similar to: Question About Repeat Random Sampling from a Data Frame

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

I have a data set that has 4 columns and 53860858 rows. I was able to read this into R with: cc <- rep("character",4) myData <- read.table("myData.csv",header=FALSE,skip=1,colClasses=cc,nrow=53860858,sep=",") I need to reshape this data from long to wide. On a small data set the following lines work. But on the real data set, it didn't finish even when I

Greetings: I am looking for some help (probably really basic) with looping. What I want to do is repeatedly sample observations (about 100 per sample) from a large dataset (100,000 observations). I would like the samples labelled sample.1, sample.2, and so on (or some other suitably simple naming scheme). To do this manually I would >smp.1 <- sample(100000, 100) >sample.1 <-

Hi, I need to sample randomly my dataset for 1000 times. The sample need to be the 80%. I know how to do that, my problem is that not only I need the 80%, but I also need the corresponding 20% each time. Is there any way to do that? Alternatively, I was thinking to something like setdiff () function to compare my 80% sample to the original dataset and obtain the corresponding 20%, unfortunately

Hello! I have a data frame mysample (sorry for a long way of creating it below - but I need it in this form, and it works). I regress Y onto X1 through X11 - first without weights, then with weights: regtest1<-lm(Y~., data=mysample[-13])) regtest2<-lm(Y~., data=mysample[-13]),weights=mysample$weight) summary(regtest1) summary(regtest2) Then I calculate Durbin-Watson for both regressions

Dear all, I have a data similar to this: myframe<- data.frame (ID=c("Ernie", "Ernie","Ernie","Ernie"), Timestamp=c("24.09.2012 08:00", "24.09.2012 09:00", "24.09.2012 10:00", "25.09.2012 10:00"), Longitude=c("8.481","8.482","8.483","8.481"),

Dear all, i have a dataset of about 400 records , with a variable that has  two levels 40 bad and 360 good among other variables,how do i come up  with10 random samples that have the composition of as the main sample  but maintaining the 40 bad 360 good with replacement, i recently discovered that my random samples generated dont maintain the ratio. My code is as  : mysample <-

Thank you for providing the example code... for the request of running it multiple times it would have helped if you could have confirmed that the example ran through without errors... there were a lot of mistakes in it. Look into using the reprex package to check your example next time. I don't do this kind of analysis... I really don't know what to expect from the functions. The

[answers inline] On 18 August 2017 at 20:08, Dagmar <Ramgad82 at gmx.net> wrote: > > myframe<- data.frame (ID=c("Ernie", "Ernie","Ernie","Ernie"), > Timestamp=c("24.09.2012 08:00", "24.09.2012 09:00", "24.09.2012 10:00", > "25.09.2012 10:00"),

Dear list I wish to extract from a population genotypized for 10 SNP a subsample of the same population of size n with similar allele frequencies. Essentially i have a matrix of 200 rows (df) like this Name,Condition,rs1385699_X,rs6625163_X,rs962458_X,Rs4658627_1, sample01,Case,1,1,1,-1 sample02,Control,1,1,1,1 sample06,Control,1,-1,1,0 sample10,Case,1,1,1,0 sample11,Control,1,1,1,1

The file TENSILE.DAT from the Hand et al "Handbook of Small Data Sets" looks like this: 0.023 0.032 0.054 0.069 0.081 0.094 0.105 0.127 0.148 0.169 0.188 0.216 0.255 0.277 0.311 0.361 0.376 0.395 0.432 0.463 0.481 0.519 0.529 0.567 0.642 0.674 0.752 0.823 0.887 0.926 except that my mail client has replaced the tab separators by blanks. If I paste this data into R 2.2.1 what I get is

Here is the file minimal.Snw: \documentclass[a4paper]{article} \title{R tips and tricks} \author{Murray Jorgensen} \usepackage{Sweave} \begin{document} \maketitle \section*{Entering data from a single variable} The following data are transformed tensile strength measurements on polyester fibres. They may be found on the file \texttt{TENSILE.DAT}. We may enter this data into R using the

Hello! I am using persp to get a plot of a 32x48 matrix. data <- as.matrix(read.table(file="mySample.dat")) persp(data) And I'm having trouble with trying to give a z-dependent palette to it like rainbow (or any built in one would do). How to combine that with the "col" argument? nice day Szilvi -- View this message in context:

Hi, I'm working with a data.frame containing values between 0 and 22000. Most of the values are actually between 0 and 50 and the high ones are outliers. I want to generate a boxplot and since the outliers are extremely high, I need to scale the y scale logarithmically. Otherwise one wouldn't really see the boxes of the boxplot. boxplot(dat, log="y", ylim=c(0,

Hello all, Need some suggestions on interesting use cases with R in the field of log processing. Any help would be greatly appreciated. -- With Thanks and Regards, Ramprakash Ramamoorthy, India, +91 9626975420 [[alternative HTML version deleted]]

Hi All, I want to take a simple random sample from a large dataset, gly, but I'm getting an error message. Any help? dim(gly) [1] 112371 37 > s1 <- sample(gly,100) Error in `[.data.frame`(x, .Internal(sample(length(x), size, replace, : cannot take a sample larger than the population when 'replace = FALSE' Thanks, Rachel [[alternative HTML version

Full_Name: Peter Gedeck Version: R1.6.2 and R1.7.0 OS: Windows XP Submission from: (NULL) (194.191.169.72) Hello, I marked the bug report Private, as I don't want my email address on the web server. The problem that I found is best explained using an example. index <- 1:6 cluster <- c(1,1,1,2,2,3) tapply(index,cluster,sample) gives $"1" [1] 2 1 3 $"2" [1] 4 5

Dear R users, I am a beginner to R. I generated 1000 samples with 15 data in each sample I tried finding the variance for each sample I used the code: m=1000;n=15 > r<-rnorm(15000) > for(i in 1:m){ x=data[,i] v=var(x)} what I got was just the variance for the last sample i.e. the 1000th sample but what I want is 1000 variance. Does anyone know what I did wrong? Thanks Chloe Smith

Dear All, I am trying to build a program which will take repeated samples (w/o replacement) from a population of values. The interesting catch is that I would like the sample values to be removed from the population, after each sample is taken. For example: pop<-c(1,5,14,7,9,12,18,19,65,54) sample(pop, 2) = lets say, (5,54) ## This is where I would like values (5, 54) to be removed from

Full_Name: Antonio Possolo Version: 1.3.1 OS: Linux (RH 7.1), Windows 2000 Submission from: (NULL) (192.35.44.3) A FEATURE THAT EASILY GENERATES BUGS sample(pi, size=1) produces 1, 2, or 3. sample(c(pi, pi), size=1) produces 3.141593 always. Although this conforms with the behavior explained in the help page for "sample", the behavior for the case where x (in sample(x, ...)) has