I just noticed that the `ordered_logistic`

function compiles (rstan 2.19.2) when the LHS is an integer array but then does something weird in the sampler. I’m a bit confused as to what is going on here so any info would be appreciated, I wonder if stan should fail during compilation in this case? It is very easy for a user to make a mistake here, I think.

I’ve attached simplified examples below, with a small number of iterations since the vectorized case is so slow.

Model with vectorized `ordered_logit`

:

```
library(rstan)
library(rethinking)
data(Trolley)
model_code1 <- "
data {
int<lower = 1> n;
int<lower = 2> num_responses;
int<lower = 1, upper = num_responses> response[n];
}
parameters {
ordered[num_responses - 1] cut_points;
}
model {
vector[n] linear_predictor = rep_vector(0.0, n);
response ~ ordered_logistic(linear_predictor, cut_points);
}
"
mod1 <- stan(model_code = model_code1,
iter = 200,
warmup = 100,
data = list(n = nrow(Trolley),
num_responses = length(unique(Trolley$response)),
response = Trolley$response))
> print(mod1)
Inference for Stan model: bb983badb3bce0d3a8dd5481b8a453c5.
4 chains, each with iter=200; warmup=100; thin=1;
post-warmup draws per chain=100, total post-warmup draws=400.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
cut_points[1] -1.11 0.65 0.93 -2.09 -1.58 -1.40 -0.90 0.42 2 114.56
cut_points[2] -0.73 0.51 0.72 -1.27 -1.25 -1.07 -0.56 0.49 2 69.00
cut_points[3] -0.19 0.47 0.67 -1.06 -0.68 -0.28 0.41 0.70 2 26.16
cut_points[4] 0.29 0.56 0.80 -0.99 0.00 0.40 0.87 1.20 2 32.86
cut_points[5] 0.96 0.62 0.89 -0.48 0.59 1.27 1.55 1.99 2 27.77
cut_points[6] 1.61 0.56 0.80 0.26 1.40 1.89 2.20 2.34 2 25.48
lp__ -22604.89 1796.57 2553.30 -25697.12 -24600.76 -23104.69 -20417.95 -19087.92 2 34.34
Samples were drawn using NUTS(diag_e) at Thu Sep 5 16:14:40 2019.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
```

Model with for loop:

```
model_code2 <- "
data {
int<lower = 1> n;
int<lower = 2> num_responses;
int<lower = 1, upper = num_responses> response[n];
}
parameters {
ordered[num_responses - 1] cut_points;
}
model {
vector[n] linear_predictor = rep_vector(n, 0.0);
for (i in 1:n) {
response[i] ~ ordered_logistic(linear_predictor[i], cut_points);
}
}
"
mod2 <- stan(model_code = model_code2,
iter = 200,
warmup = 100,
data = list(n = nrow(Trolley),
num_responses = length(unique(Trolley$response)),
response = Trolley$response))
> print(mod2)
Inference for Stan model: a276a6f50d33f30b0a0984159954e96a.
4 chains, each with iter=200; warmup=100; thin=1;
post-warmup draws per chain=100, total post-warmup draws=400.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
cut_points[1] -1.92 0.00 0.03 -1.97 -1.94 -1.92 -1.90 -1.85 226 1.00
cut_points[2] -1.26 0.00 0.03 -1.32 -1.28 -1.27 -1.24 -1.21 470 1.00
cut_points[3] -0.72 0.00 0.02 -0.76 -0.73 -0.72 -0.70 -0.67 767 0.99
cut_points[4] 0.25 0.00 0.02 0.21 0.23 0.25 0.26 0.29 454 1.00
cut_points[5] 0.89 0.00 0.02 0.85 0.88 0.89 0.91 0.94 388 1.01
cut_points[6] 1.77 0.00 0.03 1.71 1.75 1.77 1.79 1.83 591 1.00
lp__ -18926.01 0.13 1.79 -18930.46 -18926.93 -18925.65 -18924.69 -18923.60 183 1.01
Samples were drawn using NUTS(diag_e) at Thu Sep 5 16:15:46 2019.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
```