similar to: Specifying a more complex covariance matrix in lme or lmer

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

Hello, running a mixed model in the package LME4, lmer() Panel data, have about 322 time periods and 50 states, total data set is approx 15K records and about 20 explanatory variables. Not a very large data set. We run random intercepts as well as random coefficients for about 10 of the variables, the rest come in as fixed effects. We are running into a wall of time to execute these models.

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

Dear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and

Hello, I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when expanding the number of covariates I usually encounter the following error message. gm8 <-

I would like to be able to use lme to fit random effect models In which some but not all of the random effects are constrained to be independent. It seems as thought the pdMat options in lme are a promising avenue. However, none of the existing pdMat classes seem to allow what I want. As a specific example, I would like to fit a random intercept/slope mixed model to longitudinal observations in

I want to make a stacked bar plot with one bar for two variables from my data "chir", the two variables have about 100 values like no, yes and na. I want to show how many no, yes and na they both have together with the stacked bar. I tried to melt these to variables first like this: melt1=melt(data_chir, measure.vars=c("N1_re", "N2_re"), var="zpd") but it

Could anyone help with a linear mixed model fitting problem ? The model is : Y= Xp + Zu + e where X, Z are known design matrix, p is fixed effect factor, u is random effect, u~ (0, G) , e~(0,R) The main problem is , I want to fix the covariance matrix G to be a constant times a known covariance matrix A, G = c*A (c is positive constant, A is a predefined matrix with values manually set by

Alguien tiene experiencia en escribir una pdMat. Para aquellos que no lo recuerden son las matrices de covarianzas de los efectos aleatorios que ajusta la función lme de la librería nlme Estas matrices tiene especial importancia en aplicaciones de genética de poblaciones y en particular en mapeo de asociación. Pinheiro y Bates dicen que el usuario puede crear sus propias pdMat y sugiere como

Dear All, I wish to model random effects that have known between-group covariance structure using the lme() function from library nlme. However, I have yet to get even a simple example to work. No doubt this is because I am confusing my syntax, but I would appreciate any guidance as to how. I have studied Pinheiro & Bates carefully (though it's always possible I've missed

Hello, I'm very sorry for my repeated question, which i asked 2 weeks ago, namely: i'm interested in possibly simple random-part specification in the call of GLMM(...) (from lme4-package) i have a random blocked structure (i.e. ~var.a1+var.a2+var.a3, ~var.b1+var.b2,~var.c1+var.c2+var.c3+var.c4), and each one part of it i would like to model as Identity-structure matrix. So i had,

Hi, I've got a problem that sounds a lot like this, http://r.789695.n4.nabble.com/Re-R-R-2-12-0-hangs-while-loading-RGtk2-on-FreeBSD-td3005929.html under windoze 7. but it seems to hang with this stack trace, #0? 0x77830190 in ntdll!LdrFindResource_U () ?? from /cygdrive/c/Windows/system32/ntdll.dll building goes as follows, $ ./R CMD INSTALL --no-test-load nlme_3.1-97.tar.gz *

Hello all, I'm trying to create covariance matrices that, when processed via CFA methods (in the sem package) will produce exact fit with simple structure down to poor fit with cross loadings. What is the best way to do this? I don't really need to have the exact loop code, but maybe an explanation of how to make a few of the matrices or an explanation of the rationale behind performing

I have fit a model with a more complex covariance structure, but the fit reports a false convergence. I have read from past posts that this can be an indication of over-specification. I went ahead and fit a model with a simpler covariance structure. It doesn't seem like I can compare the two likelihoods or the AIC or BIC to compare the two model since the one model had false convergence.

Hello, has someone written by chance a function to extract the variance-covariance matrix from a lmer-object? I've noticed the VarCorr function, but it gives unhandy output. Regards, Roel de Jong

Hi, I usually use lme from the nlme library. Now I have read an article about lmer in Rnews and lmer seemed to me more comfortable to use. Unfortunately, I didn't find out how to use covariance structures (e. g. corSymm(), corAR1()). Is there a way to use them similarly as in lme ? Is it implemented ? If somebody knows, please let me know. Thank you very much in advance, Stephan

Anybody knows what functions can be used to calculate variance/covariance with complex numbers? var and cov don't seem to work: > a 1 V1 0.00810014+0.00169366i V2 0.00813054+0.00158251i V3 0.00805489+0.00163295i V4 0.00809141+0.00159533i V5 0.00813976+0.00161850i > var(a) 1 1 1.141556e-09 Warning message: In var(a) : imaginary parts discarded in

Hi everyone, I'm trying to fit a generalized linear mixed effects model (logistic) in R and am having some trouble specifying the covariance structure for the random effects. I'm using glmer, which by default assumes an unstructured relationship between the random effects, but I want the structure to be a multiple of an identity. Here is my code: glmer(y ~ 1 + (x1 + x2 + x3 + x4

Folks: Suppose I have 3 random effects, A,B, and C. Using the older lme() function (in nlme) it was possible (using the pdMat classes) to specify that they are uncorrelated with identical variances. Is it possible to do this with lmer? My understanding is that if I specify them as lmer( y ~ ... + (A|Grp) + (B|Grp) + (C|Grp)) then they are uncorrelated but have different variances. Motivation: