similar to: Difference in mixture normals and one density

Hi, R-listers I tried to plot several graphs in a sigle x-y coordinate settings, like the following: |(y) s | ****** s | ***** s | sssssssssssssssssss |_______________________________(x) where "*" and "s" denote two diffrent plots. However, when I used plot(data1); % data1 is the data points of "*"

Dear R Users?? I woould like to generate mixture model data. I try two type method as followings two method, can anyone tell me which is right? or better? 1. generate two vectors data y1 and y2 from f1 and f2 seperately, and calculate y=alpha1*y1+alpha2*y2, 2. generate prob=unif(1), if prob <alpha1 y=y1, else y=y2. Can anyone give me some idea? Thanks so much! Goeland goeland at

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

I have been authoring a very small R package on CRAN, named "normix" which implements an S3 class "norMix" has plot and print methods; further, E[X] and Var[X] methods, random number generation ("r") and density evaluation. It also provides the 16 "Marron-Wand densities" (known in the (1d) density estimation business). Erik J?rgensen has provided

I've had a need for multinomial "random number generation" occasionally. And other people too. The following code is currently in the (very small ``not very high importance'') CRAN package normix --- which I will rename to "nor1mix" very seen because of a ``name registration'' problem I want to add "this" (well the functionality) to a

Can anyone send me the R code for fitting a mixture of normal distributions. Or in fact any'combination' of mixtures of distributions. I looke dta mix but it is still not clear to me. Jim [[alternative HTML version deleted]]

I am doing a histogram with 2 superimposed densities. However, the density of one of the graphs is not coming out..its being erased.. Any ideas on how to fix this problem? -- Thanks, Jim. [[alternative HTML version deleted]]

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

hi i'm looking for a gibbs sampling algorithm for R for the case of mixture of K normals, and in particular for the case of bivariate normals. i'd be grateful if anyone could send its own R-routine, at least for the univariate case. thank you in advance matteo

Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k

All: I am trying to fit a mixture of 2 normals with > 110 million observations. I am running R 2.5.1 on a box with 1gb RAM running 32-bit windows and I continue to run out of memory. Does anyone have any suggestions. Thanks so much, Tim [[alternative HTML version deleted]]

Suupose I have y <- rbeta(10000, 2, 5) and I only want to see only the density plot from x = 0 to x = 1 How do I do this? -- Thanks, Jim. [[alternative HTML version deleted]]

Hi, I would like to know if it is possible to have a "R code" to estimate the parameters of a mixture of bivariate (or multivariate) normals via EM Algorithm. I tried to write it, but in the estimation of the matrix of variance and covariance, i have some problems. I generate two bidimensional vectors both from different distribution with their own vector means and variance and

I couldn't find a direct answer in CRAN to this question, so I'm asking with some trepidation. I have a multivariate dataset (data.frame) with columns that can be expressed as a set of mixed normals (at least I think) and need to impute values that have constraints (truncated mixture of normals where the values cannot be below zero). If there isn't a package that can do this, is there

Hello all, I have two columns of numbers. I would like to do the following: (1) Plot both cdfs, F1 and F2 on the same graph. (2) Find smoothed approximations of F1 and F2 lets call them F1hat and F2hat (3) Find values for F1hat when we substitue a value of x in it. (4) Find the corresponding densities of the cdfs. Any ideas? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear All, I wonder if there is an R package to estimate the generalized linear mixed models but with a random effects having a mixture of normals as a prior distributinon .. Thank you, Abderrahim [[alternative HTML version deleted]]

Dear Friends, Is there an R package which implements regression models with error distributions following a scale mixture of normals? Thanks Anup

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I'm having trouble with the standard deviation distribution as shown on http://mathworld.wolfram.com/StandardDeviationDistribution.html . (Eric Weisstein references Kenney and Keeping 1951, which I can't check.) I believe the graphs they show, but when I code the function in R, according to the listed formula, I get very different graphs. Would someone please point out my error or tell

Hi, I??m trying to use the Win2BUGS package from R and I have a similar problem that reurns with the message: Error in FUN(X[[1]], ...) : symbol print-name too long But, there is no stray ` character in the file ( Sugestions given by: Duncan Temple Lang <duncan> Date: Mon, 26 Sep 2005 07:31:08 -0700 ) The progam in R is: library(R2WinBUGS) library(rbugs) dat <-