search for: uniformly

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not un...

I am trying to write the following code in R. The code works in S+ and i am trying to do the program in R. x=discrim(admit~gpa+gmat,prior=c("uniform"),data=data.mm) i wrote the following in R: x=lda(admit~gpa+gmat,data=data.mm) i could not figure out how to write prior=c("uniform") in R. I would get an error every time. I think that it has something to do with

Hi all, Some brain-storming on an issue with SPMD/SIMT backend support where I think some additional IR attributes would be useful. Sorry for the somewhat long mail; the short version of my current thinking is that I would like to have the following: 1) convergent: a call to a function with this attribute cannot be moved to have additional control dependencies; i.e., moving it from A to B is

...I have a bunch of files containing 300 data points each with values from 0 to 1 which also sum to 1 (I don't think the last element is relevant though). In addition, each data point is annotated as an "a" or a "b". I would like to know in which files (if any) the data is uniformly distributed. I used Google and found out that a Kolmogorov-Smirnov or a Chi-square goodness-of-fit test could be used. Then I looked up ?kolmogorov and found "ks.test", but the example there is for the normal distribution and I am not sure how to adapt it for the uniform distribution. I...

On 24.10.2016 21:54, Mehdi Amini wrote: >> On Oct 24, 2016, at 12:38 PM, Nicolai Hähnle via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> Some brain-storming on an issue with SPMD/SIMT backend support where I think some additional IR attributes would be useful. Sorry for the somewhat long mail; the short version of my current thinking is that I would like to have the following:

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Hi, I am looking for a way to generate a matrix of random numbers in a way that each row of the matrix would sum to 1 and that the numbers in the columns of the matrix would have a uniform distribution. So far I have found several ways to create random numbers that would sum to 1, but then the distribution of the individual elements is more or less skewed - there are much more small numbers than

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

Hello, I would like to uniformly distribute values from 0 to 200. Can someone help me find the appropriate uniform distribution generator? I would like to thank you in advance for your help. Best Regards Alex [[alternative HTML version deleted]]

...te two random variables (x1,x2) both with uniform distribution bounded by (-1) and (1) that has a correlation of 0.6 between them. Does somebody know how I can do it? For normal random variables I known how to implement it with the rmvnorm command but I don't know how to do it with variables uniformly distributed. Thanks a lot. Alexandra [[alternative HTML version deleted]]

...3, 4) Parameter 3: uniform(5, 10) require(lhs) N <- 1000 x <- randomLHS(N, 3) y <- x y[,1] <- qnorm(x[,1], 1, 2) y[,2] <- qnorm(x[,2], 3, 4) y[,3] <- qunif(x[,3], 5, 10) par(mfrow=c(2,2)) apply(x, 2, hist) par(mfrow=c(2,2)) apply(y, 2, hist) However, some of my parameters are uniformly distributed integer values and/or uniformly distributed classes. So, for example one input parameter can be "yellow", "green", "red" with equal probability. Of course these attributes can be transformed into integers (1,2,3) with a uniform distribution. So far I'v...

Guys, Is there some uniform way to find data dependence between two (sets of) instructions? There is use/def data, and there is AliasAnalysis information, but I am looking for something more high-level and uniform. I guess I am missing something, because such interface seems to exist once and then was abandoned. Any ideas? Victor -- IBM Research Lab in Haifa, Israel

Hello all, I am trying to do a Latin Hypercube Sampling (LHS) to a 5-parameter design matrix. I start as follows: library(lhs) p1<-randomLHS(1000, 5) If I check the distribution of each parameter (column), they are perfectly uniformly distributed (as expected).For example, hist(p1[,1]) Now the hard (maybe strange) question. I want the combination of the first three parameters to sum up to 1 (which obviously do not) s<-p1[,1]+p1[,2]+p1[,3] s==1 It occurred to me to divide each of these parameters with the sum (vector &quo...

Hi Escha, Great to have your comment! Do you have any specific reason for not doing like this? I am not sure whether I understand your point correctly. For "just model one thread", do you mean "only considering ONE of the 8/16 working lanes that running in lock-step way"?? For my case, may be something like I only need to define r0~r127 as register for i32 register (each r#

Full_Name: Hsiu-Khuern Tang Version: 2.0.0 OS: Debian GNU/Linux Submission from: (NULL) (156.153.255.236) Hi all, If fit is an rpart object, plot(fit, uniform=TRUE) ignores uniform=TRUE if the graphics device is not already open. To reproduce this: library("rpart") example(plot.rpart) dev.off() # <- works OK without this plot(fit, uniform=TRUE) # <- uniform=TRUE is ignored

Hello all!!! 1) I am wondering is there a way to generate random numbers in R for Bivariate Uniform distribution? 2) Does R have  built-in function for generating random numbers for any given bivariate distribution. Any help would be greatly appreciated !! Good day! Haneef Anver [[alternative HTML version deleted]]

When `length( skewed.probs ) > 200' uniform samples are generated in R-devel. R-3.5.1 behaves as expected. `epsilon` can be a lot bigger than illustrated and still the uniform distribution is produced. Chuck > set.seed(123) > > epsilon <- 1e-10 > > ## uniform to 200 then small > p200 <- prop.table( rep( c(1, epsilon), c(200, 999-200))) > ## uniform to 201

> Is there some uniform way to find data dependence between two (sets of) > instructions? There is use/def data, and there is AliasAnalysis > information, but I am looking for something more high-level and uniform. > I guess I am missing then was abandoned. Any ideas? Nope, there currently isn't a uniform interface for doing so, at least not at the LLVM level. As you said,

I wish to generate 100 by 1 vector of x1 and x2 both are uniform distributed with covariance matrix \Sigma. Thanks, Michael [[alternative HTML version deleted]]

I'm a beginner of R. I can use runif() to generate uniformly distributed numbers, but I don't know which function can generate uniformly distributed random integers, or what kind of method do? Thanks! -- View this message in context: http://www.nabble.com/how-to-generate-uniformly-distributed-random-integers-tf4960778.html#a14208376 Sent from the R h...