similar to: R-alpha: speed of sort(.) and order(.)

I have a data set for different time intervals. The data has three comment lines before data for each time interval. For each time interval there are 500 data points. I want to change the dataset such that I have the following format: t1 t2 t3 ................ 0.00208 0.00417 0.00625 ................. a1 a2 a3 ...................

Hi, First, thanks in advance. Some useful info: >version platform x86_64-unknown-linux-gnu arch x86_64 os linux-gnu system x86_64, linux-gnu version.string R version 2.15.1 (2012-06-22) I'm trying to use the table() function on a 2 column matrix that has 711 million rows (see below). However, it freezes. If I subset the matrix to be less than or equal

Dear all, my question might be a bit off-topic. Is there anything like a standard textbook on statistical programming? With that I don't mean anything like MASS, S Programming, Programming with Data, ... (no offense meant, they are fantastic books and each of those three helped me a great deal). Rather in the direction of the "Numerical Recipes" Series addressing how to implement

Hi, I am trying to read in a rather large list of transactions using the arules library. It seems in the coerce method into the dgCmatrix, it somewhere calls unique. Unique.c throws an error when n > 536870912; however, when 4*n was modified to 2*n in 2004, the overflow protection should have changed from 2^29 to 2^30, right? If so, how would I change it in my copy? Do I have to recompile

Motivated by Deepayan's recent inquiries about the efficiency of the R 'quantile' function: http://tolstoy.newcastle.edu.au/R/devel/05/11/3305.html http://tolstoy.newcastle.edu.au/R/devel/06/03/4358.html I decided to try to revive an old project to implement a version of the Floyd and Rivest (1975) algorithm for finding quantiles with O(n) comparisons. I used

Hello everybody, I am making a modified version of "Stephan Steinhaus' benchmark test for number crunching, v. 2, (see http://www.scinetificweb.com/ncrunch/ncrunch.pdf for the original version), comparing several functions of some math/stat software. R is not performing bad at all... except for the sorting of a 1,100,000 random vector (test #3) which is the worst of all (see cell F3 in

Hello everybody, I am making a modified version of "Stephan Steinhaus' benchmark test for number crunching, v. 2, (see http://www.scinetificweb.com/ncrunch/ncrunch.pdf for the original version), comparing several functions of some math/stat software. R is not performing bad at all... except for the sorting of a 1,100,000 random vector (test #3) which is the worst of all (see cell F3 in

Dear All: In the attached file, I have 3 group patients, and there are 5 in each group (the groups are decided by the prefix of the idno). I want draw a repeat measurement comparison figure. My goal is to list 5 patients from same group on one horizontal line. But xyplot sounds pick them randomly (or I was confused?). Could you please help me modify the following code to accomplish this?

I am trying to do some verification across a large dataset, cuData, that has 23 columns. Column 23 (similarity) is the outcome 0 or 1 and the other columns are the features. I do this: verificationglm.model <- glm(formula = similarity ~ ., family=binomial, data=cuData[1:1000,]) and produce the model: > summary(verificationglm.model) Call: glm(formula = similarity ~ ., family =

Hi all: I have a question about multi-comparison. The data is in the attachment. My purpose: Compare the predicted means of the 3 methods(a,b,c) pairwisely. I have 3 ideas: #idea1 result_aov<-aov(y~ method + x1 + x2) TukeyHSD(result_aov) diff lwr upr p adj b-a 0.845 0.5861098 1.1038902 0.0000001 c-a 0.790 0.5311098 1.0488902 0.0000002 c-b -0.055 -0.3138902

Ran a bunch of variables in R and the final result of StepAIC is as below: Why are the first 5 variables kept in the stepwise result?? Are the last 4 variables finally chosen after Stepwise? Thanks Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.315e-01 2.687e-01 0.490 0.63611 Core_CPI__ 1.290e-02 7.496e-03 1.721 0.11927 GDP_change -3.482e-03 2.075e-03 -1.678 0.12767

Dear R People: Suppose I have the following data frame: x1 x2 x3 1 -0.1582116 0.06635783 1.765448 2 -1.1407422 0.47235664 0.615931 3 0.8702362 2.32301341 2.653805 > str(xx) 'data.frame': 3 obs. of 3 variables: $ x1: num -0.158 -1.141 0.87 $ x2: num 0.0664 0.4724 2.323 $ x3: num 1.765 0.616 2.654 I can exclude the second column nicely via: >

Suharto, If you're interested in performance with subscripting, you might want to look at pqR (pqR-project.org). It has some substantial performance improvements for subscripting over R Core versions. This is especially true for the current development version of pqR (probably leading to a new release in about a month). You can look at a somewhat-stable snapshot of recent pqR development

Hello~   Did any one have used the package 'orcutt' ?   I find that it can not work smoothly in a single variable regression. I use the example following, it function very well.   But when I regress "cons" on "price" (use the "reg1<-lm(cons~price+income+temp)") , then  use "reg11<-cochrane.orcutt(reg1) ". There is an error message “Error in

As a sequel to my previous mail on cut, formatC does not produce what I have been taught is significant digits: > x <- c(1.0793,1.0796, 11.0954, 11.0736 ) > formatC(x,digits=3,format="g") [1] "1.08" "1.08" "11.1" "11.1" (3,3,3,3) significant digits OK > formatC(x,digits=3,format="f") [1] "1.079"

Hello, This is an urgent request. I want to convert a list of 3 elements into a matrix and I am not sure how to do it. The list looks like this: List of 3 $ : num [1:15364, 1] 0.133 0.622 0.588 1.024 0.583 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:15364] "6420681" "3610072" "2260458" "60689" ... .. ..$ : NULL $ :

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

Hello, Sorry a second time again, Maybe I have to add that I'm running R under Windows 2000/XP, and that the download works properly under 1.062 but not under 1.070. Diethelm >Date: Tue, 10 Jun 2003 19:25:33 +0200 >To: r-devel@stat.math.ethz.ch >From: Diethelm Wuertz <wuertz@itp.phys.ethz.ch> >Subject: dse package - load failure >Cc: pgilbert@bank-banque-canada.ca

Hello, I have a statistical problem that I am using R for, but I am not making sense of the results. I am trying to use multiple regression to explore which variables (weather conditions) have the greater effect on a local atmospheric variable. The data is taken from a database that has 20391 data points (Z1). A simplified version of the data I'm looking at is given below, but I have a