search for: sample_sizes

...like this in R as you are missing close braces and haven't really worked out the difference between the number of calculations you are doing for each replication and the number of replications. It takes 5-10 minutes to run. ## Put the samples sizes into matrix then use a loop for sample sizes sample_sizes<- matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), nrow=2) #create vector to combine all std deviations sds<-c(4,6,8,10,12,14) # this number is needed below nsds<-length(sds) set.seed(8) #number of simulations nSims<-10000 #set significance level,alpha for the w...

...sing close braces and haven't really worked out the > difference between the number of calculations you are doing for each > replication and the number of replications. It takes 5-10 minutes to > run. > > ## Put the samples sizes into matrix then use a loop for sample sizes > sample_sizes<- > matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), > nrow=2) > > #create vector to combine all std deviations > sds<-c(4,6,8,10,12,14) > # this number is needed below > nsds<-length(sds) > set.seed(8) > > #number of simulations > nS...

Dear R users/developers, simple comparison of code execution time of R 2.7.1 and R 2.7.2 shows a dramatic slowdown of the newer version. Rprof() identifies .Call function as a main cause (see the code below). What happened with R 2.7.2? Kind regards Marek Wielgosz Bayes Consulting ######### Probably useful info ############### ### CPU: Core2Duo T 7300, 2 GB RAM ### WIN XP ### both standard

Okay, here is a more complete example: sample_sizes<- matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), nrow=2) # see what it looks like sample_sizes ssds<-c(4,4.4,5,6,8) nssds<-length(ssds) results<-list() # first loop steps through the sample for(ss in 1:dim(sample_sizes)[2]) { # get the two sample sizes ss1<...

...<-matrix(0,nrow=nSims,ncol=3) matrix5_unequal<-matrix(0,nrow=nSims,ncol=3) matrix8_mann <-matrix(0,nrow=nSims,ncol=3) # to ensure the reproducity of the result #here we declare the random seed generator set.seed(1) ## Put the samples sizes into matrix then use a loop for sample sizes sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), nrow=2) #shape parameter for both gamma distribution for equal skewness #forty five cases for each skewness!! shp<-rep(16/9,each=5) #scale parameter for sample 1 #scale paramter for sample 2 set as constant 1 scp1<-c(...

...rix5_unequal<-matrix(0,nrow=nSims,ncol=3) > matrix8_mann <-matrix(0,nrow=nSims,ncol=3) > > # to ensure the reproducity of the result > #here we declare the random seed generator > set.seed(1) > > ## Put the samples sizes into matrix then use a loop for sample sizes > sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), > nrow=2) > > #shape parameter for both gamma distribution for equal skewness > #forty five cases for each skewness!! > shp<-rep(16/9,each=5) > > #scale parameter for sample 1 > #scale paramter for...

Hello R-list, I am plotting a weighted linear regression in R. The points on my chart are also scaled to sample size, so some points are large, some are small. I have figured out everything I need using the plot() function: how to plot the points, scale them by sample size, weight the linear regression by sample size, plot that line, and plot the labels for the points. However, although the

...rix5_unequal<-matrix(0,nrow=nSims,ncol=3) > matrix8_mann <-matrix(0,nrow=nSims,ncol=3) > > # to ensure the reproducity of the result > #here we declare the random seed generator > set.seed(1) > > ## Put the samples sizes into matrix then use a loop for sample sizes > sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), > nrow=2) > > #shape parameter for both gamma distribution for equal skewness > #forty five cases for each skewness!! > shp<-rep(16/9,each=5) > > #scale parameter for sample 1 > #scale paramter for...

...n one code. can anyone give me a brief idea on it. I just managed to write till here and it perhaps isnt correct .. #set up matrix for storing data from simulation matrix_t <-matrix(0,nrow=nSims,ncol=3) matrix_u<-matrix(0,nrow=nSims,ncol=3) matrix_mann <-matrix(0,nrow=nSims,ncol=3) sample_sizes<- matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), nrow=2) p1<-p2<-p3<-vector() nSims<-10 alpha<-0.05 set.seed(1) simulation<-function(m,n,sds) { for (ss in 1:dim(sample_sizes[2])){ m<-[ss,1] n...

hi, i am new in this field. do favorite<http://stackoverflow.com/questions/36404707/simulation-study-of-2-sample-test-on-different-combination-of-factors#> If I wish to conduct a simulation on the robustness of two sample test by using R language, is that any ways in writing the code? There are several factors (sample sizes-(10,10),(10,25),(25,25),(25,50),(25,100),50,25),(50,100),

