similar to: what does the within component of varcomp (ape library) output indicate?

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

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? > >

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

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]]

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.

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,

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))

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

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

Hello, Samba 2.2.2 is Installed on Solaris ...which is acting as a PDC. I am able to add WinNT m/c to the samba PDC. How to add Win2K PC to Samba PDC? Please Reply.. Regards & Thanx, Sharada __________________________________________________ Do You Yahoo!? Yahoo! - Official partner of 2002 FIFA World Cup http://fifaworldcup.yahoo.com

Hello R experts, I haven't found anything like the S function 'varcomp' as described in W.N. Venables & B.D. Ripley's 'Modern Applied Statistics ...' for R; does something comparable exist for R, or is planned for future releases? More generally, are there libraries with post-anova test procedures, like Student-Newman-Keuls? Or do those of you who frequently use

Given a set of data: > names(data) [1] "city" "house" "visit" "value" I am looking for a way to compute the variance components of the nested model (ie, visit 1 at house 2 at city 3 isn't related to visit 1 and house 2 at city 4), but different houses in the same city may be related, and different visits to the same house are probably

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

Dear all, I don't understand the following behaviour: Running compar.gee (in library ape ) with and without the option 'data', it give me different results Example: .... Start R .... > load("eiber.RData") > ls() [1] "gee.na" "mydata" "mytree" > library(ape) > # runnig with the option data= mydata > compar.gee(alt ~ R,

Hello, I need help - 1. What should be the configuration of nsswitch.conf file when I want to make a Unix box both PDC (by configuring Samba as PDC) as well a NIS server. 2. what should be the value of following parameters in nsswitch.conf file? a) passwd = nis files group = nis files or b) passwd = nis [NOTFOUND=return] files group = nis [NOTFOUND=return]

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

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

I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.