similar to: many datasets run with one R script in a computer cluster

Dear R users I have a list, as follows: > intvl.period.myrs $Devonian [1] 4.8 4.2 9.5 5.7 $Ordovician [1] 7.2 5.1 10.2 1.9 $Silurian [1] 4.7 3.0 7.8 2.0 3.3 1.6 2.6 2.7 I want to write a loop that will sum up the values in each part, and give me a vector containing the (in this case 3) summed values this is what I have so far: for (i in 1:length(names(intvl.periods.myrs)) {

Hello everyone I have problem with axis labels on graphs, I have my code as below: plot(0,0,xlim=c(1,ncol(PA)),ylim=c(1,nrow(PA)),main="Stratigraphic Range",xlab="Time Bins",ylab="Taxa",cex.axis=1.5,cex.lab=2,cex.main=2.5,mgp=c(5,1.5,0),xaxt="n") text(1:(length(strat_name)), y= 0, adj=1, srt=45,labels=strat_name,xpd=TRUE, cex=1) #adds text to x

Hi, I have 35 data files for reading. I would like get a program for performing reading of 35 files at once. All are of the type: Dados1.raw, Dados2.raw and so on. If the files have the same number of columns, I can read with the following commands: rm(list=ls()) filenames = list.files(path="~/Silvano/Arq", pattern="Dados+.*raw") names = substr(filenames, 1, 7) for(i in

Dear all, I need to do the same procedure on several files. But I don't know how to refer to the file name. Here is an example of what I am trying to do. List of files: file1(A,B,C, D1...Dn), file2(A,B,C,E1,...,En), file3(A,B,C,F1,...,Fn) Procedure I want to apply on each file: dft <- melt(df,id=c('A','B','C')) dft$X <- substr(dft$variable,1,3) dft$Y <-

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

The help pages of R-1.2.2 include several pages on various time series functions, but when I try to use these functions they appear not to be available .... am I missing something obvious, or are these functions not yet built? Chris Rogers ----------------------------------------------------------------------- L C G Rogers, Professor of Probability tel:+44 1225 826224 Department of

I've got a data frame which I've split by a factor, creating a list of dataframes which I have then done various operations on individually. I next want to recombine the resulting dataframes (still held in a list, still with the same number of columns with the same names) and there does not appear to be a `good' way to do this - at the moment, I'm using a for-loop with the rbind

Hi I am trying to estimate bottom temperatures over a particular depth range, based on one dataset containing synthetic temperature profiles (for set depths) and another that contains information on bathymetry. I need to do this for multiple regions and thus would ideally like an automated solution that is quicker and more accurate than the manual option I currently have! I have not had much luck

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

Hi, According to the help file for model.frame/get_all_vars, the following should produce the same output from both functions, but it doesn't... > dat <- list(X=matrix(1:15,5,3),z=26:30) > model.frame(~z+X,dat) z X.1 X.2 X.3 1 26 1 6 11 2 27 2 7 12 3 28 3 8 13 4 29 4 9 14 5 30 5 10 15 > get_all_vars(~z+X,dat) [1] z X <NA> <NA> <0

Hello All, The link to the binary & installation instructions (tar.gz binary not an ipk I'm afraid) is as follows: http://students.bath.ac.uk/enpsgp/Zaurus/#R It eventually dawned on me that the WORDS_BIGENDIAN define (or lack thereof) was causing the problems (after testing ieee NaN compliance that is). When cross-compiling it's probably fair enough that the configure script

Hi, I've got a problem with rtp keepalives. I'm using basically the same config on 2 hosts, but one of them sends rtp comfort noise when it's on hold, the other doesn't. The only difference I can think of now is that one of the machines is multihomed, but that might be unrelated. rtpkeepalive is set to 2 and I can confirm is by doing `sip show settings`. I've tried all

Hi, I'm having problems with sip remote attended transfer using 2 asterisk boxes (same version, latest 1.4.X). Whenever I transfer from a call from box A to a call on box B, one call leg of the transferring phone is not disconnected (the one that is normally dropped by server side, phone disconnects the other one). The same situation works perfectly with local attended transfer. Is anyone

Hi, I've got different customers that may use the same asterisk. Each user can blind-transfer a call to whatever place they want. But of course the transferring side should be billed for it. What can I do to see the difference between the channels here? If there is an A->B call going on, I'd like to know which side did the transfer - but whichever side does it, I get back to context

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

x<-seq(-1,1,length=10) y<-seq(-1,1,length=10) a<-matrix(c(1,2,2,1),2,2) b<-matrix(c(2,1,1,2),2,2) fv<-function(x,y) { m<-x*a+y*b t<-m[1,1]+m[2,2]; d<-m[1,1]*m[2,2]-m[1,2]^2 return((t-sqrt(t^2-4*d))/2) } gv<-function(x,y) { t<-x*(a[1,1]+a[2,2])+y*(b[1,1]+b[2,2]) d<-(x*a[1,1]+y*b[1,1])*(x*a[2,2]+y*b[2,2])-(x*a[1,2]+y*b[1,2])^2 return((t-sqrt(t^2-4*d))/2) }