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discrete.py docs updated #7442

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33 changes: 20 additions & 13 deletions pymc/distributions/discrete.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1189,12 +1189,15 @@ class OrderedLogistic:
R"""Ordered Logistic distribution.

Useful for regression on ordinal data values whose values range
from 1 to K as a function of some predictor, :math:`\eta`. The
from 0 to K-1 as a function of some predictor, :math:`\eta`. The
cutpoints, :math:`c`, separate which ranges of :math:`\eta` are
mapped to which of the K observed dependent variables. The number
of cutpoints is K - 1. It is recommended that the cutpoints are
constrained to be ordered.

Note: The observed values should use 0-based indexing (0, 1, ..., K-1),
consistent with Python's indexing convention.

.. math::

f(k \mid \eta, c) = \left\{
Expand All @@ -1203,9 +1206,9 @@ class OrderedLogistic:
\,, \text{if } k = 0 \\
\text{logit}^{-1}(\eta - c_{k - 1}) -
\text{logit}^{-1}(\eta - c_{k})
\,, \text{if } 0 < k < K \\
\,, \text{if } 0 < k < K-1 \\
\text{logit}^{-1}(\eta - c_{K - 1})
\,, \text{if } k = K \\
\,, \text{if } k = K-1 \\
\end{array}
\right.

Expand Down Expand Up @@ -1233,9 +1236,9 @@ class OrderedLogistic:
cluster3 = np.random.randn(n3_c) + 2

x = np.concatenate((cluster1, cluster2, cluster3))
y = np.concatenate((1*np.ones(n1_c),
2*np.ones(n2_c),
3*np.ones(n3_c))) - 1
y = np.concatenate((np.zeros(n1_c),
np.ones(n2_c),
2*np.ones(n3_c))).astype(int)

# Ordered logistic regression
with pm.Model() as model:
Expand Down Expand Up @@ -1288,12 +1291,15 @@ class OrderedProbit:
Ordered Probit distributions.

Useful for regression on ordinal data values whose values range
from 1 to K as a function of some predictor, :math:`\eta`. The
from 0 to K-1 as a function of some predictor, :math:`\eta`. The
cutpoints, :math:`c`, separate which ranges of :math:`\eta` are
mapped to which of the K observed dependent variables. The number
of cutpoints is K - 1. It is recommended that the cutpoints are
constrained to be ordered.

Note: The observed values should use 0-based indexing (0, 1, ..., K-1),
consistent with Python's indexing convention.

In order to stabilize the computation, log-likelihood is computed
in log space using the scaled error function `erfcx`.

Expand All @@ -1305,9 +1311,9 @@ class OrderedProbit:
\,, \text{if } k = 0 \\
\text{normal_cdf}(0, \sigma, \eta - c_{k - 1}) -
\text{normal_cdf}(0, \sigma, \eta - c_{k})
\,, \text{if } 0 < k < K \\
\,, \text{if } 0 < k < K-1 \\
\text{normal_cdf}(0, \sigma, \eta - c_{K - 1})
\,, \text{if } k = K \\
\,, \text{if } k = K-1 \\
\end{array}
\right.

Expand Down Expand Up @@ -1337,9 +1343,10 @@ class OrderedProbit:
cluster3 = np.random.randn(n3_c) + 2

x = np.concatenate((cluster1, cluster2, cluster3))
y = np.concatenate((1*np.ones(n1_c),
2*np.ones(n2_c),
3*np.ones(n3_c))) - 1
# Note: y uses 0-based indexing (0, 1, 2)
y = np.concatenate((np.zeros(n1_c),
np.ones(n2_c),
2*np.ones(n3_c))).astype(int)

# Ordered probit regression
with pm.Model() as model:
Expand Down Expand Up @@ -1386,4 +1393,4 @@ def compute_p(cls, eta, cutpoints, sigma):
axis=-1,
)
p = pt.exp(log_p)
return p
return p
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