similar to: Loading Rdata files in a Package

Dear All, Thanks to an answer which I received from a previous post, I'm now able to create a series of environments using the following: nmes <- c("en1", "en2", "en3") for(i in nmes) assign(i, new.env(parent = .GlobalEnv)) My next question is how, using "load", can I automatically place data into each of these newly created environments. The

For reasons that are best known to myself [ ;-) ] I have a value of .Random.seed saved (via dput()) in a file ``.Random.seed.save''. In my .Rprofile I have the lines: .Random.seed <- dget(".Random.seed.save") Junk <- dget(".Random.seed.save") print(all.equal(.Random.seed,dget(".Random.seed.save")))

i use the GUI version of R on OS X. I launch it by double-clicking on the R icon. The process always starts in my home directory which means that the only .RData file that is ever read in is the one in my home directory. I would instead like to have different R sessions saved in different directories, but I can't figure out how to do this. A workaround is to use the shell version of R and

Hi R helpers, I'm fitting large number of single factor logistic regression models as a way to immediatly discard factor which are insignificant. Everything works fine expect that for some factors I get error message "Singular information matrix in lrm.fit" which breaks whole execution loop... how to make LRM not to throw this error and simply skip factors with singularity

Hi all, I was wondering whether it is possible to use the lapply() function to alter the value of the input, something in the spirit of : a1<-runif(100) a2<-function(i){ a1[i]<-a1[i-1]*a1[i];a1[i] } a3<-lapply(2:100,a2) Something akin to a for() loop, but using the lapply() infrastructure. I haven't been able to get rapply() to do this. The reason is that the "real"

Hello List, i have been agonizing over this for days, any reply would be greatly appreciated! Situation:___________________________________ My original dataset is a .csv dataset (w/ 2M records) with 4 variables: job_id (Primary key, won't be used for analysis, just used for join tables), sector_id (categorical variable, for 19 industry sectors), sqft (con't variable for square

Hi, I'm Roslina, PhD student of University of South Australia, Australia from school Maths and Stats. I use S-Plus before and now has started using R-package. I used to analyse rainfall data using julian date. Is there any similar function that you can suggest to me to be used in R-package? Thank you so much for your attention and help [[alternative HTML version deleted]]

Hi there, I run two regressions: y = a1 + b1 * x + e1 y = a2 + b2 * z + e2 I want to test against the null hypothesis: b1 = b2. How do I design the test? I think I can add two equations together and divide both sides by 2: y = 0.5*(a1+a2) + 0.5*b1 * x + 0.5*b2 * z + e3, where e3 = 0.5*(e1 + e2). or just y = a3 + 0.5*b1 * x + 0.5*b2 * z + e3 If I run this new regression, I can test against

I have a problem with expressions. I am trying to create a title where the parameter of interest is displayed as a Greek character. Which parameter is being considered is stored in a character variable. As an example, if I have param <- "alpha" and then do plot(0, 0, main = bquote(Parameter==.(param))) then in the title I get "Parameter = alpha", whereas I want the

Hi, When qqnorm on a vector of length 10M+ I get a huge pdf file which cannot be loaded by acroread or evince. Any suggestions? (apart from sampling the data). Thanks. -- Sam Steingold (http://sds.podval.org/) on Ubuntu 11.10 (oneiric) X 11.0.11004000 http://mideasttruth.com http://honestreporting.com http://camera.org http://openvotingconsortium.org http://pmw.org.il

I have a matrix of frequency counts from 0-160. x<-as.matrix(c(0,1,0,0,1,0,0,0,1,0,0,0,0,1)) I would like to apply a function creating a new column (x[,2])containing values equal to: a) log(x[m,1]) if x[m,1] > 0; and b) for all x[m,1]= 0, log(next x[m,1] > 0 / count of preceding zero values +1) for example, x[1,2] should equal log(x[2,1]/2) = log(1/2) = -0.6931472 whereas x[3,2] should

Hi, How can I check if a certain ... argument has been passed on to my user-defined function or not? foo <- function(data, ...) { ### here I want to check whether xlab was passed with the ... arguments ### or if the ... arguments did not contain an xlab argument } I tried missing(xlab) , exists(xlab) and several other things but did not find a solution. TIA, Mark

Hi, I have a question regarding the image function. Is there a way to specify a separate color set for each column in the matrix? TIA, ajar. [[alternative HTML version deleted]]

I was playing around a bit to see how I could find the two points in a set of points (or ordered pairs) furthest from each other. Here's what I did: 1) created a Nrow by 2col matrix, so each row contains an x,y coordinate pair. 2) fed the matrix to a nested mapply (cv is my matrix): mapply(function(k,l) mapply(function(x,y,a,b) +

Hi, I am having difficulty entering a 'programmable' argument into the multinom function from the nnet package. Interactively, I can get the function to work fine by calling it this way: z1=multinom(formula = class.ind(grp[-outgroup])~ (PC1 + PC2 + PC3), data=data.frame(scores)) However I need to be able to change the number of variables I am looking for in 'scores' and so am

I am doing cluster analysis [hclust(Dist, method="average")] on data that potentially contains redundant objects. As expected, the inclusion of redundant objects affects the clustering result, i.e., the data a1, = a2, = a3, b, c, d, e1, = e2 is likely to cluster differently from the same data without the redundancy, i.e., a1, b, c, d, e1. This is apparent when the outcome is visualized

Dear all, I'm trying to process HUGE datasets with R. It's very fast, but I would like to optimize it a bit more, by focusing one one column at time..... say file is 1GB big and has 100 columns..... In order to prevent "out of memory" problems.... I need to load one column at the time.... the only problem is that read.table doesn't support this feature.... Is there some

Hello R users, I have gene expression data of two groups of genes (large and small). Gene expression intensities of those genes are classified into 1 to 10 levels. What I want is to make a random set of genes that have the same levels as the small group from large group using sample(). I used smallvec to hold the number of genes in each levels (1 to 10) for small group, largevec for large group.

Dear list; I just created a utility function that replicates what I have done in the past with Excel or OO.org by putting a formula of the form =sum($A1:A$1) in an upper-corner of a section and then doing a "fill" procedure by dragging the lower-rt corner down and to the right. When divided by the grand sum of the entries this function then calculates a 2D-discrete-ECDF. I

Hello I'm trying to run a fortran code which use LAPACK subroutines. I think I should use some points shown in the manual 5.5 Creating shared objects but it is too technical for me :-(... Could anyone help me for the procedure to do: -which part of the manual is relevant for this type of question? actually I'm speaking from writing R extensions, should I read R admin? -point 1.2