similar to: really strange problem - has to be logic but i can't find it

I have many text files in the format below and in certain rare instances such as below there can be nothing in one of the fields so a double comma is written but I won't know this because I am reading in many,many files sequentially. # TEXT FILE 2004-02-10 00:01:31.00000,,105.60000000 2004-02-10 00:01:32.00001,,105.60000000 2004-02-10 00:01:45.00000,,105.60000000 2004-02-10

I have the structure, fxdata, below and I want to only keep data points of it in which the "bid" and the "ask" column names are both greater than zero. It's very hard to reproduce my problem because it seems like the behavior ( whether it gets rid of all the the zeros or not ) depends on the size of fxdata ? I had the following line for doing the above :

The pretty picture that I saw at: http://chartsgraphs.wordpress.com/2009/02/09/r-panel-chart-beats-excel-chart/#more-1096 inspired me to try something similar. The code that I wrote is: ------snipsnip--------------------------------------------------------------------- M <- structure(list(date = structure(c(13634, 13665, 13695, 13726, 13757, 13787, 13818, 13848, 13879, 13910, 13939, 13970,

Dear all, I have a problem while working with hourly data of fx rates. I've read from a csv file, the following way: csv-file like: Date,Open,High,Low,Close,Volume 2011-08-11 03:00:00,1.41758,1.42205,1.41625,1.42174,8974 ... 2011-08-12 04:00:00,1.42175,1.42413,1.42067,1.42172,7229 ... 2011-12-30 05:00:00,1.42173,1.42341,1.42062,1.42171,6703 ... raw<-

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hi R Gurus! I conducted regression and anova followings : Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.07e-01 5.95e-02 10.19 < 2e-16 *** nemp 2.87e-06 1.04e-07 27.63 < 2e-16 *** as.factor(corridor1)A -8.81e-02 2.13e-02 -4.14 3.6e-05 *** as.factor(corridor1)B

All, I would like to apply a set of rules to each row of the sample data set below. The rule sets are the guidelines for determining an individual's date for retirement eligibility. The rules are found in this document, http://www.opm.gov/feddata/RetirementPaperFinal_v4.pdf. I am only interested in the top two categories for retirement eligibility, the CSRS and FERS plans. The data set has

I have two assignment problems... I have written this small code for regression with two regressors . n <- 50 x1 <- runif(n,1,10) x2 <- x1 + rnorm(n,0,0.5) plot(x1,x2) # x1 and x2 strongly correlated cor(x1,x2) y <- 3 + 0.5*x1 + 1.1*x2 + rnorm(n,0,2) intact.lm <- lm(y ~ x1 + x2) summary(intact.lm) anova(intact.lm) the questions are 1.The function summary() is convenient since

I have two assignment problems... I have written this small code for regression with two regressors . n <- 50 x1 <- runif(n,1,10) x2 <- x1 + rnorm(n,0,0.5) plot(x1,x2) # x1 and x2 strongly correlated cor(x1,x2) y <- 3 + 0.5*x1 + 1.1*x2 + rnorm(n,0,2) intact.lm <- lm(y ~ x1 + x2) summary(intact.lm) anova(intact.lm) the questions are 1.The function summary() is convenient since

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

To anyone who can help: I have two brief questions concerning sapply. Following below is the code for my example. The two problems are described at the end of the code: site <- rep(2:6, each = 12) tillage <- rep(c(1,-1), each = 6, times = 5) carbon <- c(18.23, 16.06, 17.81, 16.07, 17.26, 17.08, 14.92, 15.88, 12.11, 14.23, 16.99, 13.57, 20.34, 20.3,

Hi, my data consists of 2 columns: one with the 'year' when daily observation was recorded, the other one consists these observations 'obs', like the following: year obs [3014,] 86 26.01 [3015,] 86 25.66 [3016,] 86 23.92 [3017,] 86 23.84 . . [3018,] 96 22.65 [3019,] 96 23.22 [3020,] 96 23.36 . . [3021,] 97 24.55 [3022,] 97 27.63 [3023,] 97 27.92 [3024,] 97

Dear all i use R only a few days and don't understand the difference between fivenum(x) und summary(x). > x [1] 20.77 22.56 22.71 22.99 26.39 27.08 27.32 27.33 27.57 27.81 28.69 29.36 [13] 30.25 31.89 32.88 33.23 33.28 33.40 33.52 33.83 33.95 34.82 > fivenum(x) [1] 20.770 27.080 29.025 33.280 34.820 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.77 27.14

Howdy I apologize for duplicated posting. But I decided to correct my previous posting. I had the regression results using r <- lm(Y ~ nemp + as.factor(devt), data=d). First, there is the result of anova(r). Here I could not find regression coefficients. Response: Y Df Sum Sq Mean Sq F value Pr(>F) nemp 1 58.2 58.2 1233.23 < 2e-16 ***

Duncan, With the commit from http://lists.cs.uiuc.edu/pipermail/llvm-commits/Week-of-Mon-20121203/158488.html, the Polyhedron 2005 benchmarks complete again on x86_64-apple-darwin12. The result are similar to what were seen with FSF gcc 4.6.2svn and llvm/dragonegg 3.0 (which was the last release that passed pb05) http://lists.cs.uiuc.edu/pipermail/llvmdev/2011-October/044091.html. Jack

Hi Jack, thanks for these numbers. > With the commit from http://lists.cs.uiuc.edu/pipermail/llvm-commits/Week-of-Mon-20121203/158488.html, > the Polyhedron 2005 benchmarks complete again on x86_64-apple-darwin12. The result are similar to what > were seen with FSF gcc 4.6.2svn and llvm/dragonegg 3.0 (which was the last release that passed pb05) >

1. My name is Bert, not Brent; 2. I am not your private consultant -- always cc the list unless you have good reason not to. I have done that here. It looks like this is what you want; if so, you really need to go through an R tutorial or two to learn the basics: d <- structure(list(region = structure(c(1L, 1L, 1L, 1L), .Label = "zilan", class = "factor"), Fe_ppmtp =