similar to: fast code

Dear R users: is there a package, similar to varclus in SAS or varclus in S, ported or written for R? Also, is there any other package in R that was designed for grouping the variables under different measures of distance (in cases where data is non-Gaussian, autocorrelated, and so on). Janusz. -- ** Janusz Kawczak ** ** UNC at Charlotte,

I've compiled the 1.8.0 R on Solaris 2.9 and when trying to use plot command I get the Bus error and a core dump. Anybody experienced something like this? If yes, what should I patch? Janusz. [[alternative HTML version deleted]]

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

Dear List I am having a bit of a problem getting a program to work. For each of i=1 to n persons I have a matrix (different for each person) with m rows. What I want to do, is create m new data sets such that the first is made up of the first row for each person from the original matrices, the second contains the second row for each person from the original matrices etc etc up to the mth

Hello, I've had a quick look on the list and can't find an answer to this niggle. Whilst debugging some code, I noticed that looping variables in R are not local to the loop as seams common in procedural languages. For example consider the following piece of code: for(i in 1:3){ cat(i,"") for(i in c("a","b","c")){ cat(i,"")

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

hello everybody I use the following code for my programming & it runs with the error as specified below.Any help that would disolve the error will be highly appreciated. Thanks in advance my code looks like this #### R programme for simulating the power of the two sample t test vs various #### non-parametric alternatives sim.size <- 200 sample.size <- 10 set.seed(231) mu1 <- 0 delta

Hi, I want to perform goodness of fit test for multinominal distribution. The easiest way would be to use Chi2, but the measurment errors are not normally distributed. I thought about using some bootstraping method to perform the analysis. How can I do it in R? Tomek -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Is there anyone who succeeded in installing R-0.61 on a IBM RISC/6000 under AIX? Juergen. ======================================================================= Juergen Dippon Mathematisches Institut A, Universitaet Stuttgart Pfaffenwaldring 57, 70569 Stuttgart

Has anybody ported the Classification and Regression Trees package to R? Stefano =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

Dear List, I am looking for a possibility to fit a mixture model under R using maximum likelihood estimation. Venables and Ripley describe a solution working under S+ (in MASS, 3. ed., p. 263) which requires the D system function and deriv3. This solution does not seem to be portable to R or at least I do not realise how. Is there anyone who a) knows how one could make the MASS-method run under

Hi, I am running samba and lprng for 4 hp laserjets. Sometimes a samba print job goes to the wrong printer. Normally everything works just fine. Shown below is the lprng acct log file enries for the concerned print job. Please notice that the print job was supposed to go to hp4050-mail, but ended up in hp8100. Any suggestions are much appreciated. Thanks Balagopal jobstart

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used

Hello -- I am curious if lmer can be used to fit a multilevel factor model such as a two-parameter item response model. The one parameter model is straightforward. A two-factor model requires a set of factor loadings multiplying a single random effect. For example, a logit model for the ith subject responding correctly to the jth item (j=1,..,J) is logit[p(ij)] = a1*item1(i) + ... + aJ *

Suppose I have a mixed-effects model where yij is the jth sample for the ith subject: yij= beta0 + beta1(age) + beta2(age^2) + beta3(age^3) + beta4(IQ) + beta5(IQ^2) + beta6(age*IQ) + beta7(age^2*IQ) + beta8(age^3 *IQ) +random intercepti + eij In R how can I get an F test against the null hypothesis of beta6=beta7=beta8=0? In SAS I can run something like contrast age*IQ 1,

I have put a preliminary version of factanal in a package at http://www.stat.ox.ac.uk/pub/R/fa_0.1-1.tar.gz and would appreciate feedback. In particular 1) What else you might want. Please give precise references with algorithmic details, as that has been a major problem for me. 2) Some convincing examples of factor analysis. Most (literally) of the ones I have looked at are local maxima, not

Dear R helpers,   I have following input files. (Actually they are more than 10 rates but here i am considering only 2 rates to write my problem)   rate1.csv min1        max1            min2          max2          min3           max3 1.05        1.30               1.30          1.65             1.65          1.99   rate2.csv min1        max1            min2          max2          min3          

Hi, Does anyone know how to compute the confidence prediction intervals for a nonlinear least squares models (nls)? I was trying to use the function 'predict' as I usually do for other models fitting (glm, lm, gams...), but it seems that se.fit, and interval computation is not implemented for the nls... Cheers Enrique ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Fisheries Research Services, Marine

Hi all, I compiled and installed Xen 3.2 source on FC8. Compilation and installation completed with no errors. However when I try to load Xen I get an error: Kernel panic - Attempted to kill init !!! This is my grub configuration: # grub.conf generated by anaconda # # Note that you do not have to rerun grub after making changes to this file # NOTICE: You have a /boot partition. This means

I encounters some codes in ggplot2 manual and confused with one of its lm syntax. The code is here: library(ggplot2) d <- subset(diamonds, carat < 2.5 & rbinom(nrow(diamonds), 1, 0.2) == 1) d$lcarat <- log10(d$carat) d$lprice <- log10(d$price) detrend <- lm(lprice ~ lcarat, data = d) d$lprice2 <- resid(detrend) mod <- lm(lprice2 ~ lcarat * color, data = d) # *** what