similar to: mixed models: correlation between fixed and random effects??

Hello, I am looking for references about mixed models built on rank transformed data. Did anybody ever consider this topic? Thank you, Bruno ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Bruno L. Giordano, Ph.D. CIRMMT Schulich School of Music, McGill University 555 Sherbrooke Street West Montr?al, QC H3A 1E3 Canada http://www.music.mcgill.ca/~bruno/

Hello, I am using logistic discriminant analysis to check whether a known classification Yobs can be predicted by few continuous variables X. What I do is to predict class probabilities with multinom() in nnet(), obtaining a predicted classification Ypred and then compute the percentage P(obs) of objects classified the same in Yobs and Ypred. My problem now is to figure out whether P(obs) is

Hello, I looked for R packages which focused on order-restricted statistical inference, but I could find only the isoreg() function. I would need to test whether the means in my (repeated measures) data follow a given order, e.g. A<B=C<D. I took a look at the monograph by Barlow et al. (1972) on this topic and found that for my case the null hypothesis is always A=B=C=D. This might be

Hello, I am writing down a general function to implement the bootstrapF method for repeated measures anova. I am passing the function several data frames: y = dependent subj = subject identifiers b = between-subjects factors (number = NB) w = within-subjects factors (number = NW) after grouping of all these variables in a single data frame the aov() call looks like this:

Dear list, I am trying to use lme to build the analogue of the following SAS MIXED random specification: random int+Variable1+Variable2 /subject = Subject group=Condition type=vc; which gives a Condition-blocked heterogeneity in the random effects variance-covariance matrix. Needless to say, I have a hard time in specifying Condition-specific heterogeneities in the variance-covariance

Dear list, is anybody aware of R implementations of the overlapping clustering methods by Arabie and Carroll (ADCLUS, MAPCLUS and INDCLUS)? I found around only their Fortran implementation of the MAPCLUS model, which comes practically without any documentation. Alternatively, has anybody used this Fortran program, and is willing to share some knowledge about its use? Thank you, Bruno

Dear R users, I m quite a novice in using R for factor analysis and I would need some help to choose the right function. I have a contingency table and I would like to perform a Correspondence analysis on this table, followed by a hirarchical clustering of my variables projected in on the first principal components. Here are my question : - what is the more appropriate function to do so ...

dfhfsdhf at ghghgr.com I ran a simple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second

Hi all, To follow up on an older thread, it was suggested that the following would produce confidence intervals for the estimated BLUPs from a linear mixed effect model: OrthoFem<-Orthodont[Orthodont$Sex=="Female",] fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) fm1.s <- coef(fm1OrthF.)$Subject fm1.s.var <- fm1OrthF. at bVar$Subject fm1.s0.s <-

Hi, In view of the problems I was having with DomU network timeout after a live migration (I posted that problems here a while ago but never got anything except from private emails) I finally updated my Debian/Squeeze Dom0s last night to a new kernel, from 2.6.32-23 to 2.6.32-26. Now live migration just hangs...Any ideas? Xen-related Debian packages (all from Debian repositary, except drbd

It would be great to know if and where an R code for Common Principal Component Analysis is available. Thanks, Daniel Daniel Berner Redpath Museum & Dept. of Biology McGill University 859 Sherbrooke St. W. Montreal, QC, H3A 2K6 Canada Phone: 514-398-4086 ext. 00908 Fax: 514-398-3185 Email: daniel.berner at mail.mcgill.ca

Working on Windows I have had to deal with CSV files that, unfortunately, contain embedded Control-Zs, i.e. ASCII character 26 in decimal, and the readLines() function in R on Windows (2.15.2 and 3.0.0) appears to truncate at the control-Z. There is no problem at all on Ubuntu Linux with R 3.0.0. Am I mistaken or is this genuine? # Create a small file with embedded Control-Z h3 <-

Hi all, What is (or how to I change) the maximum width of a pdf image. I am trying to make a pdf image: pdf(file="out.pdf",width=200,height=20) and I can't get the image to be more that 200 inches wide i.e. I get an empty out.pdf . Is there a solution around this. Thanks. Vince -- +-----------------------------------------------------------+ | Vincenzo Forgetta

Hello ... I tried to generate .fig figures with R, using the xfig() function ...When I open the figures using xfig software under linux ... the foreground color change strangely ... whereas when I display the same figure in R or when I saved it in using postscript() function there is no problem. Any idea about the behavior of the colors when using xfig ?? Thanks in advance Christophe Grova

I've downloaded R for windows (9.0.1) and it is great! I've converted all my lecture notes for my GLM course to run on R (they are available on my web page below). I must admit I particularly like the default contrast options, which are identical to GLIM. Also I like the gl function - very useful! I have a couple of questions/bugs: 1. predict.glm doesn't work, but predict.lm does -

I am playing with the a 1-way anova with and without the "-1" option. I have a simple cooked up example below but it behaves the same on a more complex real example. From what I can tell: 1) the estimated means of the different levels are correctly estimated either way (although reported as means with the -1 and as contrasts without the -1 as expected) 2) the residuals are

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Dear R-list, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction

I have data consisting of binary responses from a large number of subjects on seven similar items. I have been using lmer with (crossed) random effects for subject and item. These effects are almost always (in the case of subject, always) significant additions to the model, testing this with anova. Including them also increases the Somers' Dxy value substantially. Even without those