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How can we perform Latent class Analysis for choice based conjoint data, to both segment respondents and determine co-efficients for attributes at the same point of time? I know there is a package poLCA to do this, but haven't seen anyone using that. Any sugestion on package, function or sample code?

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flexmix would do the job but (so far I remember) only if you model binary (Yes/No) or pairwise (A vs B) choices (Last time I checked the authors were working on an extension to multinomial (MNL) choices)

However, latent class logit (LCL) models are relatively easy to code as they consist in a discrete mixture of standard MNL models (so if you know how to code an MNL model you should be able to write your own LCL code).

Here is an example for a LCL with 2 classes:

X -> Matrix of independent variables (e.g., attributes' levels)
Y -> Column vector of observed choices (0/1)
N -> Column vector of respondents ID (e.g., 1 1 1 1 2 2 2 2 3 3 3 3 ...)
G -> Column vector of observations ID (e.g., 1 1 2 2 3 3 4 4 5 5 6 6 ...)

In this the code, the model specification is quite simple:
- Only 2 latent classes.
- Same set of predictors for the 2 classes (Possible to add some constraints).
- Constant only for class membership (Possible to add some covariates (age, gender, etc)).

loglik.LCL = function(beta, X, Y, N, G){
### Class 1
num1 = exp(as.matrix(X) %*% as.vector(beta[1:ncol(X)]))
den1 = tapply(num1, G, sum)
prb1 = num1[Y==1] / den1
sprb1 = tapply(prb1, N, prod)
### Class 2
num2 = exp(as.matrix(X) %*% as.vector(beta[1+ncol(X):2*ncol(X)]))
den2 = tapply(num2, G, sum)
prb2 = num2[Y==1] / den2
sprb2 = tapply(prb2, N, prod)
### Membership
cla1 = exp(0)
cla2 = exp(beta[1+2*ncol(X)])
CLA = cla1 + cla2
### Log-likelihood
llik = -sum(log(cla1/CLA * sprb1 + cla2/CLA * sprb2))
return(llik)}

Remark: Possible to write more efficient version of this code if you have a complete dataset by replacing tapply() by matrix operations (reshape, colSums, etc).

You can compare your results with the "lclogit" Stata command.

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