search for: 18.78

Hi, I am encountering a problem in reading a file, the file looks like that: T 3 0 -- -- -- T -- -- -- 18.98 3 1 6.75 4.39 39 -- -- -- 18.58 3 2 6.90 4.90 43 -- -- -- 18.63 3 3 7.07 5.39 48 -- -- -- 18.78 G 4 0 7.41 5.54 47 G

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,

I would like to obtain a crosstabulation of means(var, quantiles...) i.e. I have a data frame with Var-i, Var-j, Var-k, Var-X, var-Y I like to have the mean of Var-X for each combination of Var-i,Var-j. One solution would be: by(var-i,Var-j,mean(Var-x)) but I would like it better formatted and with mean,S.Dev,n for each cell? Does anybody have some function to do this or some ideas how to go

Hi, im a newbie with very wobbly coding abilities. Tearing my hair out over getting the boxplot i want... I have a dataset called 'eagle' which consists of year (2011 or 2012), month (jan - dec), roof (TT6, TT13 or BARE) and temp (the continuous variable that i want to plot). So i want boxplots of the three roof treatments in every month organised in chronical order along x axis 2011 -

Hello, I'm a bit new to the world of R so forgive my ignorance. I'm trying to do a zero-inflated negative binomial regression and have received an error message and i'm not sure what it means. I'm running R 2.5.1 on XP. I have just tried a really simple version of the model to see if it would run before I put all the variables in. I have attached all the variables to the

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 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 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

This nightly tester is now using an llvm-g++ that produces the new ODR linkage types. This means that many more functions are being considered by the inter-procedural optimization passes (for example, "linkonce" functions defined in a header). The result seems to be pretty huge swings (both good and bad) in the C++ tests in the testsuite, see below. Note that this tester is often

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Hello R users, For the last few days I am struggling with the following task: my data.frame: A1 A2 A3 B1 B2 B3 58.81 53.292 54.501 13.013 17.39 19.407 56.02 56.251 54.033 20.099 13.15 10.411 55.376 53.099 57.625 13.396 21.031 13.22 58.584 53.194 54.218 13.038 16.854 19.289 55.7 55.921 53.847 19.942 13.153 9.828 55.093 52.934