similar to: nlme package, fixing variance.covariance matrix of residuals

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Dear list, I was trying to create a VarClass for nlme to work with Fay-Herriot (FH) models. The idea was to create a modification of VarComb that instead of multiplying the variance functions made their sum (I called it varSum). After some fails etc... I found that the I was not getting the expected results because I needed to make sigma fixed. Trying to find how to make sigma fixed I run into

Dear R-project team, I have a problem with the function varFixed() of the nlme-package. I used it with the squid-data of Zuur et. al 2009 (chapter 4), to fix increasing residuals (heterogenetiy) (see graph in the email) I get the message ' Variance function structure of class varFixed with no parameters, or uninitialized Could you help me please? Kind regards Franz

Hi guys! I have a problem regarding a binary logistic hierarchical model I am trying to use. The model contains various covariates that depend on the location the response was measured at but do not depend on time (year). I also have a spatial covariate that depends both on location and time. I have been trying to use the lme4 pack but the package only allows me to model variance covariance

Hello everybody, I have a dataset where each row has number of subjects and that gives me natural weights for the variance function. Additionally I see that variance increases with Age, which is a regressor. So using gls I have weights=varFixed(~1/n) but don't know how to include the extra effect of the regressor. Fitted values show a quadratic curve vs. age, not sure if that helps.

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

I want to extract the variance-covariance matrix of an nlme model of a dataset. The object is to pass this to mvrnorm to create pseudo- replicates of the original data. I note the nlme package has a getVarCov method available for lme objects but not nlme objects. Is the vcov function in the base stats package suitable? If so, why is the additional getVarCov provided? thank you Rob

Hi, How extract the Variance and Covariance Matrices D of random effects and R of error in the lme object? Thanks in advance. Alexandre Galv??o

-- Dear R users, I have data on around 2000 birds from 3 generations for which I know an individual's pedigree (i.e. the relationship it shares with other individuals e.g brother, uncle, mother) and also a pedigree based on foster-families, because half broods were removed from their nest of origin and placed in a foster parent's nest. From this I want to model two types of random

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Dear R-expert I'm fitting a non linear model (energy allocation model to individual growth data) using your nlme routine. For each individual I have thus a number of observations (age and size) to which I fit the nonlinear function, with random effects for the individuals on the estimated parameters (individual as the grouping factor). The sampling of these individuals was stratified (size

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hallo, I have a question that is more statistic related than about nlme and R functioning. I want to fit a complicated nonlinear model with nlme with several different measures of transpiration taken on each of the 220 trees grouped in 8 families. The unknown parameters of the model are three + their variances (and covariances). I want to estimate the variances among families of the

Greetings- I am in the process of constructing a nonlinear model using nlme. The model is attempting to fit a nested data structure from some public-opinion data (the data are from individuals nested within organizations). The question I have is fairly simple (I hope). The data were collected in two stages: a 15,000-subject randomly-sampled telephone interview (the SCREEN), with 2,517 subjects

Dear R users: I'm happy to announce the first CRAN release of the clubSandwich package: https://cran.r-project.org/web/packages/clubSandwich clubSandwich provides several variants of the cluster-robust variance estimator for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator of Bell and McCaffrey (2002). The package includes

Dear R users: I'm happy to announce the first CRAN release of the clubSandwich package: https://cran.r-project.org/web/packages/clubSandwich clubSandwich provides several variants of the cluster-robust variance estimator for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator of Bell and McCaffrey (2002). The package includes

Hi, my name is Ellen. I want to ask you about R Code. I got a code for extracting a pixel value, but I can't compile it..  It is said "Error in is.data.frame(x) : object 'lena' not found" Here is the original full code: library(pixmap) lena <- read.pnm("oldlennablur.pgm") write.table(lena@grey,"mylenna", quote=FALSE, row.names = FALSE, col.names=

Hello, I experimented the Metafor and Meta packages in the scope of replacing Excel for meta-analysis. I performed the first working example provided in Michael Borenstein's book "Introduction to Meta-Analysis" with Excel, Metafor and Meta. The numbers given by my spreadsheet, which I validated from Borenstein's book, conrespond quite closely to those given by Meta, but