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## 13.9 Ordered logistic generalized linear model (ordinal regression)

### 13.9.1 Probability mass function

If $$N,M,K \in \mathbb{N}$$ with $$N, M > 0$$, $$K > 2$$, $$c \in \mathbb{R}^{K-1}$$ such that $$c_k < c_{k+1}$$ for $$k \in \{1,\ldots,K-2\}$$, and $$x\in \mathbb{R}^{N\cdot M}, \beta\in \mathbb{R}^M$$, then for $$y \in \{1,\ldots,K\}^N$$, $\text{OrderedLogisticGLM}(y~|~x,\beta,c) = \\[4pt] \prod_{1\leq i \leq N}\text{OrderedLogistic}(y_i~|~x_i\cdot \beta,c) = \\[17pt] \prod_{1\leq i \leq N}\left\{ \begin{array}{ll} 1 - \text{logit}^{-1}(x_i\cdot \beta - c_1) & \text{if } y = 1, \\[4pt] \text{logit}^{-1}(x_i\cdot \beta - c_{y-1}) - \text{logit}^{-1}(x_i\cdot \beta - c_{y}) & \text{if } 1 < y < K, \text{and} \\[4pt] \text{logit}^{-1}(x_i\cdot \beta - c_{K-1}) - 0 & \text{if } y = K. \end{array} \right.$ The $$k=K$$ case is written with the redundant subtraction of zero to illustrate the parallelism of the cases; the $$y=1$$ and $$y=K$$ edge cases can be subsumed into the general definition by setting $$c_0 = -\infty$$ and $$c_K = +\infty$$ with $$\text{logit}^{-1}(-\infty) = 0$$ and $$\text{logit}^{-1}(\infty) = 1$$.

### 13.9.2 Sampling statement

y ~ ordered_logistic_glm(x, beta, c)

Increment target log probability density with ordered_logistic_lupmf(y | x, beta, c).

### 13.9.3 Stan functions

real ordered_logistic_glm_lpmf(int y | row_vector x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c. The cutpoints c must be ordered.

real ordered_logistic_glm_lupmf(int y | row_vector x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c dropping constant additive terms. The cutpoints c must be ordered.

real ordered_logistic_glm_lpmf(int y | matrix x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c. The cutpoints c must be ordered.

real ordered_logistic_glm_lupmf(int y | matrix x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c dropping constant additive terms. The cutpoints c must be ordered.

real ordered_logistic_glm_lpmf(int[] y | row_vector x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c. The cutpoints c must be ordered.

real ordered_logistic_glm_lupmf(int[] y | row_vector x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c dropping constant additive terms. The cutpoints c must be ordered.

real ordered_logistic_glm_lpmf(int[] y | matrix x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c. The cutpoints c must be ordered.

real ordered_logistic_glm_lupmf(int[] y | matrix x, vector beta, vector c)
The log ordered logistic probability mass of y, given linear predictors x * beta, and cutpoints c dropping constant additive terms. The cutpoints c must be ordered.