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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 all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Dear All, I am considering to buy a workstation. For the CPUs, I wonder whether anybody have the experience in choosing one for the R. Intel Xeon W3540 2.93 8MB/1066 QC CPU is much cheaper as compared with the Intel Xeon E5540 2.53 8MB/1066 QC CPU. However, its Hz 2.93 is bigger than 2.53. I wonder which one would run R quicker. Thank you. Huang [[alternative HTML version deleted]]

Dear all, I am a new user in r and I am facing some problems with the quantile regression specification. I have two matrix (mresultb and mresultx) with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10. Hence, the columns in my matrix represents each simulation of a determined variable. I need to regress each column of mresultb on mresultx. My codes are the following:

Hi, I have searched the lists but still can not solve the problem. I am using a windows machine. After I sweave some Rnw file, I got a tex file. However, the tex file can not be compiled. I know the problem is in the line \usepackage{C:/PROGRA~1/R/R-25~1.1/share/texmf/Sweave} and I need to modify this line to \usepackage{Sweave}. I hope there can be some automatic way to do this instead of

Dear all, I encounter some covariance matrix with quite small eigenvalues (around 1e-18), which are smaller than the machine precision. The dimension of my matrix is 17. Here I just fake some small matrix for illustration. a<-diag(c(rep(3,4),1e-18)) # a matrix with small eigenvalues b<-matrix(1:25,ncol=5) # define b to get an orthogonal matrix b<-b+t(b) bb<-eigen(b,symmetric=T)