similar to: How to free memory used by R.

Dear Experts, May be the problem is still solved, however I tried to find the answer in the archives: I use: > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 7.1 year 2003 month 06 day 16

Dear R-helpers, First of all I have nothing against the French language! But now my problem, yesterday I installed R 2.1.1 and I had to experience that my RGui is speaking French. My windows locals is French (Switzerland). I'm used to English and I want to reset my RGui to English. I was seeking for the solution in the archives, however not successfully. By the way the searchable archives

Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662

Hi, I'm using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 5.1 year 2002 month 06 day 17 language R and I would like to apply: > update.packages() trying URL

Summary: proc mixed vs. lme The objective of this summary is to help people to get more familiar with the specification of random effects with proc mixed or lme. Very useful are the examples of Ramon Littell's book: "SAS System for Mixed Models (1996)" (http://ftp.sas.com/samples/A55235) The same data set's are kindly made available by Douglas Bates in the

Hello, I'm using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R and I get the following error with: library(Design) df <- list(pre=c(0,, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,

Dear all, I want to get a series of boxplots (grouped by two factors) and I want to overlay the original observations and the following code does almost what I want: library(ggplot) ddf <- data.frame(x=factor(rep(LETTERS[1:4], each=30)), y = runif(120,0,10), grp = factor(rep(rep(1:3, 10), 4))) ggplot(ddf, aes(x, y, colour=grp)) + geom_boxplot() + geom_point() Yet the position of the points

Dear all, I want to use aggregate.formula to conveniently summarize a data.frame. I have quiet some variables in the data.frame and thus I don't want to write all these names by hand, but instead create them on the fly. This approach has the advantage that if there will be even more columns in the data.frame I don't have to change the code. I've hence tried to construct a formula

Dear all, I produced the following graph with ggplot which is almost fine, yet I don't like that the legend for "Means" and "Observations" includes a line, though no line is used in the plot for those two (the line for "Overall Mean" on the other hand is wanted): library(ggplot2) ddf <- data.frame(x = factor(rep(LETTERS[1:2], 5)), y = rnorm(10)) p <-

Dear all, Assume that I have the following data structure: d <- expand.grid(subj=1:5, time=1:3, treatment=LETTERS[1:3]) d$value <- 10 ^ (as.numeric(d$treatment) + 1) + 10 * d$subj + d$time d$value2 <- 100000 + d$value where d$treatment == "C" stands for my control group. What I want to achieve now is to subtract the values corresponding to d$treatment == "C" from

Dear all, I have a question about linear models. Is there something comparable to the estimate statement in SAS? E.g. proc mixed data=test; class x1 x2; model y=x1|x2 /s; estimate 'x1' x1 -1 1 x1*x2 -0.5 -0.5 0.5 0.5; estimate 'x2' x2 -1 1 x1*x2 -0.5 0.5 -0.5 0.5; run; Thanks for the help, Dominik Dominik Grathwohl Biostatistician Nestl? Research

Hi everybody, I want an x-axis which has xlim=c(max, min) rather than xlim=c(min, max) in order to reflect the type of the process (cooling): library(lattice) myprepanel <- function(x,y,...) list(xlim=rev(range(x))) x <- rep(1:10, 100) z <- factor(sample(10, 1000, T)) y <- rnorm(1000, x, as.numeric(z)) xyplot(y~x|z, scales=list(x="free"), prepanel=myprepanel) This works

Dear all, Today I figured out that there is a neat function called droplevels, which, well, drops unused levels in a data frame. I tried the function with some of my data sets and it turned out that not only the unused levels were dropped but also the contrasts I set via "C". I had a look into the code, and this behaviour arises from the fact that droplevels uses simply factor to drop

Hi everybody, Is there a particular reason, why this code does not work as intended: z <- factor(LETTERS[1:3], ordered = TRUE) u <- 4:6 min(z[u > 4]) Error in Summary.factor(2:3, na.rm = FALSE) : min not meaningful for factors I agree that min is indeed not meaningful for not ordered factors, but it makes sense for ordered factors. Especially since z[3] <

Dear all, In a function I paste a string and convert it to a formula which I pass to lm[e]. The idea is to write a function which takes the name of the response variable and the explanatory variable and the data frame as an argument and calculates an lm[e]. (see example below) This works fine, but if I want to make a prediction on this model, R complains that the object holding the formula

Hello, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi all, In R it is possible to sum tables: > (a <- table(rep(1:3, sample(10,3)))) 1 2 3 2 5 7 > a+a 1 2 3 4 10 14 Now suppose that I have a list of tables, where each table counts the same things > k <- list(a,a,a) How can I sum all tables in k? > do.call(sum, k) [1] 42 does not work since it sums over each table. > do.call(`+`, list(a,a)) 1 2 3 4 10

Dear r-helpers, I have a question about multiple testing. Here an example that puzzles me: All matrixes and contrast vectors are presented in treatment contrasts. 1. example: library(multcomp) n<-60; sigma<-20 # n = sample size per group # sigma standard deviation of the residuals cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE, dimnames =

Dear R-helpers: My question is how do I efficient and valid correct for multiple tests in a repeated measurement design: Suppose we measure at two distinct visits with repeated subjects a treatment difference on the same variable. The treatment differences are assessed with a mixed model and adjusted by two methods for multiple tests: # 1. Method: Adjustment with library(multcomp)