hierMeanReg {Bolstad2} | R Documentation |
Hierarchical Normal Means Regression Model
Description
fits a hierarchical normal model of the form E[y_{ij}]
= μ_{j} + β_{1}x_{i1}+...+β_{p}x_{ip}
Usage
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
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)
[Package
Bolstad2 version 1.0-26
Index]