similar to: lme4 and Variable level detection

1. One general question for general discussion: Is HLM6 faster than lmer? If so, why? What should I watch for to spot the differences? I'm always advocating R to students, but some faculty members are skeptical. A colleague compared the commercial HLM6 software to lmer. HLM6 seems to fit the model in 1 second, but lmer takes 60 seconds. My first thought was that LM6 uses PQL by default,

I'm hoping that this is a relatively easy question for someone familiar with the lme4 package. I'm accustomed to using HLM software and writing a simple 2 level [null] equation like this: L1 - Yij = b0 + e L2 - b0 = B00 + u0 The following command in R provides results that are identical to the HLM program. results <- lmer( Y ~ 1 |id , PanelData4) I can't

Hi R users! I am trying to learn some multilevel analysis, but unfortunately i am now very confused. The reason: http://www.ats.ucla.edu/stat/hlm/seminars/hlm_mlm/mlm_hlm_seminar.htm http://www.ats.ucla.edu/stat/sas/seminars/sas_mlm/mlm_sas_seminar.htm and MlmSoftRev. pdf from mlmRev package. >From what i see, the first two links seem to declare the level one variable as a random part (i

Does anyone know of code to conduct hierarchical (that is, multi-level) models using R. Beyond simply requiring a nested design, I want to model explicitly the covariance between levels as is done in such multi-level modeling software as HLM or MLwin and discussed in Goldestein (1999) available online at http://www.arnoldpublishers.com/support/goldstein.htm (a nice and free resource for anyone

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, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

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 list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Dear R-users, I'm running a lmer model using the lme4 package. My dependent variable is dichotomous and I'm using the "binomial" family. The results are slightly different from the HLM results based on a Bernoulli distribution. I read that a Bernoulli distribution is an extension of a binomial distribution. Is that right? If so, how can I adapt my R model to a Bernoulli

Dear all, I am trying to get the emperical bayes estimates together with their standard errors out of lme4. Up to now I have used MLwiN to get these estimates. I have fitted the following - very simple - model, just to find out how this works. test<-lmer(y~(1|subject),data,REML=F) ranef(test,postVar=T) str(ranef(test,postVar=T) If I use the formulation of the emperical bayes estimates and

When playing around fitting unconditional growth models using R and MlwiN today, I produced two different sets of estimates that I can't reconcile and wondered if anyone here has an idea: The data is two-level repeated measures data with measures nested within child. There are two measures per child. I've fit an unconditional growth model as in Singer and Willet (2003) that allows for

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) +

Dear R experts, I had a question which may not be directly relevant to R but I will be grateful if you can give me some advices. I ran a two-level multilevel model for data with repeated measurements over time, i.e. level-1 the repeated measures and level-2 subjects. I could not get convergence using lme(), so I tried MLwiN, which eventually showed the level-2 variances (random effects for

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

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

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

Hello, This is a follow up question to my previous one http://tolstoy.newcastle.edu.au/R/e4/help/08/02/3600.html I am attempting to model relationship satisfaction (MAT) scores (measurements at 5 time points), using participant (spouseID) and couple id (ID) as grouping variables, and time (years) and conflict (MCI.c) as predictors. I have been instructed to include random effects for the

Hi I am trying to convert SAS codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt

Hi, I compare results of a simple two-level poisson estimated using lmer and those estimated using MLwiN and Stata (v.9). In R, I trype: ------------------------------------------------------------------------------------------- m2 <- lmer(.D ~ offset(log(.Y)) + (1|pcid2) + educy + agri, male, poisson) -------------------------------------------------------------------------------------------

Full_Name: Zivan Karaman Version: 2.7.2 OS: Windows XP Submission from: (NULL) (195.6.68.214) I'm puzzled by the readRegistry function. Shouldn't the "hive" argument be something like c("HLM", "HCR", "HCU", "HU", "HCC", "HPD") rather than c("HLM", "HCR", "HCU", "HU",