search for: betas

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/${arch}/ISO-IMAGES/7.1/ ftp://ftp.freebsd.org/pub/FreeBSD/releases/${arch}/ISO-IMAGES/6.4/

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

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]

Hello, I have problem running WinBUGS from R. The following example works in WinBUGS but it does not work in R through package R2WinBUGS. Does anyone know what the problem is? x <- c(0.2, 1.1, 1, 2.2, 2.5, 2.9, 2.9, 3.6, 3.8, 0.6, 1, 2, 2.4, 2.6, 2.8, 3.2, 3.9, 3.5) y <- c(0.5, 1.3, 0.1, 0.7, -0.4, 0.5, -0.9, -0.3, -0.3, 0.6, 0.4, 0.9, -0.1, -0.4, -0.5, -0.2, 0.3, -1.5) eco <- c(1, 3,

....people, alpha=al, beta=bet) ### Since I neither have censoring nor truncation in this simple case, ### the log-likelihood should be simply the sum of the log of the ### the densities (following the parametrization of Klein/Moeschberger ### Survival Analysis, p. 38) loggomp <- function(alphas, betas, timep) { return(sum(log(alphas) + betas*timep + (alphas/betas * (1-exp(betas*timep))))) } ### Now I thought I could obtain a matrix of the log-likelihood surface ### by specifying possible values for alpha and beta with the given data. ### I was able to produce this matrix with two for-loops. B...

....people, alpha=al, beta=bet) ### Since I neither have censoring nor truncation in this simple case, ### the log-likelihood should be simply the sum of the log of the ### the densities (following the parametrization of Klein/Moeschberger ### Survival Analysis, p. 38) loggomp <- function(alphas, betas, timep) { return(sum(log(alphas) + betas*timep + (alphas/betas * (1-exp(betas*timep))))) } ### Now I thought I could obtain a matrix of the log-likelihood surface ### by specifying possible values for alpha and beta with the given data. ### I was able to produce this matrix with two for-loops. B...

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

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

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

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

Hi, I am new to R. My problem is with the ordered logistic model. Here is my question: Generate an order discrete variable using the variable wrwage1 = wages in first full calendar quarter after benefit application in the following way: * wage*1*Ordered *= 1 *if*0 *· wrwage*1 *< *1000 2 *if*1000 *· wrwage*1 *< *2000 3 *if*2000 *· wrwage*1 *< *3000 4 *if*3000 *· wrwage*1 *<

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 using in a production

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 using in a production

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

-- 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)

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

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 binomial functions (e.g. alpha + beta <1 in the Beta negative binomial). I've

Dear R users, I'm trying to fit a gamma-frailty model on a simulated dataset, with 6 covariates, and I'm running into some results I do not understand. I constructed 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).

...*%Beta.hat.j + (diag(P) - Lambda.j)%*%Wj[[j]]%*%GAMMA }  # Singular case else {  Beta.tilde.j <- V.tilde.j%*%((1/s2)*t(Xj[[j]])%*%Yj[[j]] + solve(TAU)%*%Wj[[j]]%*%GAMMA) } BETA.Js[[j]] <- t(rmnorm(1, mean=as.vector(Beta.tilde.j), V.tilde.j)) }       # THIS DOESN'T WORK USING MAPPLY update.betas <- function(s2,Xj,Yj,TAU,Wj,GAMMA) {  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 &lt...

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