13.10 Ordered probit distribution
13.10.1 Probability mass function
If K∈N with K>2, c∈RK−1 such that ck<ck+1 for k∈{1,…,K−2}, and η∈R, then for k∈{1,…,K}, OrderedProbit(k | η,c)={1−Φ(η−c1)if k=1,Φ(η−ck−1)−Φ(η−ck)if 1<k<K,andΦ(η−cK−1)−0if k=K. The k=K case is written with the redundant subtraction of zero to illustrate the parallelism of the cases; the k=1 and k=K edge cases can be subsumed into the general definition by setting c0=−∞ and cK=+∞ with Φ(−∞)=0 and Φ(∞)=1.
13.10.2 Sampling statement
k ~ ordered_probit(eta, c)
Increment target log probability density with ordered_probit_lupmf(k | eta, c).
13.10.3 Stan functions
real ordered_probit_lpmf(ints k | vector eta, vectors c)
The log ordered probit probability mass of k given linear predictors
eta, and cutpoints c.
real ordered_probit_lupmf(ints k | vector eta, vectors c)
The log ordered probit probability mass of k given linear predictors
eta, and cutpoints c dropping constant additive terms.
int ordered_probit_rng(real eta, vector c)
Generate an ordered probit variate with linear predictor eta and
cutpoints c; may only be used in transformed data and generated quantities blocks