similar to: under sample problem

Dear List: I have created some code to simulate data from a complex sample where 5000 students are nested in 50 schools. My code returns a dataframe with a variable representing student achievement at a single time point. My actual code for creating this is below. What I would like to do is generate a second column of data that is correlated with the first at .8 and has the same means within

I'm trying to figure out R, a piece at a time, hours at a time... I was trying to copy the sample function in, "An Introduction to R" (for version 2.1.0) by W. N. Venables, D. M. Smith, page 42. Section 10.1 "Simple examples" provides a sample function which I tried to duplicate (I'm using Mac OS X 10.3.9, and "R for Mac OS X Aqua GUI v1.11). The following

Hi, I'm trying to write a function that will divide a given range of numbers into 3 sets using sample(), without repetition. Currently I'm trying this approach: r <- 1:10 s1 <- sample(r,size=3) Next, I want to remove the selected elements from r and sample() from the remainder. r <- r[ -(r=s1) ] s2 <- sample(r,size=3) When I go to remove the elements contained in s2 from

Hi everyone, > does any one knows how can I calculate mean for different samples > i.e. I have a data like this: > > s1 s2 s3 s4 > 1 0 0 0 1 > 2 1 0 1 0 > 3 0 0 0 0 > 4 0 0 0 0 > 5 0 1 0 1 > 6 1 0 0 0 > 7 0 0 0 0 > 8 0 0 0 0 > 9 0 0 0 0 > 10 0 0 0 1 > > I need to make 5 different sample with 5

Hello: I wonder if I could get a little help with random sampling in R. I have a vector of length 7375. I would like to draw 3 distinct random samples, each of length 100 without replacement. I have tried the following: d1 <- 1:7375 set.seed(7) i <- sample(d1, 100, replace=F) s1 <- sort(d1[i]) s1 d2 <- d1[-i] set.seed(77) j <- sample(d2, 100, replace=F) s2 <- sort(d2[j])

Dear all, I am working with taxonomic data, represented as a list of classes, orders, families, genera and finally species. > class(mydata) [1] "data.frame" > mode(mydata) [1] "list" > names(mydata) [1] "tclass" "torder" "tfamily" "tgenus" "tspecies" > length(mydata$tclass) [1] 161590 The first 10

Hello Listers, I am trying to sample a vector to create a new one of sample length, witha sum equal to the sum of the initial vector: initial = 10, 30, 10 (sum=50) sample example = 5, 35, 10 (sum=50) or 25, 15, 10 (sum=50), etc ... My problem is to control the sum, so it stays constant. Any suggestions would be very helpful ! Thank you in advance, Eric

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

Dear all, I am having trouble creating summary tables using aggregate function. given the following table: Var1 Var2 Var3 dummy S1 T1 I 1 S1 T1 I 1 S1 T1 D 1 S1 T1 D 1 S1 T2 I 1 S1 T2 I 1 S1 T2 D 1 S1 T2 D 1 S2

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Hello all, I believe this can be done using bootstrap, but I am wondering if there is some other way that might be used to tackle this. #Let's say I have two pairs of samples: set.seed(100) s1 <- rnorm(100) s2 <- s1 + rnorm(100) x1 <- s1[1:99] y1 <- s2[1:99] x2 <- x1 y2 <- s2[2:100] #And both yield the following two correlations: cor(x1,y1) # 0.7568969 (cor1) cor(x2,y2)

On Tue, 12 Oct 2004 14:01:21 -0500 (CDT) Chris Lattner <sabre at nondot.org> wrote: > On Tue, 12 Oct 2004, Morten Ofstad wrote: > > > This is my first post on this mailing list, so bear with me... My name > > is Morten Ofstad and I work for Hue AS (www.hue.no), a company that > > makes 3D Visualization software. We are looking into using LLVM for JIT > >

I have three modules: ----------------------------------------------------------------- s1.ll: %0 = type <{ i32, i32 }> define void @s1(%0* byval %myStruct) nounwind { return: ret void } ----------------------------------------------------------------- s2.ll: %0 = type <{ i32, i32 }> define void @s2(%0* byval %myStruct) nounwind { return: ret void }

Hello there, I wish to solve the following nonlinear System of equations: + u1 - Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) - Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) == 0 + u2 - Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) - Vmax21*S2/( S2 + Km21 *(1+S1/Km11)) == 0 + Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) + Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) - d1*P1 == 0 + Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) + Vmax21*S2/( S2 +

Hello, I have a problem with rsync and hard links : I have 1 folder : P, with 2 subfolders : S1 and S2 S2 contains a lot of hard links to file stored in folder S1. P : -S1 -S2 (S2 files hard links to S1 files) I would like to rsync the folder P to another computer, but each sub folder S1 (110 Go) and S2 (10 Go + hard link to 100 Go of S1) contains thousands of thousands of

Hi everyone, I have a dataset(named "Mydata") which includes 4 different variables named; s1,s2,s3,s4 .Each variable(symptom) has 14 patients. I need to use random sampling to make, 5 different samples from my data with 5 patients in each sample. i.e. using all 4 variables I need to make 5 different samples by changing patients(with 5 patients in each sample). X8 X9 X10 X102 X110

Hello everyone, I am stuck with an apparently simple issue : i) I have two sets S1 and S2, each containing a large number of integers, say zip codes. ii) Now, I just want to test whether a particular zip code belong to S1 or S2 or neither of them. iii) If it belongs to S1, the area/region gets a particular label, say 1; if it belongs to S2, it gets a label 2 and if it doesnot belong to either S1

Hi Jean-Marc, Thanks a lot for reviewing this huge assembly function! silk_warped_autocorrelation_FIX_c()'s kernel part is for( n = 0; n < length; n++ ) { tmp1_QS = silk_LSHIFT32( (opus_int32)input[ n ], QS ); /* Loop over allpass sections */ for( i = 0; i < order; i++ ) { /* Output of allpass section */ tmp2_QS = silk_SMLAWB(

Hello, This is my first post on this mailing list, so bear with me... My name is Morten Ofstad and I work for Hue AS (www.hue.no), a company that makes 3D Visualization software. We are looking into using LLVM for JIT compiling shader programs, to replace our own (slow) VM. A requirement for this is that we can compile LLVM in VS7.1, so I contacted Paolo Invernizzi to find the status of his

Hey! Could you please take a quick look at what I have done? Somehow I get wrong results using the anova(lm()) combination compared to doing a two way ANOVA by hand. Running: Data<-read.table("Data.txt"); g<-lm(ExM~S1*S2,Data); anova(g); Gives: Analysis of Variance Table Response: ExM Df Sum Sq Mean Sq F value Pr(>F) S1 1 4.3679