similar to: errors in integrate function?

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",

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”

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

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

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 <-

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

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,

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

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,

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),

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

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,

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

Excellent description, thank you. With performance.write-behind-trickling-writes ON (default): ## 4k randwrite # fio --randrepeat=1 --ioengine=libaio --gtod_reduce=1 --name=test --filename=test --bs=4k --iodepth=32 --size=256MB --readwrite=randwrite test: (g=0): rw=randwrite, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=32 fio-3.1 Starting 1 process Jobs: 1

I'm trying to annotate a density plot and I would like to have R calculate the standard deviation and place it in the plot next to the standard deviation symbol "sigma". I can successfully use the text command to paste "StDev =",round(sd(Data),digits=3)) on the plot. However, I have trouble when I want to replace "StDev" with the Greek symbol sigma (See code

Hello all, I have a simple function which calculates summary statistics of a dataset in terms of a factor (say area). > x = data.frame(Area = c(rep("cleanup", 5), rep("ref", 5)), TcCB = c(rnorm(5)+2, rnorm(5)));x Area TcCB 1 cleanup 2.5829747 2 cleanup 2.6796868 3 cleanup 2.5437094 4 cleanup 2.8453616 5 cleanup 1.1789683 6 ref 1.0140391 7

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.:

Hello, I am trying to calculate and plot mean and confidence intervall for a set of data. This is the code that I am currently using: means <- sapply(data, mean, na.rm=TRUE) n <- sapply(data,length) stdev <- sqrt(sapply(data, var, na.rm=TRUE)) ciw <- qt(0.98, n) * stdev / sqrt(n) par(mgp=c(2,0.6,0), las=2, fin=c(7,3), mai=c(1,0.5,0.2,0.2), cex=0.8) plotCI(x=means, uiw=ciw,