similar to: ARMA

Dear fellow CentOS users, I have never experienced this problem with hard disk management before and cannot explain it to myself on any rational basis. The setup: I have a workstation for testing, running latest CentOS 7.3 AMD64. I am evaluating oVirt and a storage-ha as part of my bachelors thesis. I have already been running a RAID1 (mdraid, lvm2) for the system and some oVirt 4.1 testing.

Hi, is there a possibilty to get my function output with "cat " as data.frame.rows with variables ? Var1---------------- 8 15 1 3 Var2---------------- 0.170 0.319 0.0213 0.0638 Var3---------------- 83.8 88.6 90 75 Var4---------------- 84.3 84.3 100 83.3 Var5---------------- 62.5 56 20 53.3 function(data.frame) { .... ....

Following from the R-help thread of March 22 on "Memory usage in prcomp", I've started looking into adding an optional 'rank.' argument to prcomp allowing to more efficiently get only a few PCs instead of the full p PCs, say when p = 1000 and you know you only want 5 PCs. (https://stat.ethz.ch/pipermail/r-help/2016-March/437228.html As it was mentioned, we already

I agree with Kasper, this is a 'big' issue. Does your method of taking only n PCs reduce the load on memory? The new addition to the summary looks like a good idea, but Proportion of Variance as you describe it may be confusing to new users. Am I correct in saying Proportion of variance describes the amount of variance with respect to the number of components the user chooses to show? So

I'm trying to understand how to use kpss.test() properly. If I have a level stationary series like rnorm() in the help page, shouldn't I get a small p-value with the null hypothesis set to "Trend"? The (condensed) output from kpss.test() for the two possible null hypotheses is given below. I don't see any significant difference between these results. > x <-

Hi- I am having trouble with the syntax of looping through the rows and columns of a data frame. I have a table with 17 observations for 84 lines at n=5-10 per line. So the table is ~700x17. I want to pull out the median and stdev for each line and put it in a dataframe with rowname = linename. So I have tried the following.... #read in the table input.table <- read.table(file =

Dear sir, I am extremely sorry for messing up the logic asking for help w.r.t. my earlier mails   I have tried to explain below what I am looking for.     I have a database (say, currency_rates) storing datewise currency exchange rates with some base currency XYZ.   currency_rates <- data.frame(date = c("12/31/2010", "12/30/2010", "12/29/2010",

> On 25 Mar 2016, at 10:41 am, peter dalgaard <pdalgd at gmail.com> wrote: > > As I see it, the display showing the first p << n PCs adding up to 100% of the variance is plainly wrong. > > I suspect it comes about via a mental short-circuit: If we try to control p using a tolerance, then that amounts to saying that the remaining PCs are effectively zero-variance, but

Hallo, I am totally confused why Excel and R give me totally different regression results for the data below. If you know the solution, please enlighten me. In Excel I used LINEST() and Data>Data Analysis>Regression and both (fortunately) gave the same result. The coefficients were: /b0=1.16, b1=0.957, b2=0.024, R2=0.0027, adjusted R2=-0.017/ In R I used

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

Andy and I briefly discussed this the other day, we have not yet got chance to list a detailed pass order for the pre- and post- IPO scalar optimizations. This is wish-list in our mind: pre-IPO: based on the ordering he propose, get rid of the inlining (or just inline tiny func), get rid of all loop xforms... post-IPO: get rid of inlining, or maybe we still need it, only

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380

Hi, I installed Fedora 10 as domU on CentOS 5.3. I need new 2.6.25.14 kernel under domU (in order to patch it for MPLS protocol). I followed advice on http://wiki.xensource.com/xenwiki/Kernel.org_Linux_on_Xen, and compiled kernel with XEN options enabled under domU. I got all relevant packages in /boot and /lib/modules, but when I try to boot virtual machine I got this error: Error starting

I currently have wine-20021007 installed. I installed starcraft and all went well then i went to run the game and a message box comes up that says: Your system does not apear to be correctly configured. This problem may be corrected by changing the virtual memory settings for Windows. Ensure that your system has at least 20 megabytes of free disk space, then check your virtual memory