similar to: understanding I() in lmer formula

> Is there a difference between I(x*y) and I(y*x) ? > I have a call to lmer that results in this complaint: > Error in is.alpha2.subordinate * ~z.min.co.res : A reproducible example would help ... In the absence of that, check the classes of the two variables in I() and, if you run the product on the command line, either inside I() or not, what's the class of the result? If that

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

I saw different specifications for Random Effects and I'm confused about the use of "/" and the use of "(0+...|)" . Let say we have a nested structure where some countries have some several plants in different states and we measure the reaction to a drug. The list of Countries = USA, France, Italy The States for USA = Michigan, Florida, California The States for France

I am newbie in R, trying to understand and compare syntax in nlme and lme4. lme() model from the nlme package I am interested in is: lme.m1.1 = lme(Y~A+B+C,random=~1|D/E,data=data,method="ML") (for simplicity reason, I am giving generic names of factors) If I understand well, there are three fixed factors: A, B and C, and two random factors: D and E. In addition to that, E is nested in

Dear all, I was wondering if someone could help me with the specification of a formula including both nested and crossed effects in the same model in lmer. I have one predictor variable, x, and three grouping factors, a,b and c. Factor b is nested in a but is partially crossed with c. Also, I'm interested in an interaction between the crossed effects b and c. I specified a formula in lmer as

Dear R-users, May I seek some suggestions from you. I have a long programme written in R with several 'for' loops inside. I just want to get them out by any elegant way (if there is!) to reduce the computational time of the main programme. For instance, is there any smart way for the following programme that will lessen time?

Hey all - I am a newbie on mixed-effects models. I want to estimate the following model: Y_it = alpha_0t + alpha_1t*X_it + e_it alpha_0t = gamma_00 + u_0t alpha_1t = gamma_10 + gamma_11*W_it + u_1j Where Y is my outcome, X is my level-1 predictor, and W is my level 2 predictor. I am not sure if I am doing it right. Is this the correct specification of the formula? model = lmer(Y ~ X + X:Y + (

Dear All, May I seek your suggestion on a simple issue. I want to draw vertical lines at some positions in the following R plot. To be more specific, I wish to draw vertical lines at d=c(5.0,5.5,6) and they should go till p=c(0.12,0.60,0.20) . I haven't found any way out, though made several attempts. Please run the following commands first if you are interested in!

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Hello, I am working on using if statements. What is the error message telling me here and how do I correct for it? I have tried various combinations of quotes. Thank you. Sincerely, Mary A. Marion #Find critical values crit<-function(n,alpha,type) { if (type==twoSided) { alpha2=alpha/2 tL<-qt(alpha2,n-1) tU<-qt(1-alpha2,n-1) } if (type==Lower) { tL<- -9999

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

Hello, I recently began using R and the lme4 package to carry out linear mixed effects analyses. I am interested in the effects of variables 'prime','time', and 'mood' on 'reaction_time' while taking into account the random effect 'subjects.' I've read through documentation on lme4 and came up with the following formula for use with lmer:

Hello R and BUGS users, I am writing a heirarchical model in R to send to BUGS via R2WinBUGS and I am finding it difficult to get the model to run. I seem to be having two problems. 1) I can't seem to send variables classed as factors (Month), is there a way do this? 2) Checking the Log in WinBUGS I can see that the model is Syntactically correct, but Bugs is not able to recognise the the

http://dovecot.org/releases/2.1/alpha/dovecot-2.1.alpha2.tar.gz http://dovecot.org/releases/2.1/alpha/dovecot-2.1.alpha2.tar.gz.sig This is actually looking rather stable. I'm still calling it "alpha" in case I think of doing some more API changes, but I think the next one will be called "beta1", hopefully soon followed by "rc1". - Statistics gathering

http://dovecot.org/releases/2.1/alpha/dovecot-2.1.alpha2.tar.gz http://dovecot.org/releases/2.1/alpha/dovecot-2.1.alpha2.tar.gz.sig This is actually looking rather stable. I'm still calling it "alpha" in case I think of doing some more API changes, but I think the next one will be called "beta1", hopefully soon followed by "rc1". - Statistics gathering

Dear all, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

I have both 1.0 alpha1 and alpha2 installed on my test mail server. SSL/TLS does not work on alpha2. I'm using the same config file for both. No errrors in dovecot.log either. Is there a way to increase debugging info without recompiling?

There is a thread on this topic already: http://finzi.psych.upenn.edu/Rhelp10/2010-August/250934.html I'm rather mystified by a similar problem and wondering whether I've overlooked something obvious. I'm running with latest versions of R and all packages, and latest version of JAGS running under Windows 7. Here's the problem. I have some source code. It's given below -

I often use simple nested random-effect models for interlaboratory data. The variance components are important things to know. Is there an R function or package that gets variance components from lm objects? Or can someone point me to a method of doing so?

http://dovecot.org/releases/1.2/alpha/dovecot-1.2.alpha2.tar.gz http://dovecot.org/releases/1.2/alpha/dovecot-1.2.alpha2.tar.gz.sig Mainly the same fixes as in v1.1.3 -> v1.1.4 plus some more v1.2-specific fixes. Not many people have been testing v1.2 releases. Wonder if there's something I could do to improve that. I guess v1.2 is kind of missing those killer features that would get a