similar to: Analyze multiple data set simultaneoulsy

Hi, I''m running Opensolaris 2009.06, and I''m facing a serious performance loss with ZFS ! It''s a raidz1 pool, made of 4 x 1TB SATA disks : zfs_raid ONLINE 0 0 0 raidz1 ONLINE 0 0 0 c7t2d0 ONLINE 0 0 0 c7t3d0 ONLINE 0 0 0 c7t4d0 ONLINE 0 0

Dear all: Assume I have 3 distributions, x1, x2, and x3. x1 ~ normal(mu1, sd1) x2 ~ normal(mu2, sd2) x3 ~ normal(mu3, sd3) y1 = x1 + x2 y2 = x1 + x3 Now that the data I can observed is only y1 and y2. It is easy to estimate (mu1+m2), (mu1+mu3), (sd1^2+sd2^2) and (sd1^2+sd3^2) by EM algorithm since y1 ~ normal(mu1+mu2, sqrt(sd1^2+sd2^2)) and y2 ~ normal(mu1+mu3, sqrt(sd1^2+sd3^2)) However, I want

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

hello, I would like to create the following barplot: I have 4 different data sets (same length + stddev for each data point) data1 sd1 data2 sd2 data3 sd3 data4 sd4 now, I'd like to plot in the following way: data1[1],data2[1],data3[1],data4[1] with it's sd-values side-by-side at one x-axis label (named "position 1") and each bar in different colors.

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

Hi, I run this code to get the power of the test for modified bartlett's test..but I'm not really sure that my coding is right.. #normal distribution unequal variance asim<-5000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) n1<-20 n2<-20 n3<-20 mu<-0 sd1<-sqrt(25) sd2<-sqrt(50) sd3<-sqrt(100) g1<-rnorm(n1,mu,sd1) g2<-rnorm(n2,mu,sd2)

----- Forwarded Message ----- From: vramaiah at neo.tamu.edu To: "bernhard pfaff" <bernhard.pfaff at pfaffikus.de> Sent: Friday, November 11, 2011 9:03:11 AM GMT -06:00 US/Canada Central Subject: Use of R for VECM Hello Fellow R'ers I am a new user of R and I am applying it for solving Bi-Variate (Consumption and Output) VECM with Co-Integration (I(1)) with three lags on

I have historically noticed that in ZFS, when ever there is a heavy writer to a pool via NFS, the reads can held back (basically paused). An example is a RAID10 pool of 6 disks, whereby a directory of files including some large 100+MB in size being written can cause other clients over NFS to pause for seconds (5-30 or so). This on B70 bits. I''ve gotten used to this behavior over NFS, but

Hi all, just after sending a message to sunmanagers I realized that my question should rather have gone here. So sunmanagers please excus ethe double post: I have inherited a X4140 (8 SAS slots) and have just setup the system with Solaris 10 09. I first setup the system on a mirrored pool over the first two disks pool: rpool state: ONLINE scrub: none requested config: NAME

I am needing to create a new identifier variable for a data set which has no ID variable in the original file. I am basically wanting to take the count of each row and add "sd" in front of it - so it would look like so sd1 sd2 sd3 sd4 sd5 etc....... I have no idea how to do this. I am a SAS user trying to learn R. This question may have been answered previously, but I could be

Dear Colleague I am trying to figure out how to use R to do OLS restricted VECM regression. However, there are some notation I cannot understand. Please tell me what is 'ect', 'sd' and 'LRM.dl1 in the following practice: #OLS retricted VECM regression data(denmark) sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")] sjd.vecm<-

I updated an i386 system to b39 yesterday, and noticed this when running iostat: r/s w/s kr/s kw/s wait actv wsvc_t asvc_t %w %b device 0.0 0.5 0.0 10.0 0.0 0.0 0.0 0.5 0 0 c0t0d0 0.0 0.5 0.0 10.0 0.0 0.0 0.0 0.6 0 0 c0t1d0 0.0 65.1 0.0 119640001.5 0.0 0.0 0.0 0.3 0 2 c0t2d0 0.0 65.1 0.0 119640090.2 0.0

Windows 7, R 2.12.1 Colleagues, I am trying to understand the n.for.2means function. The code below is a copy of the function (renamed to n.for.2means.js). I have inserted a single line of code towards the bottom of the function which uses the cat function to print the value of n1. You will note the value (preceded by stars) is printed as 1. The function (1) prints a lot of output without any

Hi, I already found a conversation on the integration of a normal distribution and two suggested solutions (https://stat.ethz.ch/pipermail/r-help/2007-January/124008.html): 1) integrate(dnorm, 0,1, mean = 0, sd = 1.2) and 2) pnorm(1, mean = 0, sd = 1.2) - pnorm(0, mean = 0, sd = 1.2) where the pnorm-approach is supposed to be faster and with higher precision. I want to integrate a mixed

Hello everybody, I need to setup a Domain/subdomain environment with Windows AD. All the DCs run Windows Server 2012 R2. All domains (root and subdomains) The forest and domain functional level are set to Windows 2008 R2. I want to use Samba 4 server as fileservers in these domains, but up to now I have trouble adding Samba 4 member servers to Windows AD. My test environment is made of 2

For several years, I have been using Splus to analyze an ongoing series of datasets that have a bimodal distribution. I have used the following functions, in particular the ms() function, to estimate the parameters: two means, two standard deviations, and one proportion. Here is the code I've been using in S: btmp.bi <- function(vec, p, m1, m2, sd1, sd2) {

I have just recently (physically) moved a system with 16 hard drives (for the array) and 1 OS drive; and in doing so, I needed to pull out the 16 drives so that it would be light enough for me to lift. When I plugged the drives back in, initially, it went into a panic-reboot loop. After doing some digging, I deleted the file /etc/zfs/zpool.cache. When I try to import the pool using the zpool

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

Dear List: I am running into a memory issue that I haven't noticed before. I am running a simulation with all of the code used below. I have increased my memory to 712mb and have a total of 1 gb on my machine. What appears to be happening is I run a simulation where I create 1,000 datasets with a sample size of 100. I then run each dataset through a gls and obtain some estimates. This works

Dear all, I found a discrepancy while performing a power calculation for a two sample t-test in R and S-PLUS, respectively. For given values of sample number (5 each), sd (0.2) , significance level (0.01), and a desired power (80%) I looked for the difference in means. These values differ: 0.5488882 in R and 0.4322771 in S-PLUS (see dump below). Did I overlook any detail or confuse some