search for: credibly

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>

UseRs, Version 0.9-4 of actuar should be making its way to CRAN mirrors. The main highlights of this new version are speed enhancements for a few functions, support for phase-type distributions and functions for ruin theory. The relevant section of the NEWS file follows Version 0.9-4 ============= Maintenance and new features release. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions

UseRs, Version 0.9-4 of actuar should be making its way to CRAN mirrors. The main highlights of this new version are speed enhancements for a few functions, support for phase-type distributions and functions for ruin theory. The relevant section of the NEWS file follows Version 0.9-4 ============= Maintenance and new features release. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions

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){

UseRs, actuar is a package for Actuarial Science. A rather preliminary version (0.1-3) of the package has been available on CRAN since February 2006. We now announce the immediate availability of version 0.9-2 sporting a large number of new features. Non actuaries behold! There can be some features of interest for you, especially those related to new probability distribution and to the

UseRs, actuar is a package for Actuarial Science. A rather preliminary version (0.1-3) of the package has been available on CRAN since February 2006. We now announce the immediate availability of version 0.9-2 sporting a large number of new features. Non actuaries behold! There can be some features of interest for you, especially those related to new probability distribution and to the

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

Hi All, Can we stop this thread pl. This lady has no intentions to learn asterisk. She is just a troll and wasting our time. With her corporate attitude, what she expects is support that available with paid commercial products. Her company has enough money to buy commercial products, let she go there. Hey lady, whoever u are, dont waste our time. this is not for u. Lets move on to something

=== actuar: An 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

=== actuar: An 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

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",