similar to: Extracting correlation in a nlme model

Hello, assume I have an "unstructured" text line from a connection. Unfortunately, it is in string format: R> x [1] "\talpha0\t-0.638\t0.4043\t0.4043\t-2.215\t-0.5765\t-0.137\t501\t2000" How can I extract the data included in this string object "x" in order to get the elements for the parameter vector called "alpha0", i.e. -0.638 0.4043 0.0467

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 R-help group, I would like to model directly following random effect model: Y_ik = M_ik + E_ik where M_ik ~ N(Mew_k,tau_k^2) E_ik ~ N(0,s_ik^2) i = number of study k = number of treatment --------------------------------------------------------------------------- I have practiced using the command from 'Mixed -Effects models in S and S-plus'

Please pardon an extremely naive question. I see related earlier posts, but no responses which answer my particular question. In general, I'm very confused about how to do variance decomposition with random and mixed effects. Pointers to good tutorials or texts would be greatly appreciated. To give a specific example, page 193 of V&R, 3d Edition, illustrates using raov assuming pure

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

Hello, I have a question concerning the R-function chisq.test. For example, I have some count data which can be categorized as follows class1: 15 observations class2: 0 observations class3: 3 observations class4: 4 observations I would like to test the hypothesis whether the population probabilities are all equal (=> Test for discrete uniform distribution) If you have a small sample size

I would like to fit the following model: logit(p_{ij}) = \mu + a_i + b_j where a_i ~ N(0, \sigma_a^2) , b_j ~ N(0, \sigma_b^2) and \sigma_a = \sigma_b. Is it possible to fit a model with such a constraint on the variance components in glmer? -- View this message in context: http://r.789695.n4.nabble.com/glmer-question-tp4351829p4351829.html Sent from the R help mailing list archive at

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

Hi, as you can see in the topic, I am trying to fit a normal mixture distribution with the approach suggested by Hamilton (1991). Since I couldn't find any existing packages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea

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

Dear R-users, consider a 2-level linear mixed effects model (LME) with random intercept AND random slope for level 1 AND 2. Does anybody know how to calculate Intraclass-coefficient (ICC) for highest (innermost) level 2 ??? In the literature, I did not find an example for these kind of komplex models. For 1-level Random-Intercept models it would be easy: ICC = variance due to the clustering