similar to: How do we obtain Posterior Predictive (Bayesian) P-values in R (a sking a second time)

Dear friends, Over the past weeks, I have been asking a lot of questions about how to use R in Bayesian analysis. I am brand new to R, but I am very pleased with it. I started with winbugs but I found winbugs to be a limited software, not bad but has several limitations. By contrast, R allows the analyst to tackle any problem with a huge set of tools for any kind of analysis. I love R. In

I have been running a Gibbs Sampler to estimate levels of efficiency in the Louisiana Shrimp Industry. I created a matrix (samp) where I stored the results of each iteration for 86 variables. I run 10,000 iterations. So, the matrix samp is 10,000 x 86. I want to use the gelman-rubin test to check for convergence. To do that, I need at least two chains. If I run second chain with different starting

I was trying to generate random numbers with a gamma distribution. In R the function is: rgamma(n, shape, rate = 1, scale = 1/rate). My question is that if X~gamma(alpha, beta) and I want to generate one random number where do I plug alpha and beta in rgamma? and, what is the meaning and use of rate? Thanks for your attention, Jorge [[alternative HTML version deleted]]

Dear friends, I run the Gelman-Rubin Convergence test for a MCMC object I have and I got the following result Multivariate psrf 1.07+0i, What does this mean? I guess (if I am not mistaken) that I should get a psrf close to 1.00 but what is 1.07+0i? Is that convergence or something else? Jorge [[alternative HTML version deleted]]

IHi all, have a dataset with rows as plots and environmental data as columns. I have predicted the values using the following ed.pred<-predict(lda.ed,ed) #lda.ed the model, ed the env. variables used for the prediction plots I am wanting to know the formula used by predict.lda for calculating the posterior probabilities. Can anyone point me in the right direction? Thanks

Dear All, This is a question somewhat off-topic. Say, if I have known the number of components in the mixture, all the estimated parameters, prior probabilities, and so on for a finite mixture model, how might I compute the posterior probabilities of each case for a new dataset without observed response (Y)? I want to know the parametric form of such calculation such that I can calculate it

With predict.lda the posterior probabilities only relate to the existing Class definitions. This is fine for Class definitions like gender but it is a problem when new data does not necessarily belong to an existing Class. Is there a classification method that gives posterior probabilities for Class membership and does not assume the new data must belong to one of the existing Classes? A new

Hello, I'm doing bayesian network analyses with the deal package. I am at a loss for how to interpret output from the analysis (i.e. what is a good score, what is a bad score, which stats tell me what about the network edges/nodes). Here is an example node with its posterior scores for all parent nodes. ------------------------------------------------------------ Conditional Posterior:

Hi all, I did some analysis using package R2WinBUGS to call WinBUGS. I set the iterations to 50000 (fairly a large number, I think), but after the program was done, the effective posterior samples contained only 7 draws. I don't know why. By the way, I checked posterior sample size by using bugsobj$n.sims. And, for my previous practice with WinBUGS/R2WinBUGS, no such strange thing happend.

Hi all, I used both OpenBugs and R function bugs{R2WinBUGS} to run a linear mixed effects model based on the same data set and initial values. I got the same summary statistics but different posterior samples. However, if I order these two sets of samples, one is generated from OpenBugs and the other is generated from R, they turn to be the same. And the samples from R do not have any

Dear R useRs, i try to compute the posterior mean for the parameters omega and beta for the following posterior density. I have simulated data where i know that the true values of omega=12 and beta=0.01. With the function postMeanOmega and postMeanBeta i wanted to compute the mean values of omega and beta by numerical integration, but instead of omega=12 and beta=0.01 i get omega=11.49574 and

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

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

Dear Friends, I am trying to use the gelman-rubin convergence test. I generated a matrix samp[10,000x86] with the gibbs sampler. the test requires the creation of "mcmc" objects. Since I don't know how to define samp as a "mcmc" object, I tried to create one mcmc object by means of the mcmc() function. With this function I tried to create a mcmc object dul from samp but I

Hi All, Just thought you might be interested in a recently released R package, spm: Spatial Predictive Modelling. It aims to introduce some novel, accurate, hybrid geostatistical and machine learning methods for spatial predictive modelling. Of 22 functions available in spm, two functions are for accuracy assessment. Perhaps they are not only useful tools for spatial predictive modelling

I realize that the following has been talked about on this list many times before in some related way but I am going to ask for help anyway because I still don't know what to do. Suppose I have no intercept models such as the following : Y = B*X_1 + error Y = B*X_2 + error Y = B*X_3 + error Y = B*X_4 + error and I run regressions on each ( over the same sample of Y ) and now I want to

Dear list members, I know that there is the arules package with the implementation of the apriori algorithm. However i want to use the "predictive apriori" instead. These algorithm can mine as rules as i want and there is an implementation on weka. There is some implementation on R? -- Att, Flávio Barros [[alternative HTML version deleted]]

I have an urgent need for assistance with a complex predictive modelling project - preliminary discussion with numerous statisticians suggests that off-the-shelf packages (including existing R libraries) are unlikely to provide a complete solution to the particular problem that we're tackling. I'm looking for a statistics consultant to help with a short (less than 4 weeks) project. The

Hello, Currently I am using R for building a logistic model using numerical and nominal data as predictors. Before doing the regression, the predictors are grouped. The groups I determine manually by trying to maximize the information value (which is an indicator for the discriminatory power of the variable) under the condition that enough data are within each group (approx. 5%). Is there a

Hi, I wanted to make sure you were all aware of these upcoming events. There is a seminar in Predictive Analytics on Oct. 24-25 in DC, and in San Francisco Nov 6-7. This is intensive training for managers, marketers, and IT people who need to make sense of customer data to predict buying behavior, profit, etc. Past attendees have given rave reviews. You can find more info at