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 |
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.
http://www.perossi.org/home/bsm-1
## 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)