similar to: Multilevel modeling using R

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: >

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

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

I have been using lme to model data with multiple nested random effects and continuous response variables however I also have data with a binary response variable, binomial errors and multiple levels of nesting of random effects (e.g. site/block/quadrat/year), is there a package available which will do this? Jim Lindsey's package "repeated" appears to be only able to cope with 2

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

Hello, I have difficulties to deal with multilevel model. My dataset is composed of 10910 observations, 1237 plants nested within 17 stations. The data set is not balanced. Response variable is binary and repeated. I tried to fit this model model<- glmmPQL( y ~ z1.lon*lun + z2.lat*lun + z1.lon*lar + z2.lat*lar + z1.lon*sca + z2.lat*sca +z1.lon*eta + z2.lat*eta, random = ~ lun + lar + sca

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 *

Hi there, In multiple regression one way to view R^2 is as (the square of) the correlation between original y's and the estimated y's. Suppose you fit a multilevel model with random intercept for each cluster. Would it be valid to compute an R^2 by using fixed effects plus the group intercepts to reduce the residuals? I suspect this has been done and, given its absence from the lmer

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

Dear friends, this time I have a problem with using waba function. Firstly, I'll explain you my situation. In the survey a gruop of supervisors judge the dipendents of a company. One supervisor reported on more than one subordinate. Thus, I need to show that lack of independence is not a problem, and a reviewer told me to use WABA. The question is, how? In which way i can build my X and Y?

Dear R saviors, kindly address to this problem, I would really appreciate any takers. I am trying to resolve this issue of IMR in clustered (multilevel) cross-sectional panel data for more than two months now,. The characteristics of my dataset are as follows: - some 900 000 individuals - total of 60 countries - cross-sectional time series at the country level max 10 years, not all