similar to: Obtaining Estimates of the Random Effects Parameters

Dear List, Is there a way to extract variance components from lmeObjects or summary.lme objects without using intervals()? For my purposes I don't need the confidence intervals which I'm obtaining using parametric bootstrap. Thanks, Mike [[alternative HTML version deleted]]

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

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 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

Hello all R-users! In Jun-2005, I find the follow discussion about using of LME function ( in NLME library ) for fitting non-replicate data The thread: ANOVA vs REML approach to variance component estimation http://tolstoy.newcastle.edu.au/R/help/05/06/6498.html Someone expose the follow problem: # non-replicate data y <- c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3, 0.5, -1.4,

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

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

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hello, I am trying to use varcomp to decompose the variance across multiple nested levels on a lme object. I am able to successfully do this and when I view the varcomp object I can see the individual values / estimates for the variance at different levels. However, I want to be able to extract each of them separately, as I need to build a confidence interval using bootstrapping on the sample

I am caught in a mental trap. Why isn't the between groups variance estimated (0.0038) to be around the value with which I generated the data (0.0002)? Thanks Toby set.seed(76589437887) fph = 0.4 Sigh = sqrt(0.0002) Sigi = sqrt(0.04) ci = 1 fpi = matrix(,7200,3) for (i in 1:90) { fph = rnorm(1, fph, Sigh) for (k in 1:80) { fpi[ci,1:3] = matrix(c(i, k, rnorm(1, fph, Sigi)),1) ci

Hi try str(varcompobject) to see structure of this object. You can extract parts by standard R means. Cheers Petr > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Sharada > Ramadass > Sent: Tuesday, August 8, 2017 3:33 PM > To: r-help at r-project.org > Subject: [R] how to extract individual values from varcomp? > >

I have a data set like this in one .txt file (cols separated by !): APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! APE!KKU!684! APE!VAL!! APE!UASU!! APE!PLA!1! APE!E!10! APE!TPVA!17122009! APE!STAP!1! GG!KK!KK! it contains over 14 000 000 records. Now

I'm trying to fit a generalized mixed effects model to a data set where each subject has paired categorical responses y (so I'm trying to use a binomial logit link). There are about 183 observations and one explanatory factor x. I'm trying to fit something like: (lmer(y~x+(1|subject))) I also tried fitting the same type of model using glmmPQL from MASS. In both cases, I get a

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 fitting a mixed effects model: fit.1 <- lme(y~x,random=~1|id,data=df) There are two different observations for each id for both x and y. When I use plot(fit.1), there is a strong increasing linear trend in the residuals versus the fitted values (with no outliers). This also happens if I use random=~x|id. Am I specifying something incorrectly? Rick B.

I'm using groupedData from nlme. I set up a groupedData data.frame with outer=~group1. When I try to plot with outer=TRUE, I get "subscript out of bounds." This happens most of the time. When it works, I get spaghetti-type plots for comparing groups. But I don't understand why it doesn't usually work. > longa.mod.1.gd <- groupedData(mod1.logit~time|

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

Dear all, It is my understanding that using 'importFrom' in the NAMESPACE of a package allows to avoid conflicts between different packages defining objects with identical names. However, I can still see conflicts while loading the package using 'library'. Here is a toy example, with a package 'foo' importing 'as.igraph' from the igraph package, and 'nj'

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've just downloaded and installed the RAqua onto my Mac and I have R up and running fine. Now I'm trying to get 'ape' downloaded and installed and having quite the difficulty. I am following the directions specified under 'Installing packages' where packages can be downloaded and installed from within R. I get the following error messages: 1: argument