rhierLinearModel {bayesm}R Documentation

Gibbs Sampler for Hierarchical Linear Model with Normal Heterogeneity


rhierLinearModel implements a Gibbs Sampler for hierarchical linear models with a normal prior.


rhierLinearModel(Data, Prior, Mcmc)



list(regdata, Z)


list(Deltabar, A, nu.e, ssq, nu, V)


list(R, keep, nprint)


Model and Priors

nreg regression equations with nvar X variables in each equation
y_i = X_i\beta_i + e_i with e_i \sim N(0, \tau_i)

\tau_i \sim nu.e*ssq_i/\chi^2_{nu.e} where \tau_i is the variance of e_i
\beta_i \sim N(Z\Delta[i,], V_{\beta})
Note: Z\Delta is the matrix Z * \Delta and [i,] refers to ith row of this product

vec(\Delta) given V_{\beta} \sim N(vec(Deltabar), V_{\beta}(x) A^{-1})
V_{\beta} \sim IW(nu,V)
Delta, Deltabar are nz x nvar; A is nz x nz; V_{\beta} is nvar x nvar.

Note: if you don't have any Z variables, omit Z in the Data argument and a vector of ones will be inserted; the matrix \Delta will be 1 x nvar and should be interpreted as the mean of all unit \betas.

Argument Details

Data = list(regdata, Z) [Z optional]

regdata: list of lists with X and y matrices for each of nreg=length(regdata) regressions
regdata[[i]]$X: n_i x nvar design matrix for equation i
regdata[[i]]$y: n_i x 1 vector of observations for equation i
Z: nreg x nz matrix of unit characteristics (def: vector of ones)

Prior = list(Deltabar, A, nu.e, ssq, nu, V) [optional]

Deltabar: nz x nvar matrix of prior means (def: 0)
A: nz x nz matrix for prior precision (def: 0.01I)
nu.e: d.f. parameter for regression error variance prior (def: 3)
ssq: scale parameter for regression error var prior (def: var(y_i))
nu: d.f. parameter for Vbeta prior (def: nvar+3)
V: Scale location matrix for Vbeta prior (def: nu*I)

Mcmc = list(R, keep, nprint) [only R required]

R: number of MCMC draws
keep: MCMC thinning parm -- keep every keepth draw (def: 1)
nprint: print the estimated time remaining for every nprint'th draw (def: 100, set to 0 for no print)


A list containing:


nreg x nvar x R/keep array of individual regression coef draws


R/keep x nreg matrix of error variance draws


R/keep x nz*nvar matrix of Deltadraws


R/keep x nvar*nvar matrix of Vbeta draws


Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.


For further discussion, see Chapter 3, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

See Also



if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10}

nreg = 100
nobs = 100
nvar = 3
Vbeta = matrix(c(1, 0.5, 0, 0.5, 2, 0.7, 0, 0.7, 1), ncol=3)

Z = cbind(c(rep(1,nreg)), 3*runif(nreg))
Z[,2] = Z[,2] - mean(Z[,2])
nz = ncol(Z)
Delta = matrix(c(1,-1,2,0,1,0), ncol=2)
Delta = t(Delta) # first row of Delta is means of betas
Beta = matrix(rnorm(nreg*nvar),nrow=nreg)%*%chol(Vbeta) + Z%*%Delta

tau = 0.1
iota = c(rep(1,nobs))
regdata = NULL
for (reg in 1:nreg) { 
  X = cbind(iota, matrix(runif(nobs*(nvar-1)),ncol=(nvar-1)))
	y = X%*%Beta[reg,] + sqrt(tau)*rnorm(nobs)
	regdata[[reg]] = list(y=y, X=X) 

Data1 = list(regdata=regdata, Z=Z)
Mcmc1 = list(R=R, keep=1)

out = rhierLinearModel(Data=Data1, Mcmc=Mcmc1)

cat("Summary of Delta draws", fill=TRUE)
summary(out$Deltadraw, tvalues=as.vector(Delta))

cat("Summary of Vbeta draws", fill=TRUE)
summary(out$Vbetadraw, tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)]))

## plotting examples

[Package bayesm version 3.1-6 Index]