search for: posterior

...ecision (I add precision manually because I want it to be zero but if I include zero as prior variance I get an error cant divide by zero. Now my question is instead of having the three iterations below, is there anyway to create a loop, the only problem is I want to have elements from the previous posterior to be the new prior and now I cant figure out how to do the code below in a loop. The data below is dummy data, I used a starting mu of 1, and starting precision of 0. bayes.analysis.treat<-function(mu0,p0){ n1 = 5 n2 = 10 n3 = 15 ybar1 = 12 ybar2 = 13 ybar3 = 14 sd1 = 2 sd2 = 3 sd3...

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40, tune=1, verbose=0, logfun=T, optim.method="BFGS")...

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 assu...

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 proportion of students passing a test is 50% when the observed proportion for th...

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. Does anybody have some idea on what might be wrong with my model or whatever part in this...

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 inst...

...lue) in R from the chain I obtained from the Gibbs sampler? I have a matrix 'samp' [10,000x86] where I stored the result of each of the 10,000 iterations of the 86 variables of interest. Something I want to add is that Gelman also states that "...in practice, we usually compute the posterior predictive distribution using simulation. If we already have L simulations from the posterior density of theta, we just draw one y_rep from the predictive distribution for each simulated theta; we now have L draws from the joint posterior distribution, p(y_rep,theta|y). The posterior predictive che...

...ts, 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 posterior parameter values) However, I have also seen this as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Min(Deviance over the whole sampled posterior) Now, my understanding is that for some distributions, the deviance at the parame...

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 autocorrelation. I do not know why and how this R function destroy the orders of posterior samples. Have anyone...

...rted with the subsequent two functions to obtain random numbers for Lambda after the first observed period. My question is now, how to get the randoms for the n following periods? Thanks a lot for your hints! Maybe there is an easier way to do the necessary calculations...? Carsten # Function 1: Posterior for the first observation test.posterior=function(x,observation,p1,p2) { f1=function(x,observation,p1,p2) dpois(observation,qnorm(pnorm(x,p1,p2),p1,p2))*dnorm(x,p1,p2) integral=integrate(f1,0,Inf,p1=p1,p2=p2,observation=observation)$value ausgabe=f1(x,observation,p1=p1,p2=p2)/integral return(ausgab...

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

I have a variable that looks like this: > print(pred$posterior) o x 1 2.356964e-03 9.976430e-01 2 8.988153e-01 1.011847e-01 3 9.466137e-01 5.338627e-02 4 2.731429e-11 1.000000e+00 Now what I want to do is to access "o" and "x" How come this approach fail? > print(pred$posterior$o) or >...

I am attempting to use optim to solve a neural network problem. I would like to optimize coefficients that are currently stored in a matrix Y=270 x 1 X= 27- x 14 b1= 10x14 b2= 11x1 V= 10 x 14 set of prior variances. I have the following function: posterior.mode1=function(y,X,b_0,b2,V) { log.like=function(b1) { a_g=compute(b1) z_g=tanh(a_g); z_g=cbind(1,z_g) p=softmax(z_g%*%b2); a=sum(y*log(p)+(1-y)*log(1-p)); return(a); } compute=function(b1) { a_g=NULL; for(i in 1:nrow(b1)){ a_g=cbind(a_g,X%*%b1[i,])...

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: Yp1| 3 4 5 6 9 11 12 15 18 [[1]] [[1]]$tau [,1] [,2] [,3] [,4] [,5] [,6] [1,] 138.0000000 -201.944190 -61.827901 -29.5419149...

Dear R-users I am looking for a help in using the "deal" package. I followed the manual and paper from the author's web site to learn it, as shown below, but I could not figure out how to access the local and posterior probability of the nodes in the constructed network. library(deal) data(ksl) ksl.nw <- network(ksl) ksl.prior <- jointprior(ksl.nw) banlist <- matrix(c(5,5,6,6,7,7,9, 8,9,8,9,8,9,8), ncol = 2) ksl.nw$banlist <- banlist ksl.nw <- learn(ksl.nw, ksl, ksl.prio...

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model...

Hi. I asked a question about lda() and got some answers. However, one question remains (which is not independent of the earlier ones): What output does lda() produce which I can use to compute the posteriors? I know predict(lda())$posterior will give me precisely the posteriors, but suppose I'd like to compute them myself, outside of R. So far, I have not been able to use "coefficients of linear discrimiants" to do this, for they don't seem to be the alpha and beta in log(post...

...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 manually in excel or sql. Thank you so much! -- =============================== WenSui Liu Statistical Project Manager ChoicePoint Preci...

...ey for better visibility trellis.device(pdf,file="PostAll.pdf", width = 5, height = 5) trellis.par.set("plot.line", list(lwd=0.15)); top.left <- wireframe(post1, xlab=list("p(adj-N)", cex=.7, rot=30),ylab=list("p(num-N)", cex=.7, rot=-45),zlab=list("posterior", cex=.7, rot=90),col.regions=newcols(100), drape=TRUE,colorkey=TRUE, lwd=0.15, zoom=1, scales = list(z = list(distance = 0.5), x = list(distance = 0.5), y = list(distance = 0.5))); top.right <- wireframe(post2, xlab=list("p(adj-N)", cex=.7, rot=30),ylab=list("p(num-N)"...