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 

r 
number of draws used in GHK (def: 100) 
See rmnpGibbs
for definition of the model and the interpretation of the beta and Sigma parameters. Uses the GHK method to compute choice probabilities. To simulate a distribution of probabilities, loop over the beta and Sigma draws from rmnpGibbs
output.
p x 1
vector of choice probabilites
Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.
For further discussion, see Chapters 2 and 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.
## example of computing MNP probabilites
## here Xa has 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, 0.5, 0.5, 1), ncol=2)
mnpProb(beta, Sigma, X)