Hi, I’m trying to fit a particular parameterization of a multi-logit model. In particular, I have some survey data and am trying to recreate an analysis similar to what was done here. I can get the model to run (and fit perfectly well) if I use the reference manual’s ordered logistic function (page 138) or ordered probit (page 139), but as best I can tell neither of those give me the mu parameter that I really want.

So I’ve tried to recreate the model using normal_cdf, which parameterizes in terms of mu and sigma, but I start running into all sorts of output errors - depending on what I do exactly, I get rejected initial values, invalid ordered vector values, and/or not a valid simplex errors. I’ve put in a simple version of the code below; can someone give me a hand? If you want to run the model, the simple data set I’m starting with is one survey question with six responses: n_cat = 5, n_resp = 6, y = 3,3,3,3,2,5 .

Thanks!

```
data {
int<lower=2> n_cat;
int<lower=1> n_resp;
int<lower=1,upper=n_cat> y[n_resp];
}
parameters {
real beta;
real<lower=0> sigma;
ordered[n_cat-1] thresh;
}
model {
vector[n_cat] theta;
vector[2] junk;
junk[1]=0.0;
beta ~ normal((n_cat+0.5)/2,n_cat);
sigma ~ cauchy(0,20);
for (k in 2:(n_cat-2))
thresh[k] ~ normal(k+0.5,2);
for (n in 1:n_resp) {
theta[1] = 1-normal_cdf(thresh[1],beta,sigma);
for (k in 2:(n_cat-1)) {
junk[2] = normal_cdf(thresh[k-1],beta,sigma) - normal_cdf(thresh[k],beta,sigma);
theta[k] = max(junk);
}
theta[n_cat] = normal_cdf(thresh[n_cat-1],beta,sigma);
y[n] ~ categorical(theta);
}
}
```

[edit: escaped code and auto-indented]