similar to: generate random numbers that sum up to 1

Hi I have two arbitrarily dimensioned arrays, "a" and "b", with length(dim(a))==length(dim(b)). I want to form a sort of "corner-to-corner" version of abind(), or a multidimensional version of blockdiag(). In the case of matrices, the function is easy to write and if a=matrix(1,3,4) and b=matrix(2,2,2), then adiag(a,b) would return: [,1] [,2] [,3] [,4] [,5]

Dear R experts, I was wondering if there are any R functions that give the tail area of a sum of chisquare distributions of the type: a_1 X_1 + a_2 X_2 where a_1 and a_2 are constants and X_1 and X_2 are independent chi-square variables with different degrees of freedom. Thanks, Klaus -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ...

Hi all, I have n independent variables A_1, A_2, A_3,......,A_n, and each with known variances var(A_1), var(A_2),..., but unknown mean. How can I get the approximation of the variance of the product of the variables using numerical computation, i.e. var(A_1*A_2*A_3*.....*A_n)? Thanks. Sincerely, Yanwei Zhang Department of Actuarial Research and Modeling Munich Re America Tel: 609-275-2176

Dear All, I've been searching relevant topics about replacing values, none seemed to be applicable to me... I have a file with many many varieties, and want to replace some of them into different names. I tried various of ways, still don't know how to do that most efficiently.. Here is part of the example data: Gen Rep A_1 1 A_1 2 A_2 1 A_2 2 B_1 1 B_1

Hello, I am looking into description of surf.ls(spatial) and see under value $beta - the coefficients. When I use polynomial of degree 2 to fit surface I expect to get 4 coefficients: z = a_1 x^2 + a_2 xy + a_3 y^2 + a_4 What do beta really stand for and why do I get $beta vector of length 6? Thakns, Mark

Hello The excellent adapt package integrates over multi-dimensional hypercubes. I want to integrate over a multidimensional simplex. Has anyone implemented such a thing in R? I can transform an n-simplex to a hyperrectangle but the Jacobian is a rapidly-varying (and very lopsided) function and this is making adapt() slow. [ A \dfn{simplex} is an n-dimensional analogue of a triangle or

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Hello to all, I am having trouble with intregrating a complicated uni-dimensional function of the following form Phi(x-a_1)*Phi(x-a_2)*...*Phi(x-a_{n-1})*phi(x-a_n). Here n is about 5000, Phi is the cumulative distribution function of standard normal, phi is the density function of standard normal, and x ranges over (-infty,infty). My idea is to to use quadrature to handle this integral. But

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

Dear All, I have six different campaign and 5 different agent have login on that campaign.*Same thing i have done using agi and database,i never use queue management on this scenario. Agent** can also shuffling one campaign to anther campaign. * Now i want to do some work with queue.I want to use single queue to managing this. Eg: campaign Agent Login A a_1,a_3

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Hi! I want to write portions of my data (3573 columns at a time) to twenty folders I have available titled "A_1" to "A_20" such that the first 3573 columns will go to folder A_1, next 3573 to folder A_2 and so on. This code below ensures that the data is written into all 20 folders, but only the last iteration of the loop (last 3573 columns) is being written into ALL of the

When you say that distribution shouldn't be used, do you mean within instcombine rather than some other pass? Or not all as an IR optimization? A dedicated optimization pass that looks for and makes factoring/distribution folds to eliminate instructions seems like it would solve the problems that I'm seeing. Ie, I'm leaning towards the proposal here: https://reviews.llvm.org/D41574

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA

Hello, I have just started using R, and have found it quite easy to make plots of probability densities for normal and beta distributions, etc. Now, I'm looking at trying to make a simplex plot of a Dirichlet density, but have gotten stuck and was hoping that someone could help. Ideally, the plot would look something like the 3d plots on Wikipedia here:

Dear list, Does anyone have a suggestion (or better still) code for sampling from the uniform distribution over the convex hull of a set of points? Many thanks and best wishes, Ranjan

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

> On May 8, 2018, at 9:50 AM, Daniel Berlin via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > 1. The reassociate pass that exists right now was *originally* (AFAIK) written to enable CSE/GVN to do better. Agreed. The original mindset included a (naive) belief that going with a canonical form was better than teaching redundancy elimination to handle abstractions (as a matter

> I asked myself the same question. Without mod, how do you ensure that for instance the expression 2*i+255 was not actually 2*i-1 ? I think it is not possible in general, but I believe it is possible in case of affine expressions used as GEP indices. I assume, GEP indices (except indexing into struct) are interpreted as signed integers. It isn't explicitly stated in the LangRef, but

>However, there is one issue I have ignored - possibility of overflow in >the index expression. Suppose, we have such a loop: > for (i8 i = 0; i != 200; ++i) { > A[2 * i + 5] = ... > ... = A[2 * i + 3] > } >If both index expressions are evaluated in 8-bit arithmetic, >then the dependence equation should be solved in modular arithmetic: > 2 * i + 5 == 2 * (i +