel analysis, like HLM6, and I was surprised that lme4 does not require the user to specify the

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Hello everyone, I want to estimate the reliability *of the level*-*1 random coefficients (the one *reported in the HLM output) using the software R. Does anyone know how to get this statistic from R? I'm using the function "lmer" of the package "lme4" to estimate multilevel models. I tried to use the formula but I can't find specific information such as the sigma

I just installed R 2.4 on a windows machine and installed the lme4 package version 0.9975-1. Unfortunately this version requires the 0.9975-2 version of the Matrix package which I cannot seem to find. It seems the newest version I can find is 0.9975-1. Is there somewhere I could find this newer version. Mark Lyman

Dear list, I am trying to fit some mixed models using packages lme4 and nlme. I did the model selection using lmer but I suspect that I may have some autocorrelation going on in my data so I would like to have a look using the handy correlation structures available in nlme. The problem is that I cannot translate my lmer model to lme: mod1<- lmer(y~x + (1|a:b) + (1|b:c), data=mydata)

R Users, The d/p/q/r functions for the bridge distribution are now available in bridgedist. When a random intercept follows the bridge distribution, as detailed in Wang and Louis (2003) <doi:10.1093/biomet/90.4.765 <http://dx.doi.org/10.1093/biomet/90.4.765>>, a marginalized random-intercept logistic regression will still be a logistic regression with marginal coefficients that are

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 ListeRs, I am trying to fit a heteroscedastic multilevel model using lmer{lme4- package). Take, for instance, the (fictive) model below. lmer(test.result ~ homework + Sex -1 + (1 | School)) Suppose that I suspect the error terms in the predicted values to differ between men and women (so, on the first level). In order to model this, I want the 'Sex'-variable to be random on

I've tried unsuccessfully to install lme4 on two different Redhat systems but I've not noticed anything on this list to indicate anyone else has had similar problems. * Installing *source* package 'lme4' ... ** libs gcc -I/usr/local/R-2.4.0/include -I/usr/local/R-2.4.0/include -I/usr/local/include -fpic -g -O2 -std=gnu99 -c init.c -o init.o In file included from init.c:1:

I have uploaded version 0.96-1 of both Matrix and lme4 to CRAN. The source package should migrate to CRAN over the weekend and binary packages should be available some time next week. As for previous releases, the versions of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade

I have uploaded version 0.96-1 of both Matrix and lme4 to CRAN. The source package should migrate to CRAN over the weekend and binary packages should be available some time next week. As for previous releases, the versions of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Hi R folks (Dr. Bates in particular), In August 2005, Dr. Bates mentioned that the documentation for lme4 "will be in the form of a book with the working title 'Multilevel Modeling in R'" and I'm just wondering if there is an estimated date of publication or if it's still a long way off. The Rnews article does a great job of introducing the package, but I'm