similar to: question regarding variance components

> -----Original Message----- > From: Federico Calboli > Sent: Tuesday, January 20, 2004 5:40 PM > To: r-help > Subject: [R] matrix exponential: M^0 > > I would like to ask why the zeroeth power of a matrix gives me a matrix > of ones rather than the identity matrix: > > > D<-rbind(c(0,0,0),c(0,0,0),c(0,0,0)) > > D<-as.matrix(D) > > D >

Check out textplot in the gregmisc package ... Federico Calboli <f.calboli@ucl.ac.uk> Sent by: r-help-bounces@stat.math.ethz.ch 07/13/2004 12:06 PM Please respond to f.calboli To: r-help <r-help@stat.math.ethz.ch> cc: Subject: [R] plotting a table together with graphs Dear All, I would like to ask how to add a table to a

[ This was posted to the R-packages list (which I moderate), but definitely doesn't belong there. Martin Maechler ] ------- start of forwarded message ------- From: Federico Calboli <f.calboli at ucl.ac.uk> To: r-packages at stat.math.ethz.ch Subject: how to upload Date: 15 Jul 2004 18:30:26 +0100 Dear All, I just finished a add on lib called Malmig, to calculate Malecot

Dear All, how do I compute the left eigenvector of a matrix? I gather that "eigen" computes the right eigenvectors... Regards, Federico Calboli -- ================================= Federico C. F. Calboli PLEASE NOTE NEW ADDRESS Dipartimento di Biologia Via Selmi 3 40126 Bologna Italy tel (+39) 051 209 4187 fax (+39) 051 251 208 f.calboli at ucl.ac.uk

Dear All, I would like to ask how to customize the graph corresponding to a procrustes analysis. I have to distance matrices, that I transform to two set of coordinates by means of muti dimensional scaling: library(mva) c1<-cmdscale(mat.dist1) c2<-cmdscale(mat.dist2) I vant to rotate c2 on c1, and I use the "procrustes" analysis from the {vegan} library. library(vegan)

Dear All, I would like to ask how to get the Sum of Squares from fitted lme model. I appreciate that lme maximises the likelihood (or REML) and uses likelihood ratio tests, but I just fail why I could not get the SS if I want them. I could use lm to calculate them, but it looks quite pointless to fit a second (and wrong in a way) model for something so trivial. I know that the issue has been

Dear All, I apologize if the following has been already asked, but I could not find anything in the archives. I am running Debian Sid (on an Athlon XP), with R 1.8.1.cvs.20040307-1 (it describes itself as "R 1.9.0 experimental" when I fire R up) and I am unable to load the library "nlme" (I apt-getted r-recommended, so the library is on the system) >library(nlme) Error in

Dear all, I am putting together a business case for R and I would like to know if there exist statistics on the distribution of R in academia, research and commercial institutions. The same information about S-plus would be welcome. Please reply only if you have consistent and certain information. Thank you very much, giovanni

Dear all, We compared the gene expression level in two conditions: high (H) and low (L), using real-time PCR. We had 3 samples of each condition, and we quantified each sample twice (i.e. a technical replication). Our data looks like: H1 H1 H2 H2 H3 H3 L1 L1 L2 L2 L3 L3 We want to test if the level in H vs L is significantly different. I would like to use ANOVA and I guess a nested model with

Dear All, I am in the somewhat unfortunate position of having to reproduce the results previously obtained from (non-metric?) MDS on a "kinship" matrix using Statistica. A kinship matrix measures affinity between groups, and has its maximum values on the diagonal. Apparently, starting with a nxn kinship matrix, all it was needed to do was to feed it to Statistica flagging that the

Hello everybody, I'm seeing some strange behavior on R 1.8.1 on Intel/Linux compiled with gcc 3.2.2. The p-value calculated from the chisq.test function is incorrect for some input values: > chisq.test(matrix(c(0, 1, 1, 12555), 2, 2), simulate.p.value=TRUE) Pearson's Chi-squared test with simulated p-value (based on 2000 replicates) data: matrix(c(0, 1, 1,

I use linux debian testing for which the latest debianized version of R is 1.8.1. Therefore I installed: r-base-core_1.8.1-0.cran.1_i386.deb r-base-dev_1.8.1-0.cran.1_all.deb r-base-html_1.8.1-0.cran.1_all.deb r-base-latex_1.8.1-0.cran.1_all.deb r-base_1.8.1-0.cran.1_all.deb r-doc-html_1.8.1-0.cran.1_all.deb r-doc-info_1.8.1-0.cran.1_all.deb

Dear All, I would like to ask you to help me with my memeory. I remember using some function that would list all the possible methods I could apply to an object. Say, if I had an object of class=lme, it would tell me that that I could do stuff like qqnorm(myobjct), or VarCorr(myobject). In general, a very complete list. I though this list of all possible methods would pop out by typing

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Dear all, Here is the question: For example, using the "petrol" data offered with R. pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol) pet3.lme$sigma gives the residual StdDev. But I can't figure out how to extract the "(intercept) StdDev", although it is in the print out if I do "summary(pet3.lme)". In

Hi All. I need a bit of help extracting the residual error variance from the VarCorr structure from lmer. #Here's a 2-way random effects model lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat) #Get the structure vc.fit <- VarCorr(lmer.1) #results in..... $person 1 x 1 Matrix of class "dpoMatrix" (Intercept) (Intercept) 0.7755392 $rater 1 x 1 Matrix

Dear list, I am having some problems with extracting Variance Components from a random-effects model: I am running a simple random-effects model using lme: model<-lme(y~1,random=~1|groupA/groupB) which returns the output for the StdDev of the Random effects, and model AIC etc as expected. Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the

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 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]]

R 2.10 Windows Vista Is it possible to run a variance-components analysis using lme? I looked at Pinheiro and Bates' book and don't see code that will perform these analyses. If the analyses can not be done using lme, what package might I try? Thanks, John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology