similar to: Extracting Variance Components

Hi, I'm still having problems putting the variance components of my model in to a data frame, it is a continuation of this discussion, http://r.789695.n4.nabble.com/ANOVA-problem-td4609062.html, but now focussed on the problem of extracting variance components. I have got my mixed effects model now /narrow$line<-as.factor(narrow$line) rg.lmer <- lapply(split(narrow,

Hello, I've had no luck finding an R package that has the ability to estimate a Tobit model allowing for heteroskedasticity (multiplicative, for example). Am I missing something in survReg? Is there another package that I'm unaware of? Is there an add-on package that will test for heteroskedasticity? Thanks for your help. Cheers, Alan Spearot -- Alan Spearot Department of Economics

Hey, all: Do we have a convenient command(s) to extract the variance components from a fitted model by lm (actually it's a nexted model)? e.g.: using the following codes we could get MSA,MSB(A) and MSE. How to get the variance component estimates by command in R rather than calculations by hand? A<-as.vector(rep(c(rep(1,5), rep(2,5), rep(3,5), rep(4,5), rep(5,5)),2))

I'm running simulation using lme and sometimes the estimated variance-covariance matrix is not positive definite so that the intervals function won't work for the random effect coefficients. I've tried varcomp from the ape package but this does not return all the coefficients. How can I extract the random effect coefficients without using intervals? Rick B.

Dear Lister, I understand merge() can be used to join 2 data frames based on 1 variable. But how about merge based on more than 2 variables? Thank you so much! -- WenSui Liu (http://spaces.msn.com/statcompute/blog) Senior Decision Support Analyst Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center [[alternative HTML version deleted]]

Hi All. I need a bit of help extracting the residual error variance from the VarCorr structure from lmer. #Here's a 2-way random effects model lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat) #Get the structure vc.fit <- VarCorr(lmer.1) #results in..... $person 1 x 1 Matrix of class "dpoMatrix" (Intercept) (Intercept) 0.7755392 $rater 1 x 1 Matrix

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, Here is the question: For example, using the "petrol" data offered with R. pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol) pet3.lme$sigma gives the residual StdDev. But I can't figure out how to extract the "(intercept) StdDev", although it is in the print out if I do "summary(pet3.lme)". In

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 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 am trying to use varcomp to obtain the variance partitioning across different nested levels of random effects (say x,y and z). I get the three variance components (for each of my along with an additional one called 'within' from varcomp output. I am using the 'scale total variance to 1' option and though the within component is small, it does form a part of what explains the

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

I can ask my question using and example from Chapter 1 of Pinheiro & Bates. > # 1.4 An Analysis of Covariance Model > > OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] > fm1OrthF <- + lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) > summary( fm1OrthF ) Linear mixed-effects model fit by REML Data: OrthoFem AIC BIC

Dear All, I need to calculate the variance components in a mixed effect model (one fixed and one random effect) with REML (maximizing the proportion of the likelihood that does not depend on the fixed effects). In S+ there is the varcomp function, but I would like to do it in R. Is there a way to do that? Thanks! Katalin ___ Katalin Csillery Division of Biological Sciences University of

I had a question about scan in R. For better code readability, I would like to have lines in the block of data to be scanned that are commented - not just lines that have a comment at the end. For example #age, weight, height 33,128,65 34,56,155 instead of having to do something like 33,128,65 #age, weight, height 34,56,155 Is this at all possible?

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

R 2.10 Windows Vista Is it possible to run a variance-components analysis using lme? I looked at Pinheiro and Bates' book and don't see code that will perform these analyses. If the analyses can not be done using lme, what package might I try? Thanks, John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology

Hello, I have some data covering contaminant concentrations in fish over a time period of ~35 years. Each year, multiple samples of fish were taken (with varying sample sizes each year). Ultimately, I want an estimation of the variance between years, and the variance within years + random effects. I used a linear mixed model to estimate these variances, but after reading a number of different

Hi all, We've just had a paper accepted for publication in Nature. We used R for 95% of our analyses (one of my co-authors sneaked in some GenStat when I wasn't looking.). The preprint is available from the Nature web site, in the open peer-review trial section. I searched Nature for previous references to "R Development Core Team", and I received no hits. So I tentatively

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 )