rhierBinLogit: MCMC Algorithm for Hierarchical Binary Logit

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

MCMC Algorithm for Hierarchical Binary Logit

Description

This function has been deprecated. Please use rhierMnlRwMixture instead.

rhierBinLogit implements an MCMC algorithm for hierarchical binary logits with a normal heterogeneity distribution. This is a hybrid sampler with a RW Metropolis step for unit-level logit parameters.

rhierBinLogit is designed for use on choice-based conjoint data with partial profiles. The Design matrix is based on differences of characteristics between two alternatives. See Appendix A of Bayesian Statistics and Marketing for details.

Usage

rhierBinLogit(Data, Prior, Mcmc)

Arguments

Data	list(lgtdata, Z)
Prior	list(Deltabar, ADelta, nu, V)
Mcmc	list(R, keep, sbeta)

Details

Model and Priors

y_{hi} = 1 with Pr = exp(x_{hi}'\beta_h) / (1+exp(x_{hi}'\beta_h) and \beta_h is nvar x 1
h = 1, \ldots, length(lgtdata) units (or "respondents" for survey data)

\beta_h = ZDelta[h,] + u_h
Note: here ZDelta refers to Z%*%Delta with ZDelta[h,] the hth row of this product
Delta is an nz x nvar array

u_h \sim N(0, V_{beta}).

delta = vec(Delta) \sim N(vec(Deltabar), V_{beta}(x) ADelta^{-1})
V_{beta} \sim IW(nu, V)

Argument Details

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

lgtdata:	list of lists with each cross-section unit MNL data
lgtdata[[h]]$y:	n_h x 1 vector of binary outcomes (0,1)
lgtdata[[h]]$X:	n_h x nvar design matrix for h'th unit
Z:	nreg x nz mat of unit chars (def: vector of ones)

Prior = list(Deltabar, ADelta, nu, V) [optional]

Deltabar:	nz x nvar matrix of prior means (def: 0)
ADelta:	prior precision matrix (def: 0.01I)
nu:	d.f. parameter for IW prior on normal component Sigma (def: nvar+3)
V:	pds location parm for IW prior on normal component Sigma (def: nuI)

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

R:	number of MCMC draws
keep:	MCMC thinning parm -- keep every keepth draw (def: 1)
sbeta:	scaling parm for RW Metropolis (def: 0.2)

Value

A list containing:

Deltadraw	R/keep x nz*nvar matrix of draws of Delta
betadraw	nlgt x nvar x R/keep array of draws of betas
Vbetadraw	R/keep x nvar*nvar matrix of draws of Vbeta
llike	R/keep x 1 vector of log-like values
reject	R/keep x 1 vector of reject rates over nlgt units

Note

Some experimentation with the Metropolis scaling paramter (sbeta) may be required.

Author(s)

Peter Rossi, Anderson School, UCLA, [e-mail].

References

For further discussion, see Chapter 5, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

See Also

rhierMnlRwMixture

Examples

if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=10000} else {R=10}
set.seed(66)

nvar = 5              ## number of coefficients
nlgt = 1000           ## number of cross-sectional units
nobs = 10             ## number of observations per unit
nz = 2                ## number of regressors in mixing distribution

Z = matrix(c(rep(1,nlgt),runif(nlgt,min=-1,max=1)), nrow=nlgt, ncol=nz)
Delta = matrix(c(-2, -1, 0, 1, 2, -1, 1, -0.5, 0.5, 0), nrow=nz, ncol=nvar)
iota = matrix(1, nrow=nvar, ncol=1)
Vbeta = diag(nvar) + 0.5*iota%*%t(iota)

lgtdata=NULL
for (i in 1:nlgt) { 
  beta = t(Delta)%*%Z[i,] + as.vector(t(chol(Vbeta))%*%rnorm(nvar))
  X = matrix(runif(nobs*nvar), nrow=nobs, ncol=nvar)
  prob = exp(X%*%beta) / (1+exp(X%*%beta)) 
  unif = runif(nobs, 0, 1)
  y = ifelse(unif<prob, 1, 0)
  lgtdata[[i]] = list(y=y, X=X, beta=beta)
}

Data1 = list(lgtdata=lgtdata, Z=Z)
Mcmc1 = list(R=R)

out = rhierBinLogit(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)]))

if(0){
## plotting examples
plot(out$Deltadraw,tvalues=as.vector(Delta))
plot(out$betadraw)
plot(out$Vbetadraw,tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)]))
}

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.