similar to: Processing Sweave documents without Sweave

I met today a computer crash and our maintenance officer had to reinstall some components of the OS (MS Windows XP Pro) as well as the Internet browser (among other things). Now, I cannot install packages from a distant repository: > utils:::menuInstallPkgs() Error in .readRDS(pfile) : unknown input format > traceback() 5: .readRDS(pfile) 4: .packages(all.available = TRUE) 3:

How to add mean,sd, number of observation in each panel for lattice histogram? Aimin

Hi, Does anyone know how to make the color of the lines all black when plotting groupedData with an outer factor: For example, library(nlme) plot(Dialyzer, outer=~QB, key=F) This generated colored curves in R.2.4.1. How to make all the curves black ? (or how to alter the color (type) of lines for the nlme groupedData plotting function in general?) Thanks Qiong

Hi, I have not been able to calculate residuals in the lme4 package. I've been trying the resid() function after I ran a GLMM with the lmer() function, but I get an error message that says "residuals are not inserted yet". I looked it up in the "help" history and I realized that several people have had this problem in the past, related to some bug in this function and

windows XP R 2.8.1 I would like to learn to use Sweave with R. I have no experience using Latex. I have looked at the sweave manual (http://www.statistik.lmu.de/~leisch/Sweave/Sweave-manual.pdf) and have found it difficult to understand, probably because I don't know Latex. Can someone recommend a document that will get me started with Sweave? Thanks John Confidentiality Statement: This

Dear all, I've just downloaded R.12.0 from CRAN (http://cran.r-project.org/bin/windows/base/release.htm). I am unable to find RGUI.exe as well as Rterm.exe. Conversely to what is explained in readme.R.2.12.0: There are two versions of the R executable in R-2.12.0\bin\i386 (32-bit) or R-2.12.0\bin\x64 (64-bit). I can't find directories i386 or x64 in R-2.12.0\bin -- Renaud Lancelot EDEN

Dear all, I have installed the latest version of R 2.12.0 available on CRAN (http://cran.r-project.org). When I try to load the recommended package lattice: > library(lattice) Error: package 'lattice' is not installed for 'arch=i386' I am running Rgui using C:\R\R-2.12.0\bin\i386\Rgui.exe --vanilla > sessionInfo() R version 2.12.0 (2010-10-15) Platform:

Is it possible to read jpeg files into R? If yes please guide, Thanks.. I tried to search many time but failed to do. Thankis in advance.. with Best Regards, Malik Shahzad Visiting Researcher National Institute of Informatics (NII) Tokyo, Japan Doctoral Student Asian Institute of Technology (AIT) Bangkok, Thailand +66-8-7676-5616 [[alternative HTML version deleted]]

Dear all, I am trying to avoid the warnings produced by: > x <- -2:2 > log(x) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production de NaN in: log(x) I thought that using ifelse would be a solution, but it is not the case: > ifelse(test = x < 0, yes = NaN, no = log(x)) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

hello I make a subroutine that give-me a (mathematical) function in string format. I would like transform this string into function ( R function ). thanks for any tips. cleber #e.g. fun_String = "-100*x1 + 0*x2 + 100*x3" fun <- function(x1,x2,x3){ return( ############ evaluation( fun_String ) ############ ) True String mathematical function :-( :-( > nomes [1]

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

I wonder why the following code does not produce filled points in the key, as I would have expected: > library(lattice) > x <- 1:10 > y <- rnorm(10) > xyplot(y ~ x, pch = 21, col = "black", fill = "grey", + key = list(space = "top", + text = list("data"), + points = list(pch = 21, col =

Hi list, is there any direct way to obtain confidence intervals for the regression slope from lm, predict.lm or the like? (If not, is there any reason? This is also missing in some other statistics softwares, and I thought this would be quite a standard application.) I know that it's easy to implement but it's for explanation to people who faint if they have to do their own programming...

Dear Listers, If I have several glm objects with names glm1, glm2.... and want to apply new data to these objects. Instead of typing "predict(glm1, newdata)..." 100 times, is there way I could do so in a loop? Thank you so much! wensui [[alternative HTML version deleted]]

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

Is there any pure r code to do delaunay or voronoi diagrams? Thanks! --------------------------------- [[alternative HTML version deleted]]

Hi, I am trying to extract the coefficients matrix from a gls summary. Contrary to the lm function, the command fit$coefficients returns only the estimates of the model, not the whole matrix including the std errors, the t and the p values. example: ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <-

Hi: The gls function I used in my code is the following fm<-gls(y~x,correlation=corARMA(p=2) ) My question is how to extact the AR(2) parameters from "fm". The object "fm" is the following. How can I extract the correlation parameters Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm". Thanks a

Dear R users, I've been struggling with the following problem: I want to extract the Wald p-value from an lmer() fit, i.e., consider library(lme4) n <- 120 x1 <- runif(n, -4, 4) x2 <- sample(0:1, n, TRUE) z <- rnorm(n) id <- 1:n N <- sample(20:200, n, TRUE) y <- rbinom(n, N, plogis(0.1 + 0.2 * x1 - 0.5 * x2 + 1.5 * z)) m1 <- lmer(cbind(y, N - y) ~ x1 + x2 + (1 | id),