search for: mlwins

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

...rdized random effects, but different standardized results, the difference lies in what denominator is used. In R, we know what denominator is used and it is the standard deviation of the variance component for the random effect. If you do the steps above, you will solve for the denominator used in mlWins calculation. This will solve your problem. Harold

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

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) -------------------------------------------------------------------------------------------

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

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

Hello, how do I obtain standard errors of variances and covariances of the random effects with LME comparable to those of for example MlWin? I know you shouldn't use them because the distribution of the estimator isn't symmetric blablabla, but I need a measure of the variance of those estimates for pooling my multiple imputation results. Regards, Roel.

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

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",

Is anyone on this list aware of discussions about locking down/securing R? My colleagues and I are working with health statistics in an office that disallows many useful tools (e.g. emacs, vim, perl, make) on the grounds that they represent a security risk. We are considering pushing back, but we are worried that if we attract the attention of the Powers That Be to the reality that R allows

> On Tue, 12 Sep 2000, Magill, Brett wrote: > > > 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) > > The lme()

With lme4, use of mcmcsamp can be insightful. (Douglas Bates drew my attention to this function in a private exchange of emails.) The distributions of random effects are simulated on a log scale, where the distributions are much closer to symmetry than on the scale of the random effects themselves. As far as I can see, this is a straightforward use of MCMC to estimate model parameters; it is not

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]]

Perhaps I am not looking in the right place, but I am looking for a way to use lmer() to run a multilevel model that incorporates sampling weights. I have used the Lumley survey package to use sampling weights in the past, but according to post I found online from Thomas Lumley in mid-2012, R is currently not equipped to be able to do this. His post is here:

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.

Division of Research at Kaiser Permanente, Oakland, CA Seeking a Statistician/Senior SAS and R Programmer Schedule: Full-time regular, 40 hours/week, Monday through Friday Send Resume to: Patricia.Kipnis@kp.org Position Summary: Under general supervision, this position exists to assist the organization in making strategic data-related decisions by analyzing, manipulating, tracking, internally

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