similar to: Polya distribution

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

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

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

Hi all, I'm working with some data: 54 variables and a column of classes, each observation as one of a possible seven different classes: > var.can3<-lda(x=dados[,c(1:28,30:54)],grouping=dados[,55],CV=TRUE) Warning message: In lda.default(x, grouping, ...) : variables are collinear > summary(var.can3) Length Class Mode class 30000 factor numeric ### why?? I

I have a problem in where i generate m independent draws from a binomial distribution, say draw1 = rbinom( m , size.a, prob.a ) then I need to use each draw to generate a beta distribution. So, like using a beta prior, binomial likelihood, and obtain beta posterior, m many times. I have not found out a way to vectorize draws from a beta distribution, so I have an explicit for loop

Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

Workshop on Bayesian methods and WinBUGS *************************************** A two-day workshop on Bayesian methods is being held on Friday 3 - Saturday 4 February 2012 at the University of Sydney. This course is suitable for graduate students, academics, researchers and professionals who wish to introduce themselves in the application of Bayesian methods and the use of WinBUGS software.

I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :) Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but

Hi there, I have two questions and believe that there is an extremely easy solution. Being a beginner with R makes thinks a bit more complicated. This is the code: rpois(15,3) n<-15 DATA<-cbind(D,rpois(15,3)) data<-as.data.frame(DATA) colnames(data)<-c("D","X") *# 1. question: is it possible to put the following creation of x in a nicer form?*

This question is slightly off topic, but I'll use R to try and make it as relevant as possible. I'm working on a problem where I want to compare estimates from a posterior distribution with a uniform prior with those obtained from a frequentist approach. Under these conditions the estimates should agree. Specifically, I am asking the question, "What is the probability that the true

Hello everybody, In order to predict income for different time points, I fitted a linear model with polynomial effects using BMA (bicreg(...)). It works fine, the results are consistent with what we are looking for. Now, we would like to predict income for a future time point t_next and of course draw the prediction interval around the estimated value for this point t_next. I've found the

Dear All,   thanks to Berend, my question posted yesturday was solved succesfully here: http://r.789695.n4.nabble.com/hep-on-arithmetic-covariance-conversion-to-log-covariance-td4646068.html . I posted the question with the assumption of using the results with rlnorm.rplus() from compositions. Unfortunatelly, I am not getting reasonable enough outcome. Am I applying the results wrongfully? The

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

Hi, I am trying to recreate the right graph on page 524 of Gelman's 2006 paper "Prior distributions for variance parameters in hierarchical models" in Bayesian Analysis, 3, 515-533. I am only interested, however, in recreating the portion of the graph for the overlain prior density for the half-Cauchy with scale 25 and not the posterior distribution. However, when I try:

I have summary statistics from many sets (10,000's) of near-normal continuous data. From previously generated QQplots of these data I can visually see that most of them are normal with a few which are not normal. I have the raw data for a few (700) of these sets. I have applied several tests of normality, skew, and kurtosis to these sets to see which test might yield a parameter which

Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

Dear R-useRs, I've written a contribute (in Italian language) concering fitting distribution with R. I believe it could be usefull for someones. It's available on CRAN web-site: http://cran.r-project.org/doc/contrib/Ricci-distribuzioni.pdf Here's the abstract: This paper deals with distribution fitting using R environment for statistical computing. It treats briefly some

On 26/12/2019 11:14 p.m., Spencer Graves wrote: > Hello, All: > > > ????? The default "simulate" method for lm and glm seems to ignore the > sampling variance of the parameter estimates;? see the trivial lm and > glm examples below.? Both these examples estimate a mean with formula = > x~1.? In both cases, the variance of the estimated mean is 1. That's how