mnpProb {bayesm} | R Documentation |
mnpProb
computes MNP probabilities for a given X matrix corresponding to one
observation. This function can be used with output from rmnpGibbs
to simulate
the posterior distribution of market shares or fitted probabilties.
mnpProb(beta, Sigma, X, r)
beta |
MNP coefficients |
Sigma |
Covariance matrix of latents |
X |
X array for one observation – use createX to make |
r |
number of draws used in GHK (def: 100) |
see rmnpGibbs
for definition of the model and the interpretation of
the beta, Sigma parameters. Uses the GHK method to compute choice probabilities.
To simulate a distribution of probabilities, loop over the beta, Sigma draws from
rmnpGibbs
output.
p x 1 vector of choice probabilites
Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.
For further discussion, see Bayesian Statistics and Marketing
by Rossi,Allenby and McCulloch, Chapters 2 and 4.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
## ## example of computing MNP probabilites ## here I'm thinking of Xa as having the prices of each of the 3 alternatives Xa=matrix(c(1,.5,1.5),nrow=1) X=createX(p=3,na=1,nd=NULL,Xa=Xa,Xd=NULL,DIFF=TRUE) beta=c(1,-1,-2) ## beta contains two intercepts and the price coefficient Sigma=matrix(c(1,.5,.5,1),ncol=2) mnpProb(beta,Sigma,X)