similar to: z.test for dataframe

Hi all, I'm trying to create a function where it can process a vector and also give a vector output accordingly #input: a,b anc c are constants, data is the vector #set the function fun<-function(a,b,c,data) { N=as.vector() for (i in min(data):max(data)){ if(i>c){ N<-(a*(i-c)^0.5)+(b*(i-c))} else {N<-0}} return(N) } #try dummy data=c(100,210,320,130,170,120,220,90,55,45)

Hi all, I have a list of 20000 data, and the list look like below. I wonder what is the simplest way to extract 'kappa' value (or 'xi' or 'alpha' for the matter) from each of the data. How can I simply code it without having to change the list to a dataframe first? Many thanks! $X19997 xi alpha kappa 784.7718640 165.4065141 -0.2709599 $X19998

Hi, I have this lame question. I want to convert a list (each with varies in length) to matrix with same row length by eliminating vectors outside the needed range. For example: l<-list(NULL) l[[1]]=1,2,3.7 l[[2]]=3,4,5,6,3 l[[3]]=4,2,5,7 l[[4]]=2,4,6,3,2 l[[5]]=3,5,7,2 #so say I want to only have 4 rows and 5 column in my matrix (or data.frame) and eliminating the 5th index value in l[[2]]

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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",

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

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

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 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.:

Hi, Jens & Rusty This version is rebased against linux-next which resolves the conflict with Paolo Bonzini's 'virtio-blk: allow toggling host cache between writeback and writethrough' patch. Patch 1/3 and 2/3 applies on linus's master as well. Since Rusty will pick up patch 3/3 so the changes to block core (adding blk_bio_map_sg()) will have a user. Jens, could you please

Hi, Jens & Rusty This version is rebased against linux-next which resolves the conflict with Paolo Bonzini's 'virtio-blk: allow toggling host cache between writeback and writethrough' patch. Patch 1/3 and 2/3 applies on linus's master as well. Since Rusty will pick up patch 3/3 so the changes to block core (adding blk_bio_map_sg()) will have a user. Jens, could you please

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

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)*

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

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

Hi, all Changes in v7: - Using vbr->flags to trace request type - Dropped unnecessary struct virtio_blk *vblk parameter - Reuse struct virtblk_req in bio done function - Added performance data on normal SATA device and the reason why make it optional Fio test shows bio-based IO path gives the following performance improvement: 1) Ramdisk device With bio-based IO path, sequential

Hi, all Changes in v7: - Using vbr->flags to trace request type - Dropped unnecessary struct virtio_blk *vblk parameter - Reuse struct virtblk_req in bio done function - Added performance data on normal SATA device and the reason why make it optional Fio test shows bio-based IO path gives the following performance improvement: 1) Ramdisk device With bio-based IO path, sequential