similar to: Dirichlet kernel requires r to be defined, which is not in the help

Dear list members, I want to draw surfaces of Dirichlet distributions with different parameter settings. My code is the following: #<begin code> a1 <- a2 <- a3 <- 2 #a2 <- .5 #a3 <- .5 x1 <- x2 <- seq(0.01, .99, by=.01) f <- function(x1, x2){ term1 <- gamma(a1+a2+a3)/(gamma(a1)*gamma(a2)*gamma(a3)) term2 <- x1^(a1-1)*x2^(a2-1)*(1-x1-x2)^(a3-1)

I'm just getting started with R, having a lot of original work on modeling and exploring the simulation by MCMC. I want to simulate the prior and the posterior distribution of Dirichlet Process by MCMC. Is there anyone in NYC that might be a good tutor for me? -- Dr. P. NGOM, Facult? des Sciences et Techniques D?partement de Math?matiques et Informatique Universit? Cheikh Anta Diop Dakar -

Dear Gregory R. Warnes, I'm a phd student in statistics, at the University of Milano Bicocca. I'm interested to the methods of estimate the parameters of the Dirichlet distribution. Do you have implemented an algorithm in R? If so, can you give me the script? or, in general, some helps? Best Regards Gianna --------------------------------------------------------------- Gianna Monti

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

I am trying to use the "kernel" function. To understand how it works I tried out some of the examples. None of them works as shown in the following: > kernel("daniell", 50) # Error in kernel("daniell", 50) : unused argument(s) (50) > kernel("daniell", 10) # Error in kernel("daniell", 10) : unused argument(s) (10) >

My goal is to remove signals trend without any a-priori knowledge of the trend type, if any. Some signals are very noisy and non-stationary (example attached). I have experimented with a number of techniques. Staring at the results, I can hardly tell which method is best. I am attaching the result of function "local.trend" as I cannot understand it. Nor I can make a sense of the returned

There's Renka's STRIPACK, and TRIPACK, respectively, ACM TOMS Algorithms 772 and 751, and there's the R package "deldir" which does the Delaunay for a plane, but does anyone have or know of the tessellation in R for a sphere? Also, is there a standard indexing scheme for Delaunay facets, and perhaps of edges in such facets? I'd expect that to be a publication reference,

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

Hola comunidad, Les consulto mi duda. Quisiera generar N números aleatorios tal que su suma esté predeterminada a un valor k. Sé que con la distribución Dirichlet, por ejemplo, se podrían obtener números aleatorios tal que su suma es la unidad. Por ejemplo MCMCpack::rdirichlet(1,rep(10,7)) genera 7 números tal que su suma es 1 (el parámetro rep(10,7) es cualquier cosa). Pero me pregunto si se

Okay, been working with tripack, seems the most mature package for this. Got it to work well with their test data set - data(tritest). When i tried random numbers to explore further, i am getting some results that don't reconcile. example run this: library(tripack) y <- runif(100) x <- runif(100) vm <- voronoi.mosaic(x,y) plot(vm) par(new=T) plot(x,y,col='blue') when

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

Dear R-users, Is there an R function to compute the multinomial beta function? That is, the normalizing constant that arises in a Dirichlet distribution. For example, with three parameters the beta function is Beta(n1,n2,n2) = Gamma(n1)*Gamma(n2)*Gamma(n3)/Gamma(n1+n2+n3) Thanks in advance for any assisstance. Regards, Greg [[alternative HTML version deleted]]

Hi Thanks for replying my question! What really interested me is that the package providing some complex form sampling, such as wishart, multinomial, dirichlet. And others for example conditional beta distribution confining the random variable in the interval (a, b). Since these concept are widely used in the baysian, I wonder whether somebody has already written this package. Thanks! Best

Dear R-users, Does anyone know how to do weighted Voronoi diagrams (Dirichlet tesselation) in R? To be more specific, I have a set of coordinates for tree locations on a plot, and I'm looking for a way to do the tesselation so that the polygon size for each tree depends on the size of the subject tree, and the size of its neighbors. So, the location of the bisection between two trees

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

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:

I evaluated the Bayes factor in the k=2 allele case with a "triangular" prior under the null as in the example in the help file: HWETriangBF2(nvec=c(88,10,2)) [1] 0.4580336 When I swap the n11 entry and n22 entry of nvec, I received totally different Bayes factor: > > HWETriangBF2(nvec=c(2,10,88)) [1] 5.710153 > In my understanding, defining the genotype frequency as

I am a pretty new R user, I am running the latest linux version on xandros, updated with some extra debian packages, and I also run the latest windows version, but prefer linux. I am having trouble downloading "bayesm", it won't do it all from any of the sites on the web, I resorted to this one, http://packages.debian.org/unstable/math/r-cran-bayesm. and got slightly further, but I

I try to do a test for dirichlet process for Multivariate normal, but Winbugs always says "expected multivariate node", does that mean I miss something at initialization? I will really appreciate the help to solve this problem Here is the R code, and Winbugs code. model { for(i in 1:N){ y[i,1:2] ~ dmnorm(mu[i,],tau[i,,]) S[i] ~ dcat(pi[]) mu[i,1:2] <- mu.star[S[i],]

I do the these passages: library(Geneland) set.seed(1) data <- simdata(nindiv=200, coord.lim=c(0,1,0,1) , number.nuclei=5 , allele.numbers=rep(10,20), IBD=FALSE, npop=2, give.tess.grid=FALSE) geno <- data$genotypes coord <- t(data$coord.indiv) path.mcmc <-