similar to: negative variances

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, 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

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.

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

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:

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

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

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 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

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

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 all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.

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.

Hello, I've been trying to answer a problem I have had for some months now and came across multivariate multilevel modeling. I know MPLUS and SPSS quite well but these programs could not solve this specific difficulty. My problem: 9 correlated dependent variables (medical symptoms; categorical, 0-3), 5 measurement points, 10 time-varying covariates (life events; dichotomous, 0-1), N ~ 900.

Hi R users I want to use the lme package for a multilevel analysis on the following example: > math<-c(2, 3,2, 5, 6 ,7 , 7) > sex<-c(1, 2, 1, 2, 2, 2, 1) > school_A<-c(1,1,1,2,2,2,2) > school_B<-c(10,10,10,20,20,20,20) > mydata<-data.frame(math, sex, school_A, school_B) > mydata School_A and school_B are two different school characteristics, math is an

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]]

I am fitting individual growth models using nlme (multilevel models with repeated measurements nested within the individual), and I am trying to calculate the Pseudo R-squared for the models (an overall summary of the total outcome variability explained). Singer and Willett (2003) recommend calculating Pseudo R-squared in multilevel modeling by squaring the sample correlation between observed and

I have a collection of functions, class definitions and methods which I would like to test systematically for their correctness after changes to their code, and also after major R revisions. I believe that the correct term for these systematic tests (as opposed to more informal tests) is a 'test suite'. Does anyone [apart from Pat Burns :-) ] have code, or templates, or specific

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print, plot, extraction, append, and related functionality are available. Feedback and suggestions are welcome. Giles Heywood

I have uploaded to CRAN a new package named 'its' (Irregular Time-Series). It implements irregular time-series as an S4 class, extending the matrix class, and records the time-stamp of each row in the matrix using POSIX. Print, plot, extraction, append, and related functionality are available. Feedback and suggestions are welcome. Giles Heywood