similar to: sampling from Laplace-Normal

Dear All, Here is what I am trying to achieve: I would like to plot some data in 3D. Usually, one has a matrix of the kind y_1(x_1) , y_1(x_2).....y_1(x_i) y_2(x_1) , y_2(x_2).....y_2(x_i) ........................................... y_n(x_1) , y_n(x_2)......y_n(x_i) where e.g. y_2(x_1) is the value of y at time 2 at point x_1 (see that the grid in x is the same for the y values at all times).

Well, I could argue that it's not *completely* OT since my question is motivated by an enquiry that I received in respect of a CRAN package "Iso" that I wrote and maintain. The question is this: Given observations y_1, ..., y_n, what is the solution to the problem: minimise \sum_{i=1}^n (y_i - y_i^*)^2 with respect to y_1^*, ..., y_n^* subject to the "isotonic"

Hi, This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

Hello, I have to compute a multiple summation (not an integration because the independent variables a are discrete) for all the values of a function of several variables f (x_1,...,x_n), that is sum ... sum f(x_1,...,x_n) x_1 x_n have you some suggestion? Is it possible? I know that for multiple integration there is the function adapt, but it has at most n=20. In my case n depends on the

Hi everyone, Can anyone tell me if its possibility to extract the coefficients from the lm() command? For instance, imagine that we have the following data set (the number of observations for each company is actually larger than the one showed...): Company Y X1 X2 1 y_1 x1_1 x2_1 1 y_2 x1_2 x2_2 1 y_3 x1_3 x2_3 (...) 2 y_4 x1_4 x2_4 2 y_5 x1_5 x2_5 2 y_6 x1_6 x2_6 (...) n y_n x1_n x2_n n

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

>>>>> jing hua zhao >>>>> on Mon, 24 Jun 2019 08:51:43 +0000 writes: > Hi All, > Thanks for all your comments which allows me to appreciate more of these in Python and R. > I just came across the matrixStats package, > ## EXAMPLE #1 > lx <- c(1000.01, 1000.02) > y0 <- log(sum(exp(lx))) > print(y0) ## Inf

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

On Thu, Aug 14, 2014 at 6:37 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > On Wed, Aug 13, 2014 at 8:35 PM, Jeremy Salwen <jeremysalwen at gmail.com> wrote: >> Hey Daniel, >> >> Thanks again for the help. I'm still a bit confused about the interface to >> the alias analysis. It seems like we are talking about different >> interfaces. >

Hi Hadley, Thanks for the counterpoint. Response below. On Thu, May 16, 2019 at 1:59 PM Hadley Wickham <h.wickham at gmail.com> wrote: > The existing behaviour seems inutitive to me. I would consider these > invariants for n vector x_i's each with size m: > > * nrow(rbind(x_1, x_2, ..., x_n)) equals n > Personally, no I wouldn't. I would consider m==0 a degenerate

Hi Daniel, Sorry for taking so long to respond. I spoke with a colleague more familiar with llvm who thought he could clear up my confusion, but we both came out of the conversation confused. I will try my best to explain the ambiguity. In an DAG, alias queries would be completely unambiguous. Every instruction would only be executed once, and every SSA value really would have a single static

Hi Hal, Thank you for your email, that makes a lot of sense to me. I am working on some tools to use memory profiling to speculatively replace memory loads and stores with value forwarding in hardware implementations. I'd like to compare the profiled data to static alias analysis, so it would be super useful if there was a way to answer the questions about aliasing across backedges that

>>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: >>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: > include/Rmath.h declares a set of 'logspace' functions for use at the C > level. I don't think there are core R functions that call

Dear All, Suppose I have a vector X = (x_1, x_2, ...., x_n), X_sort = sort(X) = (x_(1), x_(2), ... , x(n) ), and I would like to know the original position of these ordered x_(i) in X, how can I do it? case 1: all values are unique x <- c( 3, 5, 4, 6) x.sort <- sort(x) # # I would like to obtain a vector (1, 3, 2, 4) which indicates that 3 in x is still the 1st element in x.sort, 5 is at

Hi all, Apologies if this has been asked before (a quick google didn't find it for me),and I know this is a case of behaving as documented but its so unintuitive (to me at least) that I figured I'd bring it up here anyway. I figure its probably going to not be changed, but I'm happy to submit a patch if this is something R-core feels can/should change. So I recently got bitten by

On Fri, 7 Feb 2014, Timothy B. Terriberry wrote: > Martin Storsjo wrote: >> This is required in order to build using the built-in assembler >> in clang. > > These patches break the gcc build (with "Error: bad instruction"). Ah, right, sorry about that. > Documentation I've seen is contradictory on which order ({cond}{size} or > {size}{cond}) is correct.

If I use the Ineq library and the Gini function in this way: >Gini(c(100,0,0,0)) I obtain the result 0.75 instead of 1 (that is the perfect inequality). I think Gini's formula in Ineq is based on a formula as reported here: http://mathworld.wolfram.com/GiniCoefficient.html but in the case of perfect inequality: x_1=.......=x_n-1 =0 x_n>0 these formula are equal to 1 - 1/n, not to

Hello! I would like to get MLE for parameter lambda of Poisson distribution. I can use fitdistr() for this. After looking a bit into the code of this function I can see that value for lambda and its standard error is estimated via estimate <- mean(x) sds <- sqrt(estimate/n) Is this MLE? With my poor math/stat knowledge I thought that MLE for Poisson parameter is (in mixture of LaTeX