rscaleUsage(bayesm) | R Documentation |
rscaleUsage
implements an MCMC algorithm for multivariate ordinal data with scale usage heterogeniety.
rscaleUsage(Data,Prior, Mcmc)
Data |
list(k,x) |
Prior |
list(nu,V,mubar,Am,gsigma,gl11,gl22,gl12,Lambdanu,LambdaV,ge) optional |
Mcmc |
list(R,keep,ndghk,printevery,e,y,mu,Sigma,sigma,tau,Lambda) optional |
Model: n=nrow(X) individuals respond to m=ncol(X) questions. all questions are on a scale 1, ..., k.
for respondent i and question j,
x_ij = d, if c_d-1 <= y_i,j <= c_d. d=1,...,k. c_d = a + bd +ek^2.
y_i = mu + tau_i*iota + sigma_i*z_i. z_i~N(0,Sigma).
(tau_i,ln(sigma_i)) ~ N(phi,Lamda). phi=(0,lambda_22).
Priors:
mu~N(mubar, Am^-1).
Sigma ~ IW(nu,V).
Lambda ~ IW(Lambdanu,LambdaV).
e ~ unif on a grid.
a list containing:
drawSigma |
R/keep x m*m array of Sigma draws |
mudraw |
R/keep x m array of mu draws |
taudraw |
R/keep x n array of tau draws |
sigmadraw |
R/keep x n array of sigma draws |
Lambdadraw |
R/keep x 4 array of Lamda draws |
edraw |
R/keep x 1 array of e draws |
It is highly recommended that the user choose the default settings. This means not specifying the argument
Prior
and setting R
in Mcmc and Data
only. If you wish to change prior settings and/or
the grids used, please read the case study in Allenby et al carefully.
Rob McCulloch and Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.
For further discussion, see Bayesian Statistics and Marketing
by Allenby, McCulloch, and Rossi, Case Study on Scale Usage Heterogeneity.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html