Hi all,

I’m trying to run an ordinal logistic regression, but am running issues when trying to set test values. The model I’m using is below,

```
with pm.Model() as model:
# weights
w_mu = pm.Laplace("w_mu", mu=0, b=1, shape=n_items)
w_sigma = pm.HalfCauchy("w_sigma", beta=5, shape=n_items)
w_raw = pm.Normal("w_raw", mu=0, sd=1, shape=(n_groups, n_items))
w = pm.Deterministic("w", w_mu + w_raw * w_sigma)
# thresholds
mu_thresholds = pm.Normal("mu_thresholds", mu=0, sd=5, shape=4)
sigma_thresholds = pm.HalfCauchy("sigma_thresholds", beta=5, shape=4)
thresholds = pm.Normal(
"thresholds",
mu=mu_thresholds,
sd=sigma_thresholds,
shape=4,
testval=np.array([-2, -1, 1, 2]),
transform=pm.distributions.transforms.ordered
)
wx = tt.sum(w[group_codes] * X_, axis=1)
y_ = pm.OrderedLogistic(
"y",
cutpoints=thresholds,
eta=wx,
observed=y
)
```

I keep seeing an error related to bad initial energy, so after looking at this previous post, I checked the logp values, none of which are NaN or inf, but the test values being used for the `thresholds`

variable aren’t what I specified.

```
{'mu_thresholds': array([0., 0., 0., 0.]),
'sigma_thresholds_log__': array([1.60943791, 1.60943791, 1.60943791, 1.60943791]),
'thresholds_ordered__': array([-2. , 0. , 0.69314718, 0. ]),
'w_mu': array([0., 0., 0., 0., 0., 0.]),
'w_raw': array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
...,
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]]),
'w_sigma_log__': array([1.60943791, 1.60943791, 1.60943791, 1.60943791, 1.60943791,
1.60943791])}
```

The test values aren’t ordered, which makes me think (according to the post I linked to) that this could be the issue. How can I specify the test values properly for the model?

Thanks!