similar to: mixed effect-models

Hi, I'm using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 5.1 year 2002 month 06 day 17 language R and I would like to apply: > update.packages() trying URL

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

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

Dear Experts, May be the problem is still solved, however I tried to find the answer in the archives: I use: > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 7.1 year 2003 month 06 day 16

Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662

Summary: proc mixed vs. lme The objective of this summary is to help people to get more familiar with the specification of random effects with proc mixed or lme. Very useful are the examples of Ramon Littell's book: "SAS System for Mixed Models (1996)" (http://ftp.sas.com/samples/A55235) The same data set's are kindly made available by Douglas Bates in the

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

Dear R-helpers, First of all I have nothing against the French language! But now my problem, yesterday I installed R 2.1.1 and I had to experience that my RGui is speaking French. My windows locals is French (Switzerland). I'm used to English and I want to reset my RGui to English. I was seeking for the solution in the archives, however not successfully. By the way the searchable archives

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

Happy new month, everyone! I am planning to write a NIH grant proposal to study ways to speed Monte Carlo based maximum likelihood algorithm for hierarchical models with a focus on generalized linear mixed models (GLM with random effects). I thought it would be nice and also increase the chance of funding if I could produce an R package in the process. I understand that Prof. Pinheiro ang Bates

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 using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R and I get the following error with: library(Design) df <- list(pre=c(0,, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

I am one of only 5 or 6 people in my organization making the effort to include R/Splus as an analysis tool in everyday work - the rest of my colleagues use SAS exclusively. Today, one of them made the assertion that he believes the numerical algorithms in SAS are superior to those in Splus and R -- ie, optimization routines are faster in SAS, the SAS Institute has teams of excellent numerical

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

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

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The

Dear all, Has anyone written R code for partial Mantel Tests- as described for instance in Legtendre & Legendre (1998) ? In other words, in a community ecology analysis, I would like to calculate the correlation between two dissimilarity matrices, controlling for a third distance matrix representing geographical distances between sites. Thanks! Christophe Bouget Biodiversité et gestion des

To account for likely differences between families in naturalization rates, we fitted a generalized linear mixed model, using PROC NLMIXED in SAS10, with the naturalization rate per genus (that is, the number of naturalized species in a genus as a proportion of the total number of introduced species in a genus) as the response variable, a variable coding genera as containing at least one native