similar to: Defining functions - an interesting problem

Hi All, I have vector of length 52, say, x=sample(30,52,replace=T). I want to sort x and split into five *nearly equal groups*. Note that the observations are repeated in x so in case of a tie I want both the observations to fall in same group. This seems a very common task to do, but still I couldn't find an R function to do this. Any help would be highly appreciated. Regards Utkarsh

Hi All, I am currently using R version 2.8.1 on linux cent os 4.4 (i386) and want to upgrade to version 2.9.1. It seems to me that version-2.9.1 is it not for my OS. Am I right? Regards Utkarsh [[alternative HTML version deleted]]

Hi, With reference to the mail below, I have large datasets, coming from various different sources, which I can read into filebacked big.matrix using library bigmemory. I want to merge them all into one 'big.matrix' object. (Later, I want to run regression using library 'biglm'). I am unsuccessfully trying to do this from quite some time now. Can you please

Hi all, I am performing a stepwise regression by running the "step" function on an "lm" object. Now I want to save the intermediate iterations. I know the argument trace=T will print it on the console, but I rather want to assign it to some R object or may be output it in a CSV or text file. Any help will be appreciated. Regards Utkarsh

Hi All, I am currently doing the following to compute summary statistics of aggregated data: a = aggregate(warpbreaks$breaks, warpbreaks[,-1], mean) b = aggregate(warpbreaks$breaks, warpbreaks[,-1], sum) c = aggregate(warpbreaks$breaks, warpbreaks[,-1], length) ans = cbind(a, b[,3], c[,3]) This seems unnecessarily complex to me so I tried > aggregate(warpbreaks$breaks, warpbreaks[,-1],

I am using the library(bigmemory) to handle large datasets, say 1 GB, and facing following problems. Any hints from anybody can be helpful. _Problem-1: _ I am using "read.big.matrix" function to create a filebacked big matrix of my data and get the following warning: > x = read.big.matrix("/home/utkarsh.s/data.csv",header=T,type="double",shared=T,backingfile

Hi all, I want to define a function such that one of its argument if passed do one thing and if not passed do the second thing. So basically, I have to check whether the argument is passed or not inside the function. I am trying to use 'exists' function to do this. > f = function(a,b){exists("b")} > f(a=3, b=4) [1] TRUE This is working as expected.

Hi all, I want to define a function such that one of its argument if passed do one thing and if not passed do the second thing. So basically, I have to check whether the argument is passed or not inside the function. I am trying to use 'exists' function to do this. > f = function(a,b){exists("b")} > f(a=3, b=4) [1] TRUE This is working as expected.

I never understood that why is the value returned by as.date function in the library(survival) never matches with the description given in the help file: Following is the extract from ?as.date Description: Converts any of the following character forms to a Julian date: 8/31/56, 8-31-1956, 31 8 56, 083156, 31Aug56, or August 31 1956. Usage: as.date(x, order = "mdy", ...)

Hi All, I have recently *re*-installed R-2.9.1 in my Linux machine. Since then, I am unable to plot using the usual interactive device. > plot(1:10) This plots in a pdf file "Rplots.pdf" in my working directory. > sessionInfo() R version 2.9.1 (2009-06-26) i686-pc-linux-gnu locale:

Hi All, There are several replies to the question below, but I think there must exist a better way of doing so. I just want to check whether all the elements of a vector are same. My vector has one million elements and it is highly likely that there are distinct elements in the first few itself. For example: > x = c(1,2,rep(1,100000)) I want the answer as FALSE, which is clear from the

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Hi r-users, I want to use the sn package but I got the following message: > install.packages(repos=NULL,pkgs="c:\\Tinn-R\\sn_0.4-12.zip") Warning: package 'sn' is in use and will not be installed updating HTML package descriptions I did tried to do it a few times but it gives the same message.  ________________________________ From:

Hi R, I am getting this error while trying to use 'lrm' function with nine independent variables: > res = lrm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810 1+WC08231,data=y) singular information matrix in lrm.fit (rank= 8 ). Offending variable(s): WC08101 WC08221 Error in j:(j + params[i] - 1) : NA/NaN argument Now, if I take choose only four

Dear R-list, I would like to use the lattice library to show several groups on the same graph. Here's my example : ## the data f1 <- factor(c("mod1","mod2","mod3"),levels=c("mod1","mod2","mod3")) f1 <- rep(f1,3) f2 <-

Dear R users, I am trying to fit an lmer model with only random effects which is giving me the following error: Error : length(f1) == length(f2) is not TRUE In addition: Warning messages: 1: In P1L55:family : numerical expression has 390 elements: only the first used 2: In P1L55:family : numerical expression has 390 elements: only the first used I am trying to extract variance components

I would like to fit a model of the type Y = Xf + e where f are cover fractions (or percent cover). Therefore, I must constrain the fit to 1. sum(f) = 1 2. 0<= f <=1 How can I introduce these 2 constraints in a ML fit? Currently, I'm using nlregb and I'm using the "upper and low" keywords to set the bounds and use the following to ensure that sum(f) = 1: let's say

#I have a data set that looks like this. A bit more complicated actually with # three factor levels but these calculations need to be done on one factor at a #I then have a set of different rates that are applied #to it. #dataset cata <- c( 1,1,6,1,1,2) catb <- c( 1,2,3,4,5,6) doga <- c(3,5,3,6,4, 0) data1 <- data.frame(cata, catb, doga) rm(cata,catb,doga) data1 # start rates #

Hi R-devel. Is the following behavior in g1() and h1() expected? It seems to make "..." arguments work slightly differently from named arguments. #missing() has the property that it looks "up the chain" #for example, "z" can be missing in f3 even if #that argument did have a name ("y") in f2 f1 <- function(x, ...) { cat("In f1, missing(x) is

Hi, I wonder how to pass several functions and their arguments as arguments to a function. For example, the main function is f = function(X ) { process1(X) ... process2(X) } I have a few functions that operate on X, e.g. g1(X, par1), g2(X, par2), g3(X, par3). par1, par2 and par3 are parameters and of different types. I would like to pass g1, g2, g3 and their arguments to f and g1,