idefix (version 0.2.4)

SeqKL: Sequential Kullback-Leibler based algorithm for the MNL model.

Description

Selects the choice set that maximizes the Kullback-Leibler divergence between prior parameter values and the expected posterior, assuming an MNL model.

Usage

SeqKL(cand.set, n.alts, alt.cte, par.draws, weights, reduce = TRUE)

Arguments

cand.set

A numeric matrix in which each row is a possible profile. The Profiles function can be used to generate this.

n.alts

Numeric value indicating the number of alternatives per choice set.

alt.cte

A binary vector indicating for each alternative if an alternative specific constant is desired.

par.draws

A matrix in which each row is a sample from the multivariate parameter distribution. See also ImpsampMNL.

weights

A vector containing the weights of the draws. Default is NULL, See also ImpsampMNL.

reduce

Logical value indicating whether the candidate set should be reduced or not.

Value

set

Numeric matrix containing the choice set that maximizes the expected KL divergence.

kl

Numeric value which is the Kullback leibler divergence.

Details

The algorithm selects the choice set that maximizes the Kullback-Leibler divergence between prior and expected posterior. Otherwisely framed the algorithm selects the choice set that maximizes the expected information gain.

References

crabbemnldes

Examples

# NOT RUN {
# KL efficient choice set, given parameter draws. 
# Candidate profiles 
cs <- Profiles(lvls = c(3, 3), coding = c("E", "E"))
m <- c(0.3, 0.2, -0.3, -0.2) # Prior mean (4 parameters).
pc <- diag(length(m)) # Prior variance
set.seed(123)
ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws.
ac <- c(0, 0) # No alternative specific constants. 
# Efficient choice set to add. 
SeqKL(cand.set = cs, n.alts = 2, alt.cte = ac, par.draws = ps, weights = NULL)

# KL efficient choice set, given parameter draws. 
# Candidate profiles 
cs <- Profiles(lvls = c(3, 3), coding = c("C", "E"), c.lvls = list(c(5,3,1)))
m <- c(0.7, 0.3, -0.3, -0.2) # Prior mean (4 parameters).
pc <- diag(length(m)) # Prior variance
set.seed(123)
ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws.
ac <- c(1, 0) # Alternative specific constant. 
# Efficient choice set to add. 
SeqKL(cand.set = cs, n.alts = 2, alt.cte = ac, par.draws = ps, weights = NULL)
# }