similar to: lme4 - higher level

Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand

data from multilevel units,first sample the class ,and then the student in calss.following is the 2-level model. and the level-1 model deals with the student,and the level-2 model deals with the class level the students belong to. Level-1 Model Y = B0 + B1*(ZLEAD) + B2*(ZBUL) + B3*(ZSHY) + R Level-2 Model B0 = G00 + U0 B1 = G10 + G11*(ZWARMT) + U1 B2 = G20 + G21*(ZWARMT) + G22*(ZABLET) +

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

Hi R Users, When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3),

-----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, 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

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,

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)

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 All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

Dear R listers, I know I'm breaking the rules by asking a "homework" related question-- I hope you'll forgive me. I am a social psychology graduate student, and the only one in my department who uses R. I successfully completed my multiple regression and structural equation modeling courses using R (John Fox's car and sem packages were a big help, as was his book).

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 am currently using R 2.1.1 under Windows and I do not seem to be able to load the current versions of lme4/Matrix. I have run 'update.packages.' I understand this is still experimental software but I would like access to a working version. Thanks. Chuck R Output: > library(lme4) Loading required package: Matrix Error in lazyLoadDBfetch(key, datafile, compressed, envhook) :

Hi, I just installed version 2.11.1 on my Mac OS X 10.5.2. When I try running R from my xterm, I get the following error: Error in loadNamespace(name) : there is no package called 'lme4' Fatal error: unable to restore saved data in .RData I am returned to the terminal prompt and can not run R. I can open this version of R from the R console and tried installing the lme4 source

I have R version 2.10.1 (2009-12-14) installed on Ubuntu HH (version 8.04) I installed lme4 from the Ubuntu respository. When I executed the library(lme4) command, R responds that lme4.so was not found. I then uninstalled lme4 and tried again from cran. When the program tried to compile, there was a message that -llapack could not be found, so the package failed to install. If I execute the

help.search("groupedData") says that it's part of the lme4 package, but it appears not to be there (details below). Is this because lme4 is new and (perhaps) still under development? > update.packages() trying URL `http://cran.r-project.org/bin/windows/contrib/1.9/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 19113 bytes opened URL downloaded 18Kb >

it takes a long time to load the lme4 package.anyone else encounter this problem? > system.time(library(lme4)) 佋佖伻侂佇佽佉仾伒伳伋佁伡伃伆侟仯伜Matrix 佋佖伻侂佇佽佉仾伒伳伋佁伡伃伆侟仯伜lattice [1] 19.90 0.30 25.56 NA NA > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status Patched major 2 minor

The impression I get from the list and the references I've perused is that nlme is being phased out in favor of lme4, but lme4 still doesn't have a complete feature set yet. What I'm still fuzzy on, being a relative R newbie, is: (a) what features in nlme are currently missing in lme4 (b) what's the projected time frame on getting them implemented. If anyone can answer my naive

I am unable to install package lme4, after several attempts to do so using various repository URLs. Just to make sure everything works fine with proxy, connection, etc, I installed ggplot2 and it worked fine. I am using command install.packages("lme4", lib="/myRlibs"), optionally using contrib argument with different URLs. Error message the I get is Warning message; In