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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

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)) {

I''m running puppetd as root and I''m trying to execute tar as the "puppetd" user 59 define untar ( $source, $dest, $creates, $user = "puppetd" ) { 60 exec { "tar -xzf $source" : 61 cwd => $dest, 62 path => "/bin:/usr/bin", 63 user => $user, 64 creates => $creates,

Hi all R users ! I'm using gamm function from Simon Wood's mgcv package, to fit a spatial regression Generalized Additive Mixed Model, as covariates I have the geographical longitude and latitude locations of indexed data. I include a random effect for each district (dist) so the code is fit <- gamm(y~s(lon,lat,bs="tp", m=2)+offset(log(exp.)), random=list(dist=~1),

Hello Everyone I have an R script (and a source file which I keep my functions) that I need to run on 70 data sets (each consisting of a pair of files). I wish to run these data sets in a computer cluster that is run by my uni (HOWEVER they cannot help me with this problem but say it is do-able) the cluster is clever enough that if i set my data up as follows: within one folder called

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

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, 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:

Can anyone tell me the trick for obtaining the smoother matrix from smooth.spline when there are non-unique values for x. I have the following code but, of course, it only works when all values of x are unique. ## get the smoother matrix (x having unique values smooth.matrix = function(x, df){ n = length(x); A = matrix(0, n, n); for(i in 1:n){ y = rep(0, n); y[i]=1; yi =

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

Has anybody got the polycom acd function to work? I have the following setup: Debian 3.1 - 2.6.8 linux zlib-1.1.4 libpri-1.2.3 zaptel- 1.2.6 Asterisk - the bweschke/polycom_acd_funtions branch version - I get one error when doing a make install about needing a newer version of libpri and zaptel, I got the above versions from asterisk.org, are there newer version anywhere else? In the sip.conf

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

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) }

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, 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 have One Problem For Example if You Take Category has_many :products Here For Category i have soap as example for products i am taking Santoor , Lux and Rin soaps as products. Here Again Both Lux and santoor comes under Bathing soaps. Rin Soaps Comes under Washing Soaps So these are sub categories... Tom-arrow some new sub category may come like baby soaps.. I am confusing How many models i need ? how to define relation between them? But Finally We need To Access Data of 1]Total Soaps 2]Bathing Soaps 3]Washing Soaps...

Hello All, I have a working port of R on my SL5500. I've not tested the X windowing support yet, but was more concerned about the accuracy of the fp emulation. The following is the result of the test which Stuart Leask recommended I should try: Mandrake 8.2 > x<-NA > is.na(x) [1] TRUE > x+1 [1] NA > 2*x [1] NA Zaurus OZ3 > x<-NA > is.na(x) [1] TRUE > x+1 [1] 1

So I''m new to all this Rails stuff and this is probably a database design-related question to, but here it is... Just for learning I''m trying to build a little real estate listings application. Of course there is the listings model which will store basic information like name, price, description, and all that jazz. I''m to the point where I want to figure out how to