On 11 Feb 2010, at 06:53, Vikrant Shimpi wrote: > Dear Luka , > I am using qcc package in R to plot SPC charts. BUt while plotting > R chart I had a error. My question is it necessary while plotting > R Chart the group sample size must be < 25 ?. Because when I took > group sample size as 1000 it gave me error, till I took group sample > size as 26, But as sooon as

...-matrix(0,nrow=nSims,ncol=3) >>> >>> # to ensure the reproducity of the result >>> #here we declare the random seed generator >>> set.seed(1) >>> >>> ## Put the samples sizes into matrix then use a loop for sample sizes >>> >>> sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), >>> nrow=2) >>> >>> #shape parameter for both gamma distribution for equal skewness >>> #forty five cases for each skewness!! >>> shp<-rep(16/9,each=5) >>> >>>...

...uld you give me some advice ? > > #For gamma disribution with equal skewness 1.5 > rm(list=ls()) > nSims<-100 > alpha<-0.05 > > #here we declare the random seed generator > set.seed(3) > > ## Put the samples sizes into matrix then use a loop for sample sizes > sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100),nrow=2) > > #shape parameter for both gamma distribution for equal skewness > shp<-rep(16/9,each=45) > > #scale parameter for sample 1 > #scale paramter for sample 2 set as constant 1 > d1<-matrix(c(...

...=3) >> matrix8_mann <-matrix(0,nrow=nSims,ncol=3) >> >> # to ensure the reproducity of the result >> #here we declare the random seed generator >> set.seed(1) >> >> ## Put the samples sizes into matrix then use a loop for sample sizes >> >> sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), >> nrow=2) >> >> #shape parameter for both gamma distribution for equal skewness >> #forty five cases for each skewness!! >> shp<-rep(16/9,each=5) >> >> #scale parameter for sample...

...n one code. can anyone give me a brief idea on it. I just managed to write till here and it perhaps isnt correct .. #set up matrix for storing data from simulation matrix_t <-matrix(0,nrow=nSims,ncol=3) matrix_u<-matrix(0,nrow=nSims,ncol=3) matrix_mann <-matrix(0,nrow=nSims,ncol=3) sample_sizes<- matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100), nrow=2) p1<-p2<-p3<-vector() nSims<-10 alpha<-0.05 set.seed(1) simulation<-function(m,n,sds) { for (ss in 1:dim(sample_sizes[2])){ m<-[ss,1] n...

Hi all, I'm having a problem with the auto.key function in xyplot. I hate to bother the list like this and I'm positive I must be missing something very simple, yet I've spent the last day searching for a solution to no avail. Essentially, I want a key that contains entries in which the plot points are superimposed on a line of the same color as the points, like this: o--o--o Now,

...l skewness 1.5 >>> rm(list=ls()) >>> nSims<-100 >>> alpha<-0.05 >>> >>> #here we declare the random seed generator >>> set.seed(3) >>> >>> ## Put the samples sizes into matrix then use a loop for sample sizes >>> sample_sizes<-matrix(c(10,10,10,25,25,25,25,50,25,100,50,25,50,100,100,25,100,100),nrow=2) >>> >>> #shape parameter for both gamma distribution for equal skewness >>> shp<-rep(16/9,each=45) >>> >>> #scale parameter for sample 1 >>> #scale paramter for...

I'm using flac-1.2.1 and visual studio 2010 on windows xp sp3 and I want to compress raw CDDA with the stream encoder to a file. But I just can't get the flac encoder to work, the encoder always returns an error enc.get_state() == "FLAC__STREAM_ENCODER_VERIFY_MISMATCH_IN_AUDIO_DATA" get_verify_decoder_state() == "FLAC__STREAM_DECODER_READ_FRAME" this is the code I

Hi there: I'm having a weird problem with my startup procedure. R.app is reading an unknown .Rprofile file. First, I'm on a Mac Os 10.6.8 running R.app 2.15.0 On startup > getwd() [1] "/Users/simon" But: the contents of my .Rprofile file in my home directory when viewed with a text editor are: .First<-function() {

Hi, I sent the below a few days ago. It still hasn't turned up in the archive so i'm trying again. Since the first mail was sent, i've found out that removing the call to *_set_periods and *_set_period_size is a better solution to the problem. It seems that the alsa defaults are better than what ever fine tuning the code is trying to do. patch: ---