similar to: DPpackage help: DPdensity

Dear All, I've had trouble compiling DPpackage as a user in one system. It works fine as root in other machines. I can see any clues in error messages My guess is that it is a permissions matter. Any help is appreciated. OS: Linux Kernel: 2.6.27 SMP Arch: Intel 64 bits gfortran not available Thank you. ----------------------><8------------------------------------- g77 ? -fpic ?-g

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List, I am trying to estimate a 3 dimensional density through the DPpackage. For example # model sigma <- matrix(c(0.1,0.05,0.05,0.05,0.1,0.05,0.05,0.05,0.1), ncol=3) rnormm<- rmvnorm(n=100, mean=c(5,100,150), sigma=sigma) sigma2 <- matrix(c(10,0.05,0.05,0.05,10,0.05,0.05,0.05,10), ncol=3) rnormm2<- rmvnorm(n=100, mean=c(20,1,110), sigma=sigma) rnormm<-rbind(rnormm,rnormm2)

Hi to everyone, I'm a PhD student and I'm involved in non parametric analyses of hierarchical models. I tried to use package DPpackage on my data, but I encountered some problems in interpreting ouputs. Can anybody help me? The problem can be remued as follows: I have a logit hierarchical model for survival (i.e. binary response) in patients affected by heart failure (the court

Dear List, I am pleased to announce the release of version 1.0.0 of DPpackage on CRAN. DPpackage covers some important models using Dirichlet process priors. The package includes: Semiparametric Bernoulli regression Semiparametric Density estimation Semiparametric Linear mixed models Semiparametric Generalized linear mixed models Semiparametric AFT model for interval-censored data I

Dear R users, Thanks in advance. I am Deb, Statistician at NSW Department of Commerce, Sydney. I am using R 2.8.1 on Windows XP. This has reference to the package “DPpackage”. The binary version is available on Mac OS, but I am using Windows XP. May I request you to assist me in the followings: How can I prepare Windows binary of DPpackage from source? 2. Is there any possibility

I fitted a gaussian mixture to my financial data. The data can be found here: http://uploadeasy.net/upload/32xzq.rar I look at the density with plot(density(dat),col="red",lwd=2) this has a skew of library(e1071) skewness(dat) -0.1284311 Now, I fit a gaussian mixture according to: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) with:

Hi, I am trying to learn lognormal mixture models with EM. I was wondering how does one compute the log likelihood. The current implementation I have is as follows, which perform really bad in learning the mixture models. __BEGIN__ # compute probably density of lognormal. dens <- function(lambda, theta, k){ temp<-NULL meanl=theta[1:k] sdl=theta[(k+1):(2*k)]

Hi list, After a lot of tweaking i have managed to create a histogram with an overlaying density plot. The histogram shows a sample of birth weights of babies and the density plot shows birth weights from a much larger reference populaton. My data is divided in 0.1 Kg bins so in the code below binweigh=0.1. The trouble with the current graph is that it is not very clear since the density plot

Merry Xmas to all, I am writing a function and curiously this runs sometimes on one data set and fails on another and i cannot figure out why. Any help much appreciated. If i run the code below with data <- iris[ ,1:4] The code runs fine, but if i run on a large dataset i get the following error (showing data structures as matrix is large) > str(cluster.data) num [1:9985, 1:811] 0 0 0 0

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with

Hi all, I have been looking for means of add a contour around some points in a scatterplot as a means of representing the center of density for of the data. I'm imagining something like a 95% confidence estimate drawn around the data. So far I have found some code for drawing polygons around the data. These look nice, but in some cases the polygons are strongly influenced by outlying points.

Dear R-list, I have a statistical problem with the comparison of two short time-series of density data in an ecological framework. I have to compare two short time series (5 years, one value for each year) of species density data (it is the density of fish in two different streams) to test if the two means of the five densities are significantly different, so basically if the two mean

Hello, I am doing mcmc=10000 simulations from a posterior distribution of the parameters of a mixture of K=6 normal densities. I have mcmc by K matrices simMeans, simVars and simWeights containing the simulation output: one row for each simulation, one column for each normal component of the mixture. One thing I would like to do is a plot of the posterior predictive density. In order to do that

Hi,all with my data,there are more than 1000 quantitative results of antibody concentrations, there may be 2 components(positive and negative), or 3 components (may be strong positive, positive, and negative), or 4-6 components. Could you tell me how to determine the number of components of the mixture model? the "anova.mix" in mixdist of the R software seems not work. my

The new package "benchden" 1.0.0 implements 28 benchmark densities for nonparametric density estimation that were introduced by A. Berlinet and L. Devroye ("A Comparison of Kernel Density Estimates", Pub. Inst. Stat. Univ. Paris, XXXVIII, fasc. 3, 1994, 3-59, http://cg.scs.carleton.ca/~luc/devs.html ). This collection includes a variety of densities with different degrees of

The new package "benchden" 1.0.0 implements 28 benchmark densities for nonparametric density estimation that were introduced by A. Berlinet and L. Devroye ("A Comparison of Kernel Density Estimates", Pub. Inst. Stat. Univ. Paris, XXXVIII, fasc. 3, 1994, 3-59, http://cg.scs.carleton.ca/~luc/devs.html ). This collection includes a variety of densities with different degrees of