similar to: Multilevel logistic regression using lmer vs glmmPQL vs.gllamm in Stata

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,

Dear all, I have two factors 'country' and 'status' which I would like to plot via barchart (lattice). 'status' consist of three different levels and should be the grouping variable, i.e. there should be drawn three different panels and within each panel a barchart of 'country'. barchart(daten$COUNTRY|daten$STATUS),

Hi, I'm trying to perform a power simulation for a simple multilevel model, using the function glmmPQL in R version 2.8.1. I want to extract the p-value for the fixed-effects portion of the regression, but I'm having trouble doing that. I can extract the coefficients (summary(fit)$coeff), and the covariance matrix (summary(fit)$varFix), but I can't grab the p-value (or the

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Hello I have a problem when fitting a mixed generalised linear model with the lmer-function in the Matrix package, version 0.98-7. I have a respons variable (sfox) that is 1 or 0, whether a roe deer fawn is killed or not by red fox. This is expected to be related to e.g. the density of red fox (roefoxratio) or other variables. In addition, we account for family effects by adding the mother

Full_Name: Chanseok Park Version: 1.8.1 OS: linux-gnu Submission from: (NULL) (130.127.112.183) digamma with any negative value does not give a right answer. It gives -1.797693e+308 for any negative arguments. For example, digamma(-1.1) gives -1.797693e+308. The right answer should be 10.15416 This bug can be easily fixed by using the following digamma identity. digamma(x) = digamma(1-x) -

Hi, <foreword> I am about to set up Samba 3.0.14a on Linux as PDC wit LDAP backend for our faculty. However, first tries have only partly been successful. First I added samba LDAP-Schema attributes to existing account, created their Samba passwords with smbpasswd and it worked so that normal users could log in via the windows network neighborhood and use the shares. But, I couldn't

The Intel compiled version also fails the below test: > ###------------ Very big and very small > umach <- unlist(.Machine)[paste("double.x", c("min","max"), sep='')] > xmin <- umach[1] > xmax <- umach[2] > tx <- unique(outer(-1:1,c(.1,1e-3,1e-7)))# 7 values (out of 9) > tx <- unique(sort(c(outer(umach,1+tx))))# 11 values

Hi -- I'm using lmer for binomial data. I am trying to replicate estimates provided by Agresti (2002, Categorical data analysis, Wiley) using abortion data in table 10.13 (estimates provided in table 12.3 p. 505). I fit the same model using these three commands: a1 <- lmer(resp ~ sex + option1 + option2 + (1|id), data=abort,family=binomial, method = c("AGQ")) a2 <-

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

Hi R-Help. I am working on a data set with a 3-level nested structure. I have individuals nested in households and multiple observations on each individual. I assume that the individuals inside a given household are correlated and that the individuals are correlated with themselves over time. The data is not balanced. I have computed a GLMM with logit link function and two random normal

Hello everyone, I am trying to regress applicants' performance in an assessment center (AC) on their gender (individual level) and the size of the AC (group level) with a multi-level model: model.0 <- lme(performance ~ ACsize + gender, random = ~1 | ACNumber, method = "ML", control = list(opt = "optim")) I have 1047 applicants in 118 ACs: >

Hello, Have someone performed a bootstrap in a multiple-mediator model? I am trying to compute a bootstrap in a multiple and multilevel mediation. Up top now, I have developed bootstraps in random coeffient models, but I am very lost concerning the mediation. Could someone to provide me some ideas about syntax in R? Thank you very much in advance, Bea

Dear all, I posted this two years ago, getting no answers or suggestions - now I am trying again, hoping something new is available in R. I am interested in an application of linear multilevel model with unequal selection probabilities at both levels. Do you know if there is an R function for multilevel pseudo-maximum likelihood estimation? Or is it possible to obtain these estimates using

Hi R folks (Dr. Bates in particular), In August 2005, Dr. Bates mentioned that the documentation for lme4 "will be in the form of a book with the working title 'Multilevel Modeling in R'" and I'm just wondering if there is an estimated date of publication or if it's still a long way off. The Rnews article does a great job of introducing the package, but I'm

Hi, I have a number of multilevel models and I would like some Latex outputs of them. I usually use the "apsrtable" package, but it does not accept "lme" outputs. Neither does the "mtable" function in the "memisc" package. Is there any good alternative that I am missing? Thanks, Jonas

Hello everyone, I am employing the waba method from the multilevel package for obtaining a within-group correlation (Description: http://bg9.imslab.co.jp/Rhelp/R-2.4.0/src/library/multilevel/man/waba.html). Does anybody know a way or a calculation for obtaining a significance value for that correlation? And another question: Does anybody know whether it is possible to save individual

I have fit a model with glmmPQL function in MASS library. I fit a binomial longitudinal response variable nested in 17 stations. I would like to know how I can obtain elements of diagnostic checks about these models in order to choose best model. I use summary(), but can I use other functions like in lme, for example anova? I would be thankfull for all the insights. Fabrizio Consentino.

Longitudinal and Multilevel Data in R and Stan ICPSR short course: May 28 to June 1, 2018 May 28: Introduction to R by John Fox May 29 to June 1: Longitudinal and Multilevel Data in R and Stan by Georges Monette Sponsored and organized by ICPSR, University of Michigan and held at York University in Toronto, Ontario Course description:https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0226

Hi, I am working with a multiple-item questionnaire. I have previously done item-level multiple imputation using MICE in R and right now I am attempting duplicate-scale imputation based on the guidelines listed in Enders's applied missing data analysis book. I use MICE to do MI as it allows me to specify school effect as I am working with multilevel data; my respondents come from different