similar to: a simple mixed model

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

Hi, We released individual birds into a room with 2 trees. We counted the number of visits to each of the 2 tree. One of the trees is always a control tree and the other tree is either treatment 1, treatment 2 or treatment3 or treatment 4. Ind Treat ContrTree ExpTree Total visits 1 1 11 16 27 1 2 6 9 15 1 3 5 13 18 1 4 11 25 36 2 1 2 3 5 4 1 6 7 13 4 3 4 4 8 4 4 2 5 7 6 1 1 1 2 6 4 5 16 21

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

I have a question about some strange results I get when using lmer to build a logistic mixed effects model. I have a data set of about 30k points, and I'm trying to do backwards selection to reduce the number of fixed effects in my model. I've got 3 crossed random effects and about 20 or so fixed effects. At a certain point, I get a model (m17) where the fixed effects are like this

Dear list I need your help: Execuse me for my limited R knowledge. #example data set set.seed (134) lm=c(1:4) block = c(rep(lm,6)) gen <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4),rep(5, 4),rep(6, 4)) X1 = c( rnorm (4, 10, 4), rnorm (4, 12, 6), rnorm (4, 10, 7),rnorm (4, 5, 2), rnorm (4, 8, 4), rnorm (4,7, 2)) X2 = X1 + rnorm(length(X1), 0,3) yvar <- c(X1, X2) X <- c(rep( 1,

Dear R List, I am trying to use mixed models to analyze an intervention and want to make sure I am doing it correctly. The intervention is for lowing cholesterol and there are two groups: one with an intervention and one without. The subjects were evaluated a differing amount of time, so there were between 2 and 7 visits, equally spaced. Sample output is below. TC is total cholesterol,

Hi,I have tumor growth curve data for a bunch of different mice in various groups. I want to compare the growth curves of the different groups to see if timing of drug delivery changed tumor growth.I am trying to run a mixed models with repeated measures over time with each mouse as a random effect with linear and quadratic terms for time.This took me a long time to figure out and I just wanted to

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

Dear R-help list members I gathered longitudinal data on fish behaviour which I try to analyse using a multi level model for change. Mostly, I am following Singer & Willett (2003), who provide also the S/R code for their examples in the book (e.g. http://www.ats.ucla.edu/stat/Splus/examples/alda/ch4.htm). Of course I am interested in change over time, but I am also very much interested in

Dear All, I'me having (much) trouble understanding why it happened and answering a referee's comment to part of a submitted manuscript. I've tried to google for help but... I'm really confident that although this is a R-Help list someone can help me! I used R to do an ANCOVA w/ RNA/DNA as the dep var, sl as the indep var and gut (a factor w/ levels: prey and empty) as the

Hi all, I've spent quite a lot of time searching through the help lists and reading about how best to run perform a 2-way ANOVA with unbalanced data. I realize this has been covered a great deal so I was trying to avoid adding yet another entry to the long list considering the use of different SS, etc. Unfortunately, I have come to the point where I feel I have to wade in and see if someone

First of all thank you for the responses. I appreciate the suggestions i have received thus far. Just to reiterate I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say

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 )

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovered that I can reproduce certain items with lmer but not

hello R help I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say something about the variability between regions (fixed factor, contrast of means). The data is as

Dear Rxperts, Running the following code: ======================================================= twlo=10; twhi=20; wt=154; vd=0.5; cl=0.046; tau=6; t=3; F=1; wtkg <- wt/2.2 # convert lbs to kg vd.pt <- wtkg * vd # compute weight-based vd (L) cl.pt <- wtkg * cl # compute CL (L/hr) k <- cl.pt/vd.pt # compute k (hr^-1) cave <-

Hi all, I am currently doing a consistency analysis using an lmer model and trying to use parametric bootstrapping for the confidence intervals. My model is like this: model<-lmer(y~A+B+(1|C/D)+(1|E),binomial) where E is the individual level for consistency analysis, A-D are other fixed and random effects that I have to control for. Following Nakagawa and Scheilzeth I can work out the