Following up on this earlier discussion (https://groups.google.com/forum/#!topic/stan-users/kpV7R6f9x1M), providing the syntax for cross-classified model for an intercept, if one wanted to extend the model to include slopes, would it be a simple matter of extending the use of the index, as follows:

I couldn’t quite follow this question given the link. I don’t know what a “cross-classified model for an intercept” is.

I also don’t follow the notation where you have y[n] distributed as inv_logit, because inv_logit is just a function. Was that supposed to be y[n] ~ bernoulli_logit(...) instead?

There’s nothing wrong with the model you write here. Assuming my guess above is right, you want to code this using vectorization as

y ~ bernoulli_logit(alpha1[state]
+ alpha2[sex]
+ alpha
+ beta * income
+ beta1[state] .* income
+ beta2[sex]);

Note the .* used for the elementwise multiplication.