similar to: Significance levels of variance terms

Hi R-users, I am using lme to fit a linear mixed model with the nlme package, does anyone know if it is possible to obtain standard error estimates of the variance components estimators and an adequate method to test the significance of the variance component? Thanks, Berta. [[alternative HTML version deleted]]

Dear R team, I would like to do a multivariate meta-analysis in R using the nlme package. In meta-analysis I fix the residuals to known sampling errors. As I do a multivariate analysis, I have a variance-covariance matrix of sampling errors. Unfortunately, via varFixed I can only fix a vector of sampling errors and no matrix. In the R package metafor using the rma.mv function I can insert the

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

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Hi, I need to correct for ar(1) behavior of my residuals of my model. I noticed that there are multiple gls models in R. I am wondering if anyone has experience in choosing between gls models. For example, how should one decide whether to use lm.gls in MASS, or gls in nlme for correcting ar(1)? Does anyone have a preference? Any advice is appreciated! Thanks, -- Tom [[alternative HTML

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 have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Hello, I have been asked to help perform a meta-analysis with individual- and aggregate-level data. I found a nice article on this, and the idea is easy to understand: use a mixed effects models, but for the studies where there is only aggregate level data, force the variance to be that which was observed. Unfortunately, I am struggling to see how to implement this in R. I am familiar with

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

I have two straightforward questions about linking in the man pages for R packages: First, is it possible to link from within parts of the documentation that are not the \seealso section? For example, I would like to have something like: \arguments{ \item{correlation}{an optional \code{corStruct} object describing the within-group correlation structure; the available classes are given in

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

I am using a two-factor ANOVA model with random factor effects including the interaction, i.e. the factors are crossed. I would like to be able to generate all four variance components along with approximate confidence intervals using the NLME package. However, I do not know how to specify the random option because of two problems. First, I do not know how to enter the interaction term into the

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

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

-- 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 R developers, recently I have written a wishlist bug report for nlme containing a patch that adds the variance function structure s2(v) = t1^2 + t2^2*v^2 where v denotes the variance covariate, s2(v) denotes the variance function evaluated at v, and t, t1 and t2 are the variance function coefficients. The covariate can also be the fitted response. The idea that the residual variance

Dear R-nlme expert We need two pieces of information about the fitting of a nlme model which we cannot extract from the R help files and would be most grateful if you could help us. We fit an energy allocation growth model with 4 parameters to individual growth curves using the nlme routine. We thus have repeated age and size measurements of individuals and therefore allow for random

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, I have been using the function lme() of the package nlme to model grouped data that is auto-correlated in time and in space (the data was collected on different days via a moving monitor). I am aware that I can use the correlation classes corCAR1 and corExp (among other options) to model the temporal and spatial components of the auto-correlation. However, as far as I can tell, I can only