search for: stdevs

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120",

I have been trying the integrate function in R, a function which would be very useful for a current project of mine. But I am encountering errors integrating the one function I have tried. The function to be integrated is a product of a gamma demsity and a normal density: gamma.by.normal_function(y,x,shape,scale,stdev) return( dgamma(y,shape=shape,scale=scale)*

Holding the vblk->lock across kick causes poor scalability in SMP guests. If one CPU is doing virtqueue kick and another CPU touches the vblk->lock it will have to spin until virtqueue kick completes. This patch reduces system% CPU utilization in SMP guests that are running multithreaded I/O-bound workloads. The improvements are small but show as iops and SMP are increased. Khoa Huynh

Holding the vblk->lock across kick causes poor scalability in SMP guests. If one CPU is doing virtqueue kick and another CPU touches the vblk->lock it will have to spin until virtqueue kick completes. This patch reduces system% CPU utilization in SMP guests that are running multithreaded I/O-bound workloads. The improvements are small but show as iops and SMP are increased. Khoa Huynh

R-help, I'm just trying to find the ML (maximum likelihood) estimates of the mean and standard deviation of a set of observations: >xx=c(2.5,3.5,4,6,6.5,7.5) fn<-function(params,x=xx) { media<-params[1] st <-params[2] pdf=-sum(dnorm(log(xx),log(media),st,TRUE)) return(pdf) } optim(c(mu,stdev),fn,method="L-BFGS-B",lower=c(0.001, 0.001) ,upper = rep(Inf, 2),

Dear R-Help, I also send the following post by e-mail to you, however I try to post it here aswell. My name is Henrik and I am currently trying to solve a Maximum Likelihood optimization problem in R. Below you can find the output from R, when I use the "BFGS" method: The problem is that the parameters that I get are very unreasonable, I would expect the absolute value of each

Dear list members, I would like to create two series of plotted mean values and error bars, yet with different locations along the x-axis. Plotting of first series using plotCI with the standard arguments goes without any problem. However I do not succeed to add the second series in the same plot, which should be horizontally shifted from the first series along the x-axis. The “add=TRUE”

Is there a "canned" function in R for finding the standard error of the mean? I have tried > sem <- function(x) c(mean =mean(x), + SEM = stdev(x)/sqrt(length(x))) > sem(pnet.lai) Error in sem(pnet.lai) : couldn't find function "stdev" It looks like there is no stdev function in R Thanks, Kirk Kirk R. Wythers email: kwythers at umn.edu University of

Hi all, I've been trying (unsuccessfully) to modify an xyplot I created using the lattice package. I would like to change default strip colors and locations. I started with numeric data in 4 columns, which look like this: 0.25 2 1 32 0.25 2 2 30 0.25 2 3 27 0.25 2 4 23 0.25 2 5 17 0.25 3 1 30 0.25

2017-10-10 8:25 GMT+02:00 Karan Sandha <ksandha at redhat.com>: > Hi Gandalf, > > We have multiple tuning to do for small-files which decrease the time for > negative lookups , meta-data caching, parallel readdir. Bumping the server > and client event threads will help you out in increasing the small file > performance. > > gluster v set <vol-name> group

Hi I'm trying to create tables of means, standard deviations and numbers of observations (i) for each laboratory (ii) for each batch number (iii) for each batch at each laboratory for the attached data. I created these functions: summary.aggregate <- function(y, label, ...) { temp.mean <- aggregate(y, FUN=mean, ...) temp.sd <- aggregate(y, FUN=sd, ...) temp.length <-

I recently started using R 2.7.2, and noticed a surprising change in the behavior of var() on NA data: R 2.6.1 (Patched), 2007-11-26, svn.rev 43541, x86_64-unknown-linux-gnu: > stdev(rep(NA,3), na.rm=F) [1] NA > stdev(rep(NA,3), na.rm=T) [1] NA > var(rep(NA,3), na.rm=T, use="complete.obs") [1] NA R 2.7.2 (Patched), 2008-09-02, svn.rev 46491,

Dear All, I have this script: dat <- data.frame(Month = hstat$Date,C_avg = hstat$C.avg,C_stdev = hstat$C.stdev) ggplot(data = dat, aes(x = Month, y = C_avg, ymin = C_avg - C_stdev, ymax = C_avg + C_stdev)) + geom_point() + geom_line() + geom_errorbar() dat <- data.frame(Month = hstat$Date,K_avg = hstat$K.avg,K_stdev = hstat$K.stdev) ggplot(data = dat, aes(x = Month, y = K_avg,

Hi I am fairly new to R and I would appreciate some help to hopefully a trivial problem. I created a function: summary.aggregate <- function(y, ...) { temp.mean <- aggregate(y, FUN=mean, ...) temp.sd <- aggregate(y, FUN=sd, ...) temp.length <- aggregate(y, FUN=length, ...) temp <- data.frame(cbind(mean=temp.mean$x,stdev=temp.sd$x,n=temp.length$x)) } this outputs e.g.:

this is probably not really a R specific question, if so apologies for off-topic posting: I'm interested in the probability density function of the maximum values from repeated samples of size N from a normal distribution: smp <- rnorm(N, meanval, stdev) with some mean 'meanval' and standard deviation 'stdev'. I would like to know what is the frequency distribution of

SAS Enterprise Miner recommendeds to standardize using X / STDEV(X) versus [X ? mean(X)] / STDEV(X) Any thoughts on this? Pros Cons Philip

I made simulation with Weibull and create Matrix, How can I create mean/min/max/stdev on column or rows of matrix?, Thanks,

I just tried setting: performance.parallel-readdir on features.cache-invalidation on features.cache-invalidation-timeout 600 performance.stat-prefetch performance.cache-invalidation performance.md-cache-timeout 600 network.inode-lru-limit 50000 performance.cache-invalidation on and clients could not see their files with ls when accessing via a fuse mount. The files and directories were there,