similar to: Sampling Weights and lmer() update?

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

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

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

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

Dear all, I am struggling with a problem which I have been reading on the forums about and it did not seem to me that there is a precise answer to my question. However, I still hope there is one. I am working with http://timss.bc.edu/ PIRLS data and trying to conduct multilevel analysis. There are different weights for each level of analysis in the PIRLS dataset (e.g. there is a school

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

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

Hello -- I am curious if lmer can be used to fit a multilevel factor model such as a two-parameter item response model. The one parameter model is straightforward. A two-factor model requires a set of factor loadings multiplying a single random effect. For example, a logit model for the ith subject responding correctly to the jth item (j=1,..,J) is logit[p(ij)] = a1*item1(i) + ... + aJ *

Dear all, I am trying to replicate some multilevel models with binary outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>. The relevant Stata output can be found at <http://www.uni- koeln.de/~ahf34/stataoutput.txt>. First, you will find the unconditional model,

Hi everyone, I'm posting again, since I haven't got an answer (yet :( ). According to R help, manova does not support the inclusion of the Error() term in the formula call. I have repeated measures data for two dependent variables, so this means I can't account for subject variance in time?. Any lights? Thanks in advance, Rodrigo Abt, Department of Economic Studies, SII, Chile.

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

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

Por favor, estoy aprendiendo R para aplicarlo exclusivamente en modelos jerárquicos lineales o multinivel. Todo lo que me puedan ayudar les quedaré muy agradecido. Cordialmente, Jairo P.D.: Presento mis disculpas por el correo anterior [[alternative HTML version deleted]]

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until

list, i am using lmer to fit multilevel models and trying to use anova to compare the models. however, whenever i run the anova, the AIC, BIC and loglik are different from the original model output- as below. can someone help me out with why this is happening? (i'm hoping the output assocaited with the anova is right!). thank you, darren > unconditional<-lmer(log50 ~ 1 + (1 |

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

It is not a pure R question,but I hope some one can give me advices. I want to use analysis my data with the multilevel model.The data has 2 levels---- the second level has 52 units and each second level unit has 19-23 units.I think the sample size is quite small,but just now I can't make the sample size much bigger.So I want to ask if I use the multilevel model to analysis the data set,will

Hi Sir I am very new user of R for the research project on multilevel logistic regression. There is confusion about bugs() function in R and BUGS software. Is there any relation between these two? Is there any comprehensive package for Multilevel Logistic modelling in R? Please guide in this regard. Thank You RAZA --------------------------------- Boardwalk for

I'm trying to figure out how to use lmer to fit models with factors that have some nesting and some non-nested groupings. For example, in this paper: http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf we have a logistic regression of survey respondents' political preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, state (51 states within 5