Description

fits a hierarchical normal model of the form E[y_{ij}] = μ_{j} + β_{1}x_{i1}+…+β_{p}x_{ip}

Usage

 1 2 3 hierMeanReg(design, priorTau, priorPsi, priorVar, priorBeta = NULL, steps = 1000, startValue = NULL, randomSeed = NULL)

Arguments

 design a list with elements y = response vector, group = grouping vector, x = matrix of covariates or NULL if there are no covariates priorTau a list with elements tau0 and v0 priorPsi a list with elements psi0 and eta0 priorVar a list with elements s0 and kappa0 priorBeta a list with elements b0 and bMat or NULL if x is NULL steps the number of Gibbs sampling steps to take startValue a list with possible elements tau, psi, mu, sigmasq and beta. tau, psi and sigmasq must all be scalars. mu and beta must be vectors with as many elements as there are groups and covariates respectively randomSeed a random seed for the random number generator

Value

A data frame with variables:

 tau Samples from the posterior distribution of tau psi Samples from the posterior distribution of psi mu Samples from the posterior distribution of mu beta Samples from the posterior distribution of beta if there are any covariates sigmaSq Samples from the posterior distribution of σ^2 sigma Samples from the posterior distribution of sigma

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 priorTau <- list(tau0 = 0, v0 = 1000) priorPsi <- list(psi0 = 500, eta0 = 1) priorVar <- list(s0 = 500, kappa0 = 1) priorBeta <- list(b0 = c(0,0), bMat = matrix(c(1000,100,100,1000), nc = 2)) data(hiermeanRegTest.df) data.df <- hiermeanRegTest.df design <- list(y = data.df\$y, group = data.df\$group, x = as.matrix(data.df[,3:4])) r<-hierMeanReg(design, priorTau, priorPsi, priorVar, priorBeta) oldPar <- par(mfrow = c(3,3)) plot(density(r\$tau)) plot(density(r\$psi)) plot(density(r\$mu.1)) plot(density(r\$mu.2)) plot(density(r\$mu.3)) plot(density(r\$beta.1)) plot(density(r\$beta.2)) plot(density(r\$sigmaSq)) par(oldPar) ## example with no covariates priorTau <- list(tau0 = 0, v0 = 1000) priorPsi <- list(psi0 = 500, eta0 = 1) priorVar <- list(s0 = 500, kappa0 = 1) data(hiermeanRegTest.df) data.df <- hiermeanRegTest.df design <- list(y = data.df\$y, group = data.df\$group, x = NULL) r<-hierMeanReg(design, priorTau, priorPsi, priorVar) oldPar <- par(mfrow = c(3,2)) plot(density(r\$tau)) plot(density(r\$psi)) plot(density(r\$mu.1)) plot(density(r\$mu.2)) plot(density(r\$mu.3)) plot(density(r\$sigmaSq)) par(oldPar)

Bolstad2 documentation built on May 29, 2017, 3:35 p.m.