similar to: Post-processing of approximated irregular time series

Hi all, Issue: I have two datasets, one is a regular time series (rain gauge) with resolution of 10 minutes. The other one is an irregular time series (link). Now I want to analyze the correlation between these two datasets with linear regression. The regular time series is a data.frame and the irregular time series is a zoo object. Problem: For the irregular time series (link) I want also a 10

Hello! I'm looking for an easy way to filter data frames. Some rows in my data frame needs to be left out, for instance, rows with invalid data. Right now, if I want to pair two columns of this data frame, I have to do this: > pairs(as.data.frame(list(tstart=df$tstart[valid],tend=df$tend.sdr[valid]))) or this: >

Hi everyone, I was trying to mode the following exercise using R. The question: Set up a one pool model using numericintegration. The model will run from time 1 to time 30 using a time step of 1.The pool (A) will be fed by flux "inA" at a rate of 5 units per hour anddrained by flux "outA" at a rate of 20% per hour. At time 0, A has 5units. At time 30, what is the pool size of

  Hi there,   I am beginner in R and I have some basic question. Suppose I run a common procedure such as a t test or cox model like below:   out<-coxph( Surv(tstart,tstop, death1) ~ x1+x1:log(tstop+1) , test1,method=c("breslow"))    Which yields the following result:   Call: coxph(formula = Surv(tstart, tstop, death1) ~ x1 + x1:log(tstop +     1), data = test1, method =

Hi, I want to read a text file into R with .Call(). So I define some NEW_CHARACTER() to store the chracters read and use SET_STRING_ELT to fill the elements. e.g. PROTECT(qNames = NEW_CHARACTER(10000)); char *foo; // This foo holds the string I want. while(foo = readLine(FN)){ SET_STRING_ELT(qNames, i, mkChar(foo))); } In this way, I can get the desired character from qNames. The only problem

I have data that looks like this time_stamp (seconds) user_id The data is (partial) ordered by time - in that sometimes transactions occur at the same timestamp. The output I want is collated by transaction time on a per user basis, normalized by the maximum number of transactions per user, and aggregated over each day. So, if the users have 50 transactions in the first day and 20 transactions

Hi all, Sometimes the process cdrommon use 99,8% of CPU and the result is that all vms becames very slow. I killed the process and now everything is fine but I can''t live migrate VMS. I found this problem also in this citrix thread: http://forums.citrix.com/thread.jspa?messageID=1475962 <http://forums.citrix.com/thread.jspa?messageID=1475962&tstart=0#1475962>

Hello, I have a cohort with approx 1,200 patients at the ages of 30-65 that had their first myocardial infarction during 1992: · They were in a follow up until 2005. · About 400 of them died during this period of time (right censored) · Each one of them had up to 4 mi recurrent events. I am using the semi-parametric model in order to assess the relationship of

Hi there, I have a rather large data set and perform the following cox model: test1 <- list(tstart,tstop,death1,chemo1,radio1,horm1) out1<-coxph( Surv(tstart,tstop, death1) ~ chemo1+chemo1:log(tstop+1)+horm1+horm1:log(tstop+1)+age1+grade1+grade1:log(tstop+1)+positive1+positive1:log(tstop+1)+size1+size1:log(tstop+1), test1) out1 Up to here everything works fine (with each covariate

I frequently see this from my iPhone/iPad IMAP users: Oct 24 21:30:55 server dovecot: imap-login: Login: user=<user>, ... [... repeated 10 times ...] Oct 24 21:32:54 server dovecot: imap-login: Maximum number of connections from user+IP exceeded (mail_max_userip_connections=12): user=<user> Oct 24 21:32:54 server dovecot: imap(user): Logged out ... [... repeated 11 times

Hi, when i run the following code i get this massege: "The instruction at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart,

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Hi Nicolas, 2009/3/30 Nicolas Geoffray <nicolas.geoffray at lip6.fr> > Can you be more verbose on this? Are you planning to implement some JVM > or .Net extension for supporting your new intrinsics? Or are you just > looking for a runtime with a GC? At the moment I am not looking to add any new extensions to JVM or .NET. I would need a runtime with a GC to demonstrate and test

Hi there, I''ve written a small manual on how to install OpenSolaris 2008/05 as paravirtualized domU. http://wiki.xensource.com/xenwiki/OpenSolarisPVonLinux Maybe, this is useful for someone. Cheers, Stepha _______________________________________________ Xen-users mailing list Xen-users@lists.xensource.com http://lists.xensource.com/xen-users

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Hi Milos, Milos Puzovic wrote: > Hi, > > I would like to extend LLVM IR with two new intrinsic: spawn and join. > The spawn intrinsic will indicate that the call it modifies can safely > run in parallel, while join intrinsic will indicate that the execution > of the current call cannot continue until all previously spawned calls > in the list/array passed as argument to

Hi, I would like to extend LLVM IR with two new intrinsic: spawn and join. The spawn intrinsic will indicate that the call it modifies can safely run in parallel, while join intrinsic will indicate that the execution of the current call cannot continue until all previously spawned calls in the list/array passed as argument to join intrinsic have completed and returned their results to it. This

Oh -- then I did not understand the documentation. fallback-mount This optional value specifies a mountpoint that clients are automatically moved to if the source shuts down or is not streaming at the time a listener connects. Only one can be listed in each mount and should refer to another mountpoint on the same server that is streaming in the _same streaming format_. I was thinking that

I have some time stamped events that are supposed to be unrelated. I have plotted them and that assumption does not appear to be valid. http://metrak.com/tmp/sevents.png is a plot showing three sets of events over time. For the purpose of this exercise, the Y value is irrelevant. The series are not sampled at the same time and are not equispaced (just events in a log file). The plot is

I haven't done any R memory debugging lately, but https://www.mail-archive.com/rcpp-devel at lists.r-forge.r-project.org/msg10289.html shows how I used to have gdb break where valgrind finds a problem so you could examine the details. Also, running your code after running gctorture(TRUE) can help track down memory problems. -Bill On Wed, Feb 7, 2024 at 12:03?PM Therneau, Terry M., Ph.D.