similar to: Can R do rts (Regular Time Series) like S-Plus?

Dear readers I would like to make General Linear Model (GLM) for the following data set http://louise.hoffman.googlepages.com/fuel.csv The code I have written is fuelData<-read.table('fuel.csv',header=TRUE, sep=',') n<-dim(fuelData)[1] xOnes<- matrix(1,nrow=n,ncol=1) x<-cbind(xOnes,fuelData[,3]) y<-fuelData[,4] theta<-((t(x)%*%x)^(-1))%*%t(x)%*%y which gives

Dear developers I have just noticed your fantastic work, and I am looking forward to send you dumps of my GeForce 6150, when I get vacation in two weeks. It is an integrated GPU on my Asus A8N-VM CSM, so I hope it will work. The reason I post is, I am wondering what the motivation is for doing nVidia and not ATi? I have read the "FAQ:Why are you doing this?", but it doesn't

Dear readers, I am using Fedora Core Linux 6 and Wine installed from rpm packages. I haven't got Windows installed, so what files should I copy from a working Windows? I assume XP is best? Lots of love, Louise

Hi, When REnouveau came to "74/75 test_interesting_registers" the CPU used 100% and I could only use the mouse, but not interact witt anything. What could be wrong? I am using GeForce 6150 and Fedora 7. Love, Louise

Dear developers I would like to submit dumps, but I spend ~4 hours last time on installing the nVidia drivers, and ended up with the livna.org RPM's. So can I install the Nouveau driver without damaging the nVidia drivers? I am using Fedora 7 and integrated NVIDIA GeForce 6150. That's the C51 core, right? Hugs, Louise

Hi Not sure if this is going to the right place… I am attempting to use a S-Plus workspace that has been saved via the dump facility. These include a number of commands not recognised by R, namely rts commands. Is there a way round this by changing these in some way for R’s ts commands? Any help much appreciated. Adam [[alternate HTML version deleted]]

anybody tell me does wine supported rts program.. ?

I have two samba servers running Ubuntu 10.04 Samba Version 3.4.7 One server acts as domain controller and stores user ids in a .tdb Somehow I've ended up with a duplicate user name. On the Domain Controller # pdbedit -w -L|grep debbie debbie:1005:XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX:84DEC6FE3B018B0FB977EDDF5009742C:[U ]:LCT-4D4B086F: On the other Server running winbind I get #

I ungraded my Linux Operating system from Ubuntu 16.04 LTS to 8.04 LTS Samba will not start after an upgrade to 18.04. My samba is running as an NT style member server server with smbd nmbd and winbindd all running. None of them will start. It worked this morning. This morning it was running Version 4.3.11-Ubuntu This afternoon after the upgrade it's Version 4.7.6-Ubuntu The logs in

Hi, I am performing GEV analysis on temperature/precipitation data and want to use the ks.boot function but I am unsure of how to implement it using the pgev distribution. For example: ks.test(data,pgev,shape,location,scale) ks.boot(data,(distribution parameters?),alternative="pgev",nboots=1000) Any advice? Apologies in advance if I have used the wrong email address. Regards, Louise

On Fri, Apr 22, 2016 at 1:32 AM, Louise Mair <louise.mair at slu.se> wrote: > Dear R Users, > > I am encountering a problem when reading nc files into R using the ncdf > and ncdf4 libraries. The nc files are too large to attach an example (but > if someone is interested in helping out I could send a file privately via > an online drive), but the code is basic: > ?[...]?

Please can someone advise me how I can adjust correlations using bonferroni's correction? I am doing manny correlation tests as part of an investigation of the validity/reliability of a psychometric measure. Help would be so appreciated! Cheers, Louise

Dear UseRs, I wrote following function in order to solve Data Envelopment Analysis. Reason for posting is that the function is slow when nrow(dat) is large. I wonder if other functions could substitute the for() loop in the code, such as mapply(). Can anybody help to rewrite the dea() function as efficiently as possible? The code is as follows:

Hi there, I have subscribed to R-help but am not sure how to view or post questions? I think this is the right way. I am planning on doing a multivariate regression investigating the relationship between depression (a continuous variable) and social support variables (mostly continuous, some categorical) among older people. I have a number of demographic and health-related variables that I am

Is plot(ts.object) equivalent to the S-Plus ts.plot ? -- ------------------------------------------------------------------------- Konstantinos E. Vorloou | Tel: +44 (0)191 374 1821 Department of Economics & Finance | Fax: +44 (0)191 374 7289 University of Durham, | email: K.E.Vorloou at durham.ac.uk 23/26 Old Elvet, | or : vorlow at rocketmail.com

Dear readers, Does anyone know how I can keep the file permissions on the destination side when I sync? This is what I have done so far. In /etc/rsyncd.conf on the server have I: ------------------------------------------------------- log file = /var/log/rsyncd.log pid file = /var/run/rsyncd.pid lock file = /var/run/rsync.lock [server] path = / uid = root gid = root read only = yes

Last line in the following (updated) code produces the error require(raster) require(rts) require(stringr) r <- raster(ncol=100, nrow=100) values(r) <- runif(ncell(r)) stack(r)->s r->rs for(i in 1:23){ rs[]<-r[]*i addLayer(s,rs)->s print(nlayers(s)) } dt<-list(ID=seq(1:24),month=rep(formatC(1:12,flag=0,width=2),2), year=sort(rep(2016:2017,12)))

Hello I have been trying to figure out how to randomly select a number of records. I have a column "ID" which contains 16 individuals (numbered 1-16), each individual has a few hundred rows of GPS locations associated with it. I am trying to select a random sample of these individuals (with all their associated GPS locations) to run a kernel density analysis on, and need to repeat this

It works if you use as.Date. But this defeates the purpose for the yearmon notion... require(raster) require(rts) require(stringr) r <- raster(ncol=100, nrow=100) values(r) <- runif(ncell(r)) stack(r)->s r->rs for(i in 1:23){ rs[]<-r[]*i addLayer(s,rs)->s print(nlayers(s)) } dt<-list(ID=seq(1:24),month=rep(formatC(1:12,flag=0,width=2),2), year=sort(rep(2016:2017,12)))

Hello, I am working with a dataset which essentially has only one column - a list of distances in metres, accurate to several decimal places. eg distance 1000 6403.124 1000 1414.214 1414.214 1000 I want to organise this into a frequency table, grouping into categories of 0 - 999, 1000 - 1999, 2000-2999 etc. I'd also like the rows where there are no data points in that category to