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The FreeBSD 7.1-BETA and 6.4-BETA builds are now available on the FreeBSD FTP mirror sites. This is the first step in the release process for FreeBSD-7.1 and FreeBSD-6.4. This set of builds do not include pre-built packages. The ISOs are available from: ftp://ftp.freebsd.org/pub/FreeBSD/releases/${arch}/ISO-IMAGE...

Hi, Having a weird problem with the integrate function. I have a function which calculates a loss density: I'd like to integrate it to get the distribution. The loss density function is: lossdensity<-function(p,Beta,R=0.4){ # the second derivative of the PDF # p is the default probability of the pool at which we are evaluating the lossdensity # Beta is the correlation with the market factor as a function of K # R is the recovery rate K=p C=qnorm(p) # default threshold for the pool A=1/Beta(K)*(C-sqrt(1-Beta(K...

I have a VAR model with five macro-economic variables, y[1], y[2], y[3], y[4], y[5]. They are related to each other in following manner. y[1,t] = alpha[1,0] + beta[1,1, 1]*y[1,t-1]+............+beta[1,1, 12]*y[1,t-12] + beta[1,2, 1]*y[2,t-1]+............+beta[1,2, 12]*y[2,t-12] + e[1,t] y[2,t] = alpha[2,0] + beta[2,2, 1]*y[2,t-1]+............+beta[2,2, 12]*y[2,t-12] + e[2,t] y[3,t] = alpha[3,0] + beta[3,1, 1]*y[1,t-1]+............+beta[3,1, 12]*y[1,t-12] +...

...0.1, -0.4, -0.5, -0.2, 0.3, -1.5) eco <- c(1, 3, 1, 2, 1, 2, 1, 1, 2, 1, 1, 3, 2, 3, 2, 2, 2, 1) N <- length(y) J <- length(unique(eco)) f.data <- list("N", "y", "x", "eco", "J") f.inits <- function(){ list(sigma.y=1, beta.0=1, beta.x=1, beta.eco=c(NA, 1, 1))} f.parameters <- c("beta.0", "beta.x", "sigma.y", "beta.eco") f.ml <- bugs (data=f.data, inits=f.inits, parameters.to.save=f.parameters, model.file="oecd1.bug&quo...

...b <- 1:5 outer(a,b) ## A bit more complicated is this: f <- function(a,b,d) { return(a*b+(sum(d))) } additional <- runif(100) outer(X=a, Y=b, FUN=f, d=additional) ## So far so good. But now my real example. I would like to plot the ## log-likelihood surface for two parameters alpha and beta of ## a Gompertz distribution with given data ### I have a function to generate random-numbers from a Gompertz-Distribution ### (using the 'inversion method') random.gomp <- function(n, alpha, beta) { return( (log(1-(beta/alpha*log(1-runif(n)))))/beta) } ## Now I generate...

...b <- 1:5 outer(a,b) ## A bit more complicated is this: f <- function(a,b,d) { return(a*b+(sum(d))) } additional <- runif(100) outer(X=a, Y=b, FUN=f, d=additional) ## So far so good. But now my real example. I would like to plot the ## log-likelihood surface for two parameters alpha and beta of ## a Gompertz distribution with given data ### I have a function to generate random-numbers from a Gompertz-Distribution ### (using the 'inversion method') random.gomp <- function(n, alpha, beta) { return( (log(1-(beta/alpha*log(1-runif(n)))))/beta) } ## Now I generate...

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 within my code for( i in 1: m ) { beta.post = rbeta( 10000, draw1[i] + prior....

Could I have some suggestions as to how (various ways) I can display my confidence interval results? rm(list = ls()) set.seed(1) func <- function(d,t,beta,lambda,alpha,p.gamma,delta,B){ d <- c(5,1,5,14,3,19,1,1,4,22) t <- c(94.32,15.72,62.88,125.76,5.24,31.44,1.048,1.048,2.096,10.48) post <- matrix(0, nrow = 11, ncol = B) theta <- c(lambda,beta) beta.hat <- 2.471546 for(j in 1:B){ for(i in 1:(B-1)){ c.lambda <- rgamma(10,...

Hi, I run LLVM test suite using the following command and get report.nightly.html successfully. % make report.html TEST=nightly But GCC/LLC, GCC/LLC-BETA and LLC/LLC-BETA columns look strange. Here is for example snippet of "clamscan" test result: [[ Program MultiSource/Applications/ClamAV/clamscan GCC 0.2360 LLC 0.2360 LLC-BETA * GCC/LLC 17.92 GCC/LLC-...

I have a data frame with data similar to this: NameA GrpA NameB GrpB Dist A Alpha B Alpha 0.2 A Alpha C Beta 0.2 A Alpha D Beta 0.4 B Alpha C Beta 0.2 B Alpha D Beta 0.1 C Beta D Beta 0.3 Dist is a distance measure between two entities. The table displays all to all distances, but the distance between two entities only appears on...

...ge) > Ik <- model.matrix(~fw-1) > I1 <- Ik[,1] > I2 <- Ik[,2] > I3 <- Ik[,3] > I4 <- Ik[,4] > I5 <- Ik[,5] > yelmon <- function(theta,x){ + mu1 <- theta[1] + mu2 <- theta[2] + mu3 <- theta[3] + mu4 <- theta[4] + mu5 <- theta[5] + beta <- theta[6] + logL <- sum((Ik*log((exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))-(exp(mu1-(x*beta))/(1+exp(mu1-(x*beta))))))+(Ik*log((exp(mu3-(x*beta))/(1+exp(mu3-(x*beta))))-(exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))))+(Ik*log((exp(mu4-(x*beta))/(1+exp(mu4-(x*beta))))-(exp(mu3-(x*beta))/(1+exp(mu...

The CentOS development team is pleased to announce the release of CentOS 5 (Beta) for i386 and x86_64. It is available via beta.CentOS.org mirrors and bittorrent. This release corresponds to the upstream vendor EL5 beta2 release. NOTE: This software is BETA and should be treated as such. It is for testing purposes only. Please ensure it meets your needs completely before u...

The CentOS development team is pleased to announce the release of CentOS 5 (Beta) for i386 and x86_64. It is available via beta.CentOS.org mirrors and bittorrent. This release corresponds to the upstream vendor EL5 beta2 release. NOTE: This software is BETA and should be treated as such. It is for testing purposes only. Please ensure it meets your needs completely before u...

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 om...

-- uklinux.net - The ISP of choice for the discerning Linux user. ---------- Forwarded message ---------- Date: Mon, 27 Sep 2004 12:44:37 -0400 From: taroon-beta-list at redhat.com To: taroon-beta-list at redhat.com Subject: Announcing Red Hat Enterprise Linux 4 (Nahant) Beta 1 Public Availability Red Hat is pleased to announce the availability of the Red Hat Enterprise Linux (version 4) Beta 1 milestone. This is a public beta. Please feel free to f...

On Mon, Nov 12, 2012 at 11:15 PM, Simon Atanasyan <satanasyan at mips.com> wrote: > I run LLVM test suite using the following command and get > report.nightly.html successfully. > > % make report.html TEST=nightly > > But GCC/LLC, GCC/LLC-BETA and LLC/LLC-BETA columns look strange. Here > is for example snippet of "clamscan" test result: > [[ > Program MultiSource/Applications/ClamAV/clamscan > GCC 0.2360 > LLC 0.2360 > LLC-BETA * > G...

Hi, I need Beta binomial and Beta negative binomial functions but in R there is only SuppDists package which provide this distributions using a limited parameter space of the generalized hypergeometric distribution (dghyper & Co.) which provide a limited parameter space for Beta binomial and Beta negative bino...

...an example from my simulation code, where I fit a coxph model without frailty (M1) and with frailty (M2) on a number of data samples with a varying degree of heterogeneity (I'm running R 2.3.1, running takes ~1 min). library(survival); set.seed(10000) lambda <- 0.01 # Exp. hazard rate # Beta coefficients for Age,TC,HDLC,SBP,Diab,Smok beta <- c(0.0483,0.0064,-0.0270,0.0037,0.4284,0.5234) n <- 1000; Ngrp <- 2; # Nr patients, Nr frailty groups # Thetas for gamma-frailty thetaset <- c(1,2,10,100); Ntheta <- length(thetaset); # Define the simulated population a...

...Any guidance would be greatly appreciated. Xj, Yj, and Wj are also lists, and s2, TAU, and GAMMA are scalars.   Thank You.   # THIS WORKS USING THE LOOP for (j in 1:J) { V.tilde.j <- solve((1/s2)*t(Xj[[j]])%*%Xj[[j]] + solve(TAU)) # Not singular case: if(round(det(t(Xj[[j]])%*%Xj[[j]]),8)!=0) {  Beta.hat.j <- solve(t(Xj[[j]])%*%Xj[[j]])%*%t(Xj[[j]])%*%Yj[[j]]  V.j <- s2*solve(t(Xj[[j]])%*%Xj[[j]])  Lambda.j <- solve(solve(V.j) + solve(TAU))%*%solve(V.j)  Beta.tilde.j <- Lambda.j%*%Beta.hat.j + (diag(P) - Lambda.j)%*%Wj[[j]]%*%GAMMA }  # Singular case else {  Beta.tilde.j <- V.til...

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni measurements for object i. Par...