similar to: speeding up functions for large datasets

We are using roaming profiles. I have a user that logs onto two computers that are in different buildings. Computer 1 is collections w/ default printer HP Laserjet 4000DTN (10.8.9.223) and Computer 2 is salesshop w/ default printer HP Laserjet 4100DTN (10.8.3.31). The user complains that when she logs into salesshop, does her work, logs out, and then logs in at the collections computer,

Any ideas what is the problem with this code? > N <- 2; c(Sys.Date(), sprintf('N = %d', N)) [1] "2012-01-10" NA Warning message: In as.POSIXlt.Date(x) : NAs introduced by coercion Best regards, Ryszard Ryszard Czerminski AstraZeneca Pharmaceuticals LP 35 Gatehouse Drive Waltham, MA 02451 USA 781-839-4304 ryszard.czerminski@astrazeneca.com

Currently as the first column in a data frame I have string values in the format xx_yy - I want to create a new column with just the substring xx (for each row in turn). Three possible ways to do this might be (1) split the string by '_' using strsplit and paste the first of the resulting variables into a new column, but I have been unable to do this for each row of my data frame in turn

Is there any rational explanation for the bizarre seq() behavior below? > seq(2,8.1, lenght.out=3) [1] 2 3 4 5 6 7 8 > help(seq) > seq(2,8,length.out=3) [1] 2 5 8 > seq(2,8.1,length.out=3) [1] 2.00 5.05 8.10 Except maybe that it is early in the morning :) Best regards, Ryszard Ryszard Czerminski AstraZeneca Pharmaceuticals LP 35 Gatehouse Drive Waltham, MA 02451 USA 781-839-4304

Any ideas what is wrong? > strsplit("a.b", ".") # generates empty strings with split="." [[1]] [1] "" "" "" > strsplit("a b", " ") # seems to work fine with split=" ", and other characters... [[1]] [1] "a" "b" > > R.Version() $platform [1]

I wonder if there is a package that will estimate a Zero Inflated Poisson Model (ZIP), and also if there is a package that will estimate what is called the Tobit model: that is a combination of censored and observed values in the same sample. Georgina Bermann Biostatistics AstraZeneca R&D M?lndal -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

I am relatively new to R and am trying to do some basic data manipulation. Basically I have a table (csv - table 1) of data for a set of samples (rows), and a second table (table 2) of information about a subset of samples of particular interest. I want to pull out the data from table 1 for the samples in table 2, either by: * Merging the two tables based on a common identifier (SampleID - may

Is there an easy way to control smoothness of the contour lines? In the plot I am working on due to the undersampling the contour lines I am getting are jugged, but it is clear "by eye" these should be basically straight lines. In maps package I found smooth.map function, but maybe there is a more generic way of accomplishing the same thing. Ideally there would be an option to control

I have been lurking in this list a while and searching in the archives to find out how one learns to write fast R code. One solution seems to be to write part of the code not in R but in C. However after finding a benchmark article (http://www.sciviews.org/other/benchmark.htm) I have been more interested in making the R code itself more efficient. I would like to find more info about this. I have

RF trains fine with X, but fails on prediction > library(randomForest) > chirps <- c(20,16.0,19.8,18.4,17.1,15.5,14.7,17.1,15.4,16.2,15,17.2,16,17,14.1) > temp <- c(88.6,71.6,93.3,84.3,80.6,75.2,69.7,82,69.4,83.3,78.6,82.6,80.6,83.5,76 .3) > X <- cbind(1,chirps) > rf <- randomForest(X, temp) > yp <- predict(rf, X) Error in predict.randomForest(rf, X) : subscript

We are presenty using SAMBA 2.2 w. Windows 2000 sp1 and will be upgrading to Windows 2000 sp3. Are there any known or suspected problems with the combination of Windows 2000 sp3 and SAMBA 2.2. We are using Solaris 7 on the Unix side. /ola Ola Engstr?m Technical Computing & Information Services AstraZeneca R&D M?lndal S-431 83 M?lndal Sweden

I am currently summarising a data set by collapsing data based on common identifiers in a column. I am using the 'aggregate' function to summarise numeric columns, i.e. "aggregate(dat[,3], list(dat$gene), mean)". I also wish to summarise text columns e.g. by concatenating values in a comma separated list, but the aggregate function can only return scalar values and so something

"Swinton, Jonathan" <Jonathan.Swinton@astrazeneca.com> writes: > # works as expected > > ac <- c('A','B'); > > identical(ac,ac[1:2]) > [1] TRUE > > #but > > af <- factor(ac) > > identical(af,af[1:2]) > [1] FALSE > > Any opinions? Did a cross-check with Splus and it doesn't do that , so I think it

If I have a class Foo, then i can write an S3 method for sum for it: >setClass("Foo",representation(a="integer"));aFoo=new("Foo",a=c(1:3,NA)) >sum.Foo <- function(x,na.rm){print(x);print(na.rm);sum(x at a,na.rm=na.rm)} >sum(aFoo) But how do I write an S4 method for this? All my attempts to do so have foundered. For example

We have a particular file system that we're trying to keep in sync between two FreeBSD/ZFS servers using Rsync. The file system has many millions of files, and about 4TB of data total. Rsync takes HOURS to run, even when there are no files to transfer. Just the comparison itself takes hours. Is there any way to speed up the transfer? The command line I'm using is:

Perhaps this question has been asked before: but using the function predict( fit,type="terms",se.fit=T), where fit is a coxph object in S-plus, the estimated standard errors are different. It may be different estimators of the variance of the residuals? Which one is the default in R, I don't find that too easily in the documentation. Does anybody know? I'll be very grateful

I getting "Error in matrix(0, n, n) : too many elements specified" while building randomForest model, which looks like memory allocation error. Software versions are: randomForest 4.5-25, R version 2.7.1 Dataset is big (~90K rows, ~200 columns), but this is on a big machine ( ~120G RAM) and I call randomForest like this: randomForest(x,y) i.e. in supervised mode and not requesting

I'm trying to: Resample with replacement pairs of distinct rows from a 120 x 65,000 matrix H of 0's and 1's. For each resampled pair sum the resulting 2 x 65,000 matrix by column: 0 1 0 1 ... + 0 0 1 1 ... _______ = 0 1 1 2 ... For each column accumulate the number of 0's, 1's and 2's over the resamples to obtain a 3 x 65,000 matrix G. For those

Hi I really hope someone can help me. I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with

Hi, I posted this question on stats.stackexchange.com 3 days ago but the answer didn't really address my question concerning the speed in competing risk regression. I hope you don't mind me asking it in this forum: I?m doing a registry based study with almost 200 000 observations and I want to perform a competing risk analysis. My problem is that the crr() in the cmprsk package is