# Posterior predictive checks for dimension and with applied filters on values

**URL:** <https://discourse.pymc.io/t/posterior-predictive-checks-for-dimension-and-with-applied-filters-on-values/14779>\
**Category:** v5\
**Tags:** modeling\
**Created:** [July 15, 2024, 11:06pm UTC](https://discourse.pymc.io/t/posterior-predictive-checks-for-dimension-and-with-applied-filters-on-values/14779 "2024-07-15T23:06:25Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![angrydozer](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/angrydozer/32/8790_2.png) [@angrydozer](https://discourse.pymc.io/u/angrydozer)\
**Post date:** [July 15, 2024, 11:06pm UTC](https://discourse.pymc.io/t/posterior-predictive-checks-for-dimension-and-with-applied-filters-on-values/14779/1 "2024-07-15T23:06:25Z")

</div>

People, hey!

I wrote model to describe difference in ARPU (average revenue per user) of two countries.

I used data stored in pandas dataframe called `data` where every row = 1 user and columns:

- **player\_id** – unique user id
- **country\_code** – US or JP
- **revenue\_7** – cumulative revenue to 7th day of user’s life (~95% of users are not payers, for them value = 0)

Model:

```auto
country = np.array(['US', 'JP'])
country_idx = pd.Categorical(data['country_code'], categories=country).codes
coords = {'country': country, 'country_flat': country[idx]}

with pm.Model(coords=coords) as model:
    
    psi = pm.Beta('psi', alpha=1, beta=1, dims='country')
    mu = pm.HalfNormal('mu', sigma=10, dims='country')
    sigma = pm.HalfNormal('sigma', sigma=15, dims='country')
    y = pm.HurdleGamma('y', mu=mu[country_idx], sigma=sigma[country_idx], psi=psi[country_idx], observed=data['revenue_7'])

    revenue = pm.Deterministic('revenue', psi * mu, dims='country')

    diff = pm.Deterministic('diff', revenue[0] - revenue[1])
    
    idata = pm.sample()
    idata.extend(pm.sample_posterior_predictive(idata))

```

Can you help me please with following questions 🙂:

1. How to plot posterior predictive checks for dimensions separately (2 countries) using `az.plot_ppc`?
2. How to ignore in vizualization zero values? it’s needed because psi is less then 5% and validation of revenue spread is impossible, there is only spike in x:0 definable and don’t see anything else. I wanna look at KDE for only payers, revenue tail (hope you understand). Or can you suggest better model setup that make inference data better structured for post-analysis 🙂