Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 
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, [email protected].
For further discussion, see Chapters 2 and 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.
http://www.perossi.org/home/bsm1
1 2 3 4 5 6 7 8 9  ## 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)

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