search for: credibility

Dear I am trying to find a 90% credible interval. I am using the following code. fit<-bugs( model.file=BUGScode, data=data, inits = list(geninits1,geninits2), parameters.to.save=keepers, n.chains=nchains, n.iter=runs, n.burnin=burn, n.thin=nthin, DIC= TRUE, bugs.directory="C:/Program Files/WINBUGS.14", \ ) But this is only giving 95%

I am trying to calculate Bayesian Credible Intervals for a proportion (disease prevalence values to be more specific) and am having trouble using R to do this. I am working with ncredint() function but have not had success with it. Please help! Example: Positive samples = 3 Total sampled = 10 Prevalence = 0.3 pvec <- seq(1,10,by=1) npost = dbinom(pvec,10,prob=0.3, log=FALSE) ncredint(pvec,

Hi all, I have the data from the posterior distribution for some parameter. I want to find the 95% credible interval. I think "t.test(data)" is only for the confidence interval. I did not fine function for the Bayesian credible interval. Could some one suggest me? Thanks [[alternative HTML version deleted]]

Hi all, I'm new to the list and am hoping to get some advice. I have a set of multivariate data and would like to find the densest part of the data cloud containing 95% of the data, like a 95% HPD credible volume. Is there any R code available to compute that? Thank you very much! Your help and patience are much appreciated. G.S. [[alternative HTML version deleted]]

Dear all, I would like to plot credible interval for a function estimate in R. I would like to plot the credible intervals as shaded region using polygon function. Does anyone ever used that? I tried several times but I could not obtain the right figure. xis=sort(xi,decreasing=TRUE) plot(xi,fm) polygon(c(xi,xis),c(f05m,f95m)) The above piece of code produces something else. Many thanks in

Do you know this? Dario ------- Messaggio inoltrato ------- Da: stan <stanl-fedorauser at vfemail.net> Reply-to: Community support for Fedora users <users at lists.fedoraproject.org> A: users at lists.fedoraproject.org Oggetto: CIA Outlaw Country attack against CentOS / Rhel (and Fedora?) Is this credible? Data: Thu, 29 Jun 2017 15:51:43 -0700 Wikileaks released a document about an

> Do you know this? "For operational use, shell access is assumed, and root privileges are required." It's not much of a secret that you can mess with a system if you have root access... Yves Bellefeuille <yan at storm.ca>

...ndberg and Sparre Andersen models. Most calculations are done using the cdf of phase-type distributions as per Asmussen and Rolski (1991). o Calculations of the aggregate claim distribution using the recursive method much faster now that recursions are done in C. NEW FEATURES -- CREDIBILITY THEORY o Modular rewrite of cm(): the function now calls internal functions to carry calculations for each supported credibility model. This is more efficient. o Basic support for the regression model of Hachemeister in function cm(). o For the hierarchical credibility mo...

...ndberg and Sparre Andersen models. Most calculations are done using the cdf of phase-type distributions as per Asmussen and Rolski (1991). o Calculations of the aggregate claim distribution using the recursive method much faster now that recursions are done in C. NEW FEATURES -- CREDIBILITY THEORY o Modular rewrite of cm(): the function now calls internal functions to carry calculations for each supported credibility model. This is more efficient. o Basic support for the regression model of Hachemeister in function cm(). o For the hierarchical credibility mo...

I have a function "eval.delta" which does what I want but isn't very elegant. I have consulted the R documents, MASS, and S Programming. Is there a practical way to optimize the code? Thank you very much. Peter B. Function: eval.delta <- function(delta){ cat("VALUES\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j] <= 2){

...et it speak for itself: === actuar: an R package for Actuarial Science === Version 0.9-2 ============= Major official update. This version is not backward compatible with the 0.1-x series. Feature of the package can be split in the following categories: loss distributions modeling, risk theory, credibility theory. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions {d,p,q,r}foo to compute the density function, cumulative distribution function, quantile function of, and to generate variates from, all probability distributions of Appendix A of Klugman et al. (2004), "Loss Models, Second Edition...

...et it speak for itself: === actuar: an R package for Actuarial Science === Version 0.9-2 ============= Major official update. This version is not backward compatible with the 0.1-x series. Feature of the package can be split in the following categories: loss distributions modeling, risk theory, credibility theory. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions {d,p,q,r}foo to compute the density function, cumulative distribution function, quantile function of, and to generate variates from, all probability distributions of Appendix A of Klugman et al. (2004), "Loss Models, Second Edition...

This is a question of clarification. IN 2009 Higgins, Thompson and Spiegelhalter (J R Statist Soc A 172:137-159) gave WinBUGs code to get credible intervals from random effects meta analysis for the prediction interval of a new study. It appears that the predict.rma function creates approximate credible intervals (pending a function revision by the author) for that purpose. Is my assumption

Try this: split(delta,cut(delta, breaks=c(0,2,4,7,10,max(delta)),include.lowest=T)) $"[0,2]" [1] 0.0 1.4 2.0 2.0 $"(2,4]" [1] 2.3 2.3 $"(4,7]" [1] 4.5 6.4 $"(7,10]" [1] 8.9 7.4 $"(10,12.4]" [1] 12.4 11.5 "Peter B. Mandeville" <mandevip at uaslp.mx>@stat.math.ethz.ch on 06/24/2002 13:11:41 Sent by: owner-r-help at

...E: [Asterisk-Users] New to asterisk? RUN... don't walk. > On Sat, 2004-01-03 at 14:12, Me wrote: > > Mr. West, > > > > Sorry to burst your bubble, but that is not me. My > > name is Barbara Simpson. Either you are lying or > > someone is trying to remove any credibility from my > > original post. I now stand by my original post with > > more conviction than ever. > > You had little to no credibility when you show up acting like a troll > from what most people would consider a throw away account. > > > There were a lot of insightful r...

...R Package for Actuarial Science === We are pleased to announce the immediate availability of version 1.0-0 of actuar. This release follows publication of our papers in JSS (*) and R News (**). From the NEWS file: Version 1.0-0 ============= NEW FEATURES o Improved support for regression credibility models. There is now an option to make the computations with the intercept at the barycenter of time. This assures that the credibility adjusted regression line (or plane, or ...) lies between the individual and collective ones. In addition, contracts without data are now s...

...R Package for Actuarial Science === We are pleased to announce the immediate availability of version 1.0-0 of actuar. This release follows publication of our papers in JSS (*) and R News (**). From the NEWS file: Version 1.0-0 ============= NEW FEATURES o Improved support for regression credibility models. There is now an option to make the computations with the intercept at the barycenter of time. This assures that the credibility adjusted regression line (or plane, or ...) lies between the individual and collective ones. In addition, contracts without data are now s...

Imagine the following scenario Last Saturday, 3:00 AM a big phishing attack hits our e-mail inboxes. Spamassassin does not mark them as spam, and our 50.000+ users have in their mdbox a very credible phishing attack. What doveadm-fu could I use to delete (or move to spam) that e-mail from each user INBOX (let?s imagine the Subject or a Header is known)? I repeat: already delivered e-mail, how

How do you know when a Linux system has been compromised?? Every day I watch our systems with all the typical tools, ps, top, who, I watch firewall / IPS logs, I have logwatch setup and mailing daily summaries to me and I dive deeper into logs if something looks suspicious. What am I missing or not looking at that you security gurus are looking at? I subscribe to the centos and SANS

This is a follow-up mail of "the effect of blocking on the size of confidence intervals - analysis using aov". In both mails I pursue the idea of using blocking factors in order to reduce the width of confidence intervals. My dataset comprises, a quantitative response variable, namely: "response", and three categorical eplanatory variables, namely: "method",