similar to: Extract Variance Components

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

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? > >

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

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'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

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.

Hi Everyone, I am using ASReml to fit a spatial model. I do not have all the components of ASReml when I call; names(summary()) e.g. names(summary(fcov.asr2)) [1] "call" "loglik" "nedf" "sigma" "varcomp" I am trying to get the coefs but I get "NULL". Does anybody know the reason? Any help would be much appreciated. Regards

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 all, I'd like to remove the space between the x-axis and the 0 of the y-axis in the hist function. I saw the previous post https://stat.ethz.ch/pipermail/r-help/2012-September/324177.html suggesting adding the x-axis at position 0 (or the lowest value of y) after making the histogram. Unfortunately, it doesn't work in my case as I want to use it with a histogram with a ylim starting

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 all, I have written a script in R that simulates genetically informative data - it is posted on my website and available to the public. This is my first time to write a script for use by others and am learning that it isn't as easy as it seems. To the issue. My script runs fine on my machine and on a server I have access to, but a user has written me saying that it crashes the first time

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

I''m testing the http module in console. BUt I got the following error. Anyone knows why? >> Net::HTTP.get_print ''www.google.com'', ''index.html'' SystemStackError: stack level too deep from C:/InstantRails/ruby/lib/ruby/1.8/net/http.rb:451:in `newobj'' from C:/InstantRails/ruby/lib/ruby/1.8/net/http.rb:451:in

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,

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]]

http = Net::HTTP.new(HOST, PORT) When I try to create a new Net::HTTP object, I get a SystemStackError: "stack level too deep". The trace shows a long list of "/usr/lib/ruby/1.8/net/http.rb:333:in `newobj''", which when I look at the code seems to say that Net::HTTP keeps calling its own newobj method recursively, with some Proxy object involved? I''m not

Hi all, I didn't get a response to my post of this issue a week ago, so I've tried to clarify: When I use lme to analyze a model of nested random effects, the variance estimates of levels higher in the hierarchy appear to have much more variance than they should. In the example below with 4 levels, I simulate variance in level 2 (sd=1.0) and level 4 (sd=0.1), but levels 1 and 3 do

Dear R users: I've a problem with lme function, when I want to model an unbalanced two-way anova, with 2 random factors say t and b. My two models are: model1- y(ijk) = beta+b(i)+t(j)+epsilon(ijk) model2- y(ijk)= beta+b(i)+t(j)+b:t(ij)+epsilon(ijk) beta overall mean effect The data.frame is X t b med celda 1 1 10 1 1 1 12 1 1 1 11 1 1 2 13 2 1

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