similar to: newline in lattice axis label

Hello, I have some code that currently works fine and I am endeavoring to convert the major pieces of it into functions. This involves taking "hard coded" names of variables that are used in various places and figuring out how to abstract them out into functions where the arguments (i.e. a list of variables)?can be passed to the parent function and used within that function for various

Hello,   I'd like to generate automatically all the possible combinations of a set of 8 variables (there are 535, too many to do it by hand). For example:   input: varA, varB, varC output: varA+varB+varC             varA+varB             varA+varC             varB+varC             varA             varB             varC Is there any function that produces this option?   Thank you [[alternative

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

Dear R-list, I have a data set (in the following example called "a") which have: one "subject indicator" variable (called "id") three dependent variables (varD, varE, var F) three independent variables (varA, varB, varC) I want to fit 9 lme models, one per posible combination (DA, DB, DC, EA, EB, EC, FA, FB, FC). In stead of writting the 9 lme models, I want to

Hi, I wonder whether the following is possible with R, and whether anyone has done that and can share his/her code with me. I have a correlation matrix, and I want to create a correlation table that I can copy to Microsoft Word with a superscript above each correlation, indicating significant differences in the same row. That is, when correlations in the same row do not share superscript, it means

Dear R Users, Am using lm() with contrasts as below. If I skip the contrasts() statement, I get the coefficient names to be > names(results$coef) [1] "(Intercept)" "VarAcat" "VarArat" "VarB" which are much more meaningful than ones based on integers. Can anyone tell me how to get R to keep the coefficient names based on the factor levels

I'm having trouble understanding how functions in the subset argument for lmList search for the objects they need. This trivial example produces "Error in rownames(fakedf) : Object "fakedf" not found": library(nlme) fitbyID <- function() { fakedf <- data.frame(ID = gl(5, 10, 50), A = sample(1:100, 50), B = rnorm(50))

Hello, I have a dataframe of say 20 lines with one line per individual. I want to group these 20 individuals by length class (eg. of 5cm) and get the mean value of all the other variables (eg VarA and VarB) for each length class My dataframe is as follow: Length <- 10:30 VarA <- seq(1000,1200,10) VarB <- seq(500,700,10) Data <- cbind(Length,VarA,VarB) And I want to get something

Hi: Can anyone advice me on how to loop and perform a calculation through all the columns. here's my data xd<- c(2.2024,2.4216,1.4672,1.4817,1.4957,1.4431,1.5676) pd<- c(0.017046,0.018504,0.012157,0.012253,0.012348,0.011997,0.012825) td<- c(160524,163565,143973,111956,89677,95269,81558) mydf<-data.frame(xd,pd,td) trans<-t(mydf) trans I have these values that I need to

Dear R users I'm having problems in putting special characters (like 佱, 併, 佺 ) in my plots, as much in titles, as in axis names, as in legend...when I save them as a pdf document. They don't appear... I don't know if it is because I'm using a linux platform... The script is the following: library(grDevices) jpeg(file="Fronteira/FronteiraNova.jpeg",

Dear Lister, I understand merge() can be used to join 2 data frames based on 1 variable. But how about merge based on more than 2 variables? Thank you so much! -- WenSui Liu (http://spaces.msn.com/statcompute/blog) Senior Decision Support Analyst Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center [[alternative HTML version deleted]]

Hi, I have a data set that looks like this: > data vara varb S PC 1 None 250 1 80 2 None 250 1 70 3 Some 250 1 60 4 Some 250 1 70 5 None 1000 1 90 6 None 1000 1 90 7 Some 1000 1 80 8 Some 1000 1 70 9 None 250 2 100 10 None 250 2 80 11 Some 250 2 70 12 Some 250 2 70 13 None 1000 2 100 14 None 1000 2 90 15 Some 1000 2 50 16 Some 1000 2 40 ... And so on.

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

Dear R users, I'm having a hard time with some very simple things. I have a time series where the dates are in the format 7-Oct-04. I imported the file with read.csv so the date column is a factor. The series is rather long and I want to plot it piece by piece. The function below works fine, except that the labels for date are meaningless (ie 9.47e+08 or 1098000000 - apparently the number of

Is there code available to decompose interactions involving at least one nominal factor with more than 2 levels as described, e.g., by Tukey or by Mandel (1971, Technometrics, 13: 1-18)? Tukey's model: E(y[i,j]) = mu0 + a[i] + b[j] + c*a[i]*b[j], estimating a, b, and c so sum(a) = sum(b)= 0. Mandel essentially describes a singular value decomposition of the interaction. Thanks,

Dear all, I think I'm nearly there in writing R code which will read in files with two variable parts to the file name and then assigning these file names to objects, which also have two variable parts. I have got the code running without encountering errors, however, I receive 50+ of the same warnings: 1: In assign(paste("Fekete_", index$year, index$month, sep = ""),

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following

Dear list members I am still a newbie so might be asking a stupid question, but I can't get ggplot to work in a loop (or a "while" statement for that matter). # to take a minimal example - mydata$varc = c(1,2,3) for (i in 1:1){ jpeg("test3.jpg") plot(mydata$varc) #ggplot(mydata, aes(x=mydata$varc)) + geom_bar() dev.off() } this produces

Hi, I am trying to get figures from multiple source files in Sweave. My test file is as follows \documentclass{article} \usepackage{Sweave} \begin{document} \begin{section}{notitle} This is a simple Sweave test <<gethypergraphs_rhea,fig=TRUE,echo=F>>= source("./testfig.r") @ \\ End of the simple sweave test \end{section} \end{document} where testfig.r is as simple as

Greetings - I am working on a piece of code to simulate vehicle times in and out in each of a number of parking spaces. At this stage, my code basically does what it is supposed to do but for the sequential number of each new parking event for a given space (i.e., the index of the loop variable). Instead of passing the index of the loop variable (iter) to the data frame, it passes the value