search for: uniformity

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 uniform distributed library(ecodist) x <- runif(10^5) y

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

...of a better name...): a call to a function with this attribute cannot be moved to have _fewer_ control dependencies; i.e., moving it from A to B is only possible if A dominates or post-dominates B. 3) uniform (for function arguments): transformations are not allowed to introduce additional non-uniformity in this argument. I'd appreciate input on this proposal, e.g. if this can be solved in an easier way or if there are obvious problems with this approach. Thanks, Nicolai .......... In a nutshell, the problem that this intends to solve is that: %v1 = texelFetch(%sampler, %coord0) %v2...

Hello, 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

...ame...): a call to a function with this attribute cannot be moved to have _fewer_ control dependencies; i.e., moving it from A to B is only possible if A dominates or post-dominates B. >> >> 3) uniform (for function arguments): transformations are not allowed to introduce additional non-uniformity in this argument. > > Can you describe it in terms that are non-SPMD/SIMT? > I.e. I’m not sure that “uniformity” refers to an existing LLVM IR concept. Yeah, that's actually the key problem I've been struggling with. The first example I sent shows the gist of it. It also shows th...

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]]

Hello, I want to create 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

Hi, I'd like to use Latin Hypercube Sampling (LHC) in the the context of uncertainty / sensitivity analysis of a complex model with approximately 10 input variables. With the LHC approach I'd like to generate parameter combinations for my model input variables. Therefore I came across an simple example here on the mailing list (

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

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 help mailing list archive at