similar to: Accessing the fixed- and random-effects variance-covariance matrices of an nlme model

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

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

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

In a newly downloaded version (today) of R-2-4-0alpha, with all packages from CRAN also installed today, I get: > library(Matrix) Erro en loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: BIC, anova, coef, confint, deviance, fitted, fixef, formula, head, lmer, logLik, mcmcsamp, plot,

Dear list, I'd like to create a visual plot of a clmm() I've fitted using the 'ordinal' package in R. It's possible to do this with a glm() by using the 'effects' package. For example: library(effects) data(BEPS) mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS) eff <- effect("age", mod, default.levels=100) plot(eff,

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

Dear All, I would like to ask you to help me with my memeory. I remember using some function that would list all the possible methods I could apply to an object. Say, if I had an object of class=lme, it would tell me that that I could do stuff like qqnorm(myobjct), or VarCorr(myobject). In general, a very complete list. I though this list of all possible methods would pop out by typing

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

In some trial simulation work I need to create batch files that will repeatedly generate pseudoreplicate datasets and then create non- linear mixed effects models using nlme. Inevitably these models sometimes fail to converge but I need the batch file to simply move on to another simulation rather than abort. I am using the try() function as in model<-try((nlme(...))) which handles

Dear all, I'm trying to store/extract the mean& standard error of the fixed effects parameter and the variance of the random effects parameter from "lmer" procedure from mlmre4 package developed by bates n pinheiro. while storing fixed effects parameter is straight forward, the same is not true for storing the variance parameter of the random effects. kindly help me ~prabhu

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

Hello, Is there a way to obtain the variance-covariance matrix of the estimated parameters from GLM? my.glm<-glm(mat ~X,family = binomial, data =myDATA) out1<-predict(my.glm,se.fit = TRUE) std<-out1$se.fit se.fit is for getting the standard errors of the estimated parameters (\betas). Is there a way to get the variance-covariance matrix of the estimated parameters? Many thanks,

-- 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

dfhfsdhf at ghghgr.com I ran a simple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

In "lm" command, we can use "vcov" option to get variance-covariance matrix. Does anyone know how to get variance-covariance matrix in nlrq? Thanks, Kate [[alternative HTML version deleted]]

Hi all, I have the output of summary() of an lme object called "lme.exp1", for example ############################################# > summary(lme.exp1) Linear mixed-effects model fit by REML Data: DATA Log-restricted-likelihood: -430.8981 Fixed: fixed.exp1 .... Random effects: Formula: ~-1 + mu1 + log.sig1 | animID Structure: Diagonal mu1 log.sig1