similar to: using stimulate(model) for parametric bootstrapping in lmer repeatabilities

For a Monte Carlo study I need to extract from an lme model the estimated standard deviation of a random effect and store it in a vector. If I do a print() or summary() on the model, the number I need is displayed in the Console [it's the 0.1590195 in the output below] >print(fit) >Linear mixed-effects model fit by maximum likelihood > Data: datag2 > Log-likelihood:

Folks In trying to use lmer for a hierarchical model, I encountered the following message: Error in UseMethod("VarCorr") : no applicable method for 'VarCorr' applied to an object of class "mer" foo.mer <- lmer(y ~ TP + (TP|M),data=joe.q) > head(joe.q[,1:5]) TP M AB Trt y 1 1 Jan A NN 19.20002 2 1 Jan A NN 19.06378 3 1 Jan A NN

Hello, I would appreciate *any* ideas on this problem. I'm the maintainer of a package ("subselect"), which on CRAN's Daily Package Checks is OK on all flavours of R, except r-devel Linux x86_64, where there is a "memory not mapped" segfault with the very first example that is tried out (output below). Additionally, a user with an AMD64 machine has just reported a

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.

Hello! I am performing coupling of chains in MCMC and I need the same value of seed for two chains. I will show demo of what I want: R code, which might show my example is: niter <- 3 nchain <- 2 tmpSeed <- 123 for (i in 1:niter) { # iterations for (j in 1:nchain) { # chains set.seed(tmpSeed) a <- runif(1) cat("iter:", i, "chain:", j,

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

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 don't know the details of pls (in the pls.pcr package, I assume), but if you use validation="CV", that says you want to use CV to select the best number of components. Then why would you specify ncomp as well? Andy > From: ryszard.czerminski at pharma.novartis.com > > When I try to use ncomp parameter in pls procedure I get > following error: > > >

I have a loop that I would like to use to extract the "stddev" for each itteration so I can average the "stddev" for all the runs. It would be helpful to know how to extract the "stddev" for each run from the VarCorr. Thanks MCruns<-1000 sighatlvec<-rep(NA,MCruns) sighatbvec<-rep(NA,MCruns) sighatevec<-rep(NA,MCruns) for(mc in 1:MCruns) {

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

If you are indeed using lme and not lmer then the needed function is VarCorr(). However, 2 recommendations. First, this is a busy list and better emails subject headers get better attention. Second, I would recommend using lmer as it is much faster. However, VarCorr seems to be incompatible with lmer and I do not know of another function to work with lmer. Hence, a better email subject header

Hello, I'm working on a problem using nested for-loops and I don't know if it's a problem with the order of the loops or something within the loop so any help with the problem would be appreciated. To briefly set up the problem. I have 259 trees (from 11 different species, of unequal count for each species) of which I am trying to predict biomass. For each tree species I have 10000

Dear list, I am having some problems with extracting Variance Components from a random-effects model: I am running a simple random-effects model using lme: model<-lme(y~1,random=~1|groupA/groupB) which returns the output for the StdDev of the Random effects, and model AIC etc as expected. Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the

I've only been using R on and off for 9 months and started using the glasso package for sparse covariance estimation. I know the concept is to shrink some of the elements of the covariance matrix to zero. However, say I have a dataset that I know has some underlying "baseline" covariance/correlation (say, a value of 0.3), how can I change or incorporate that into to the

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

Hello. I've developed an algorithm in R which I need to package. The implementation uses S4 objects and it's divided in 5 files. Everything is working fine when I load the files into the R console but when I try to make a package I get an error that I don't quite understand. Here's what I do: *1.* in R console, I do and get: > package.skeleton(name='remora') Creating

Hello, My problem is, that I have to build hundreds of GARCH models to obtain volatility forecasts. I would like to run a loop, that would build those forecasts for me. There is no problem, with writing only the results of the forecasts, but I'd like to have stored results of the models in some file, that I could check later, what are the models like, to be able to compare if I should use

Hi, I am currently preparing a new version of my package papeR. When I run R CMD check using the development version of R I get the following note: Package in Depends field not imported from: ?nlme?, ?lme4?, ?survival? These packages needs to imported from for the case when this namespace is loaded but not attached. I now have problems to fix this issue. It is easy to get rid of two of the

Dear R-help list members I gathered longitudinal data on fish behaviour which I try to analyse using a multi level model for change. Mostly, I am following Singer & Willett (2003), who provide also the S/R code for their examples in the book (e.g. http://www.ats.ucla.edu/stat/Splus/examples/alda/ch4.htm). Of course I am interested in change over time, but I am also very much interested in

Here is a piece of code fitting a model to a (part) of a dataset, just for illustration. I can extract the random interaction and the residual variance in group meth==1 using VarCorr, but how do I get the other residual variance? Is there any way to get the other variances in numerical form directly - it seems a litte contraintuitive to use "as.numeric" when extracting estimates